GenColor: Generative and Expressive Color Enhancement with Pixel-Perfect Texture Preservation
Authors: Yi Dong
Yuxi Wang
Xianhui Lin
Wenqi Ouyang
Zhiqi Shen
Peiran Ren
Ruoxi Fan
Rynson W. H. Lau
Venue: NeurIPS 2025 spotlight ()
Keywords: color enhancement
Primary area: applications

Abstract: Color enhancement is a crucial yet challenging task in digital photography. It demands methods that are (i) expressive enough for fine-grained adjustments, (ii) adaptable to diverse inputs, and (iii) able to preserve texture. Existing approaches typically fall short in at least one of these aspects, yielding unsatisfactory results. We propose GenColor, a novel diffusion-based framework for sophisticated, texture-preserving color enhancement. GenColor reframes the task as conditional image generation. Leveraging ControlNet and a tailored training scheme, it learns advanced color transformations that adapt to diverse lighting and content. We train GenColor on ARTISAN, our newly collected large-scale dataset of 1.2M high-quality photographs specifically curated for enhancement tasks. To overcome texture preservation limitations inherent in diffusion models, we introduce a color-transfer network with a novel degradation scheme that simulates texture–color relationships. This network achieves pixel-perfect texture preservation while enabling fine-grained color matching with the diffusion-generated reference images. Extensive experiments show that GenColor produces visually compelling results comparable to those of expert colorists and surpasses state-of-the-art methods in both subjective and objective evaluations. We have released the code and dataset.

Gen Color: Generative and Expressive Color Enhancement with Pixel-Perfect Texture Preservation
Yi Dong", Yuxi Wang", Xianhui Lin?, Wenqi Ouyang', Zhiqi Shen'
Peiran Ren Ruoxi Fan', Rynson W. H. Lau
Nanyang Technological University, 2 AAlibaha Group, City University Hong Kong
Project Page: https://yidong pro/prooeeeeeeeppoppo http://eccttgggncooo.o.//
Unsupervised Learning
Supervision from Adobe5K Dataset
Generative Methods
Human Expert
Difference Map vs Inpot
Texture Fidelity
X
Exposure
14
D&R
30
3D-LUT
37
RSFNet
28
Midjoumey GenColor (Ours) Expert C
Figure 1: GenColor achieves expressive color enhancement with superior texture preservation. While unsupervised methods (Exposure [14], Distort-and- -nd-Recovvr (30) lack expressiveness, supervised approaches (3D-LUT (37,, RSFNet (28] are constrained by global adjustments their training data, Generative methods (e.g., Midjourney), on the other hand, create dramatic transformations but alter textures, compromising authenticity. The difference maps (2nd row) show GenColor making selective region-specific adjustments similar to Midjourney, but with better results than Expert C (the best retoucher in Adobe5K [2]), which is limited to global filter adjustments.
Abstract
Color enhancement is crucial yet challenging task in digital photography. It demands methods that are (i) expressive enough for fine-grained adjustments, (ii) adaptable diverse inputs, and (iii) able preserve texture. Existing approaches typically fall short in at least one of these aspects, yielding unsatisfactory re- sults. We propose GenColor, novel diffusion-based framework for sophisticated, texture-preserving color enhancement. GenColor reframes the task as conditional image generation. Leveraging ControlNet and tailored training scheme, learns advanced color transformations that adapt to diverse lighting and content. We train GenColor ARTISAN, newly collected large-scale dataset of 1.2M high-quality photographs specifically curated for enhancement tasks. To overcome texture preservation limitations inherent in diffusion models, introduce color transfer network with novel degradation scheme that simulates texture-col relationships. This network achieves pixel-perfect texture preservation while en-
"Corresponding authors, 'Equal contribution
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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abling fine-grained color matching with the diffusion-genes generated reference images. Extensive experiments show that GenColor produces visually compelling results comparable to those of expert colorists and surpasses ofate-oftthe-art methods in both subjective and objective evaluations. We have released the code and dataset.
Introduction
Color enhancement turns ordinary photographs into compelling visual narratives through the fine- grained adjustments professional colorists apply evoke emotion and aesthetic quality. Despite considerable progress, automated algorithms still struggle to match human expertise. As shown in Figure []] three fundamental challenges impede progress in this domain: (1) achieving fine- ane-grained expressiveness comparable to human colorists; (2) adapting to diverse input conditions involving varied lighting and content; and (3) preserving the textural fidelity of the input images while making color adjustments.
Existing approaches face significant limitations in addressing these challenges. Unsupervised methods like Exposure [14] and Wistort-and-Recoee [30] preserve texture but lack expressiveness, particularly in difficult lighting conditions. Supervised methods like DeepLPF [25] and RSFNet (28) rely on
datasets, such as Adobe FiveK (2] and PPR 1OK [22] that contain only global adjustments, fail to capture the complex local modifications characteristic of expert retouching. These approaches produce less vibrant results, as visible in Figure [] Meanwhile, generative methods like Midjourney create dramatic transformations but significantly alter textures, distorting details and introducing unnatural patterns that compromise the authenticity of the original scene. These collective limitations underscore the need for a color enhancement approach that combines expressive adjustments with texture preservation capabilities across diverse real-world conditions.
In this paper, we present GenColor, a novel framework reinterpreting color enhancement as texture- preserving conditional image generation task. Our key insight is to leverage the expressive generation capabilities of diffusion models while decoupling texture preservation network, ensuring fine-grained adjustments without compromising texture fidelity.
specialized transfer
to be the largest dataset specifically designed for image enhancement with 1.2 million high-quality photographs- and develop iiftussionbbaeed color generation approach with three distinctive technical advances. A central observation in our work is that diffusion models, traditionally known for generating new content, can be repurposed for color enhancement through targeted conditioning and careful training regimes. First, we reframe color enhancement as conditional generation by leveraging ControlNet (38) conditioned directly on input images, unlocking superior color expressiveness compared to traditional methods that rely on carefully designed filters with limited creative range. Second, we adapt distort-anddrecovee principles to diffusion models through a self superviedd training strategy with a wider range of color adjustments, enhancing compatibility with challenging lighting conditions and diverse content. Third, we discover that strategic blending of weights from different training stages yields good balance between creative expressiveness and artifact control. These three technical advances enable highly expressive color generation that adapts to diverse input conditions, achieving sophisticated aesthetic enhancements that traditional methods cannot attain.
For expressive color transformations, we curate ARTISAN-bella
Although diffusion models excel at generating aesthetically pleasing colors, their intrinsic iterative denoising process inevitably alters textural elements-a fundamental limitation that can be alleviated but never fully eliminated. To address this limitation, we develop a texture-preeerving network that achieves three critical objectives simultaneously: (1) transferring semantically consistent color styles from the diffusion-generaut that typically appear as random noise or irregular color strokes in the diffusion -ennuiiinnnggeeteedd color reference image, and (3) performing implicit super-resolution to handle the resolution discrepancy between diffusion models (typically 512x512 pixels) and high-resolution input images. While this task resembles traditional color matching or transfer, a key technical observation guided our upproach-the diffusion model's 's output and the input image are near-duplicates at the texture level, with relevant pixels for color transfer spatially placed nearby neighborhoods. To leverage these characteristics, we design seff-supervised learning approach with a novel degradation scheme that simulates all three objectives: circular shift to model the nearby spatial misalignment, random stroke degradation to simulate or restore artifacts, and random resolution rescaling to simulate the
reference images to the original input, (2) restoring artifacts

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Challenges
Phase 1: Color Generation Module
Addeveser Challengar #1 A
Expresinccuss: Lack grainnd aljustments Compsttiility Proc adaptaaion to various inpuis excture Preservation Loss of structural integiity
Training
Culor Generation conditined on distocted version
Highly aestlotic photos with
ARTISAN-IM Dutnvet 1.2M High-quilty photos
disturted version
Baxed Contralleet
Inpui
Expresivenen: Diftusion
Compatibility: Trainin various
Centribatios
model leams comples color
distotions is adge to challennins ceses
Generation
Dedlurion-Resed Nerwork
Phase 2:
Texture Preservation Module T
Adfresses Challeages #3
ANAA
Color Referencc I,
Self Supervised Leamlnng Conditional Genention
Tranformeshssed Netwook
C Contribation
CNN + Swin Transformer
Residal multi-cale festure
Texture Preservation
AMA
Noor-Dipliaae Transfer
Restoration J. Super Resolation
Testure Presenation 2. Artiforr
Key Innovalion: Novel degnstation enahles piscl-pericct prosrvation
Textere Preservation: Usler tranttrr while mointanning strectur
Textare Recovered 1
Phase 3: Global Adjustment Module JF
Addrezus Diffasiio model's comtrast busor
3. Global Adjastment Lighöleiihht Fillers
Lighhwwight
Brighteer
Highlight
Saturation
Enhunced Ourgut I.
Munettet
[ Tiaiking Datasets
Key
Figure 2: GenColor framework overview: (left) the overall pipeline and (right) summary/highlights of each module. The approach addresses three key challenges through a three-phase process: (1) diffusion-based Color Generation module creates expressive color enhancements conditioned on the input image, trained on the ARTISAN- 1M dataset; (2) Texture Preservation module maintains texture integrity while transferring colors from the color reference I, to the input image I; (3) A Global Adjustment module applies five lightweight filters to enhance contrast and saturation.
super-resolution challenge. This design, based on accurate observations of underlying data patterns, achieves superior texture preservation and significantly better color matching performance than statc-of-the-art methods.
Extensive experiments demonstrate that GenColor outperforms existing methods in both subjective evaluations and objective metrics for color enhancement and cexture-preservee color transfer. As illustrated in Figure [ our approach outperforms existing methods while maintaining texture fidelity that generative methods sacrifice. This example image showcases GenColor's 's ability to create beautiful contrast between the golden building tones and clear blue sky, intelligently lightening shadowed areas while preserving architectural details. It demonstrates how our approach achieves more expressive results than Human Expert C-a limitation inherent to the supervised learning methods, which have relied on Adobe5K due to the absence of fine- -ga-geaine paired datasets, for color aahancement-pparticulaa in scenes with complex lighting and content that require different color treatments for specific elements. Our key contributions include:
1. A diffusion-based color enhancement framework capable of expert-level fine fine-grained ad- justments, supported by a training scheme that enables sophisticated color generation with selective manipulations across varying lighting and content conditions.
2. A texture-preserving color transfer network that maintains pixel-perfect textural fidelity while achieving precise color matching with the diffusion-generaated reference images, resulting in outputs that are both visually compelling and faithful to the original content.
3. ARTISAN, large-scale dataset of 1.2 million high- aesthetic-oo aesthetic-quality photos specifically curated for color enhancement, enabling robust training of ad advanced models.
2 Related Works
2.1 Image Color Enhancement Methods
Traditional color enhancement relies on global adjustments using predetined filters or lookup tables (LUTs), which lack expressiveness for fine-grained local adjustments. To address this limitation, color enhancement methods that operate at a fine-graine grained level have been proposed. Supervised learning approaches such as DeepLPF (25]. 3D-LUT (37]. and RSFNet (2] learn enhancement mappings from paired datasets such as Adobe FiveK (2] and PPR IOK [2] but are constrained by these datasets' focus on global adjustments rather than sophisticated local edits. Recent work like ICELUT (36) improves interpretability while still limited by training data expressiveness. Unsupervised methods, including Exposure [.. EnhanceGAN 4 and Distort-and-Recome [29], overcome paired data limitations but struggle to achieve fine-grained control comparable to professional retouching. Our
3

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Dataset size
RAH
100
11.9 %
1.2 M
1.3 %
(a) ARTISAN-100 K
(b) ARTISAN-1 M
(c) Strong hue change
Figure 3: Impact of dataset size color enhancement quality. (a) Model trained on ARTISAN- 100K produces artifacts. (b) Model trained on ARTISAN-IM generates high-quality results. (c) RAH measures the percentage of output images with significant areas where the hue has changed substantially compared to the input, indicating undesirable color shifts. ARTISAN-IM reduces this metric, enabling effective enhancement learning.
approach addresses these limitations through diffusion models' generative capabilities combined with texture preservation mechanisms.
2.2 Diffusion Models for Image Color Editing
Diffusion models 12 have revolutionized image synthesis, with latent diffusion models 31 improv- ing efficiency through compressed latent space operations. ControlNet (38 enables structure-gus -ggided generation through conditional signals, ControlColor [23] successfully applied this approach to image colorization and achieve strong performance in comparison to existing methods [17]. In related vein, DiffRetouch (8 employs Stable Diffusion for multi-style photo retouching. Nevertheless, its focus remains user-adjustable global attributes, whereas our work enables expert-level fine-grained color generation with adaptability across diverse lighting and content conditions.
2.3 Texture Preservation Image Enhancement
Diffusion- based methods struggle with texture preservation due to latent space compression (1/8 or 1/16 downsampling). While DiffRetouch g mitigates distortion using Affine Bilateral Grid for pixel-wise adjustments, two fundamental limitations remain: it operates in latent space where high-frequency details are already lost during downsampling, and its grid-based approach introduces interpolation errors in complex scenes like text. Several established approaches for texture preserva- tion also face challenges in our setting. Existing approaches include: (1) hint-based color propagation methods (ControlColor 23 UniColor (15) that preserve lightness but are unsuitable for enhance-
ments requiring luminance adjustments; (2) style transfer methods (CAP-VSTNet (33) that sacrifice texture fidelity; and (3) color matching/transte techniques (Color Matcher [10, NeuralPreset (18)) that achieve pixel- pixel-perfect texture preservation but aren't ' optimized for scenarios where relevant color pixels for transfer exist in nearby spatial neighborhoods between input and reference images. (4) color fusion methods 7 that only focus on the edge area of the editing region.
Our approach uses CNN-transformee hybrid operating directly in image space, leveraging the observation that diffusion outputs and inputs are texture-level near-duplicates with correspondences in nearby spatial neighborhoods. This insight enables self-supervise learning with novel degradation scheme, significantly outperforming existing methods in accuracy and texture preservation.
ARTISAN-IM Dataset
Deep learning requires large, high-quality datasets with diverse content and styles. Existing datasets fall short- Adobe5K [2] and PPRIOK (22] are too small, while
V2 [32] lacks real-world texture details the majority of its aesthetically high subset consists of non-photographic artworks. We introduce ARTISAN-IM, containing 1.2 million carefully curated photographs from online platforms (primarily Flickr), selected based on visual quality (Q-Align score [34]), content diversity, and color representation. includes 1.0M daytime and 240.9K nighttime images, with balanced distribution between images with people (617.0K and without (653.0K).
ARTISAN-IM enables our color generation module to learn complex enhancement patterns. shown in Figure models trained on the smaller ARTISAN-100K subset exhibit artifacts with 11.9% of images having undesirable strong hue shifts, while the full dataset reduces this to just 1.3%. When strong hue changes occur with the full dataset, changing the generator's seed typically resolves the

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High Aestbetic Images as Training Targel
2 Condition is Target with Full Range Random Color Adjustment
3 Though Each Image is Adjusted Globally, Training over Large Datasel Captures Fine-Grained Nuances
Stable Diffusion
Filters: Exposuse, rest, Hue, Seturet son, Namth
Strong Deviation
Controlinat
Extreme Lighting
RessuraliseCCooriittiiee
Condition (Global Adjusted Target)
Target
Slight Deviation
(High Acstictic Placs
Suboptmml Color
ARTISAN-IM)
Fine Color Edits
Figure 4: Overview of our color generation module. (1) High-quality aesthetic images from our ARTISAN-IM dataset are used as training targets. (2) wide range of random color adjustments (exposure, contrast, hue, saturation, warmth, etc.) are applied to the target images to create inputs with strong to slight deviations as conditioning. (3) The ControlNet architecture, based on Stable Diffusion, is then trained on this large dataset, learning to map the color distorted inputs back to the aesthetic targets, capturing fine-grained color enhancement nuances.
issue. ARTISAN-IM IM's unprecedented scale and quality provides a solid foundation for GenColor to learn expressive yet faithful color enhancement from real-world high-qualigg photography. Due to space limitations, the details of the datasets are discussed in the appendix.
4 Method
4.1 Method Overview
As shown in Figure 2 we present GenColor, novel framework that addresses the fundamental challenges of image color enhancement through a three-phase approach. Given an input image I, our goal is to produce an aesthetically enhanced output I, while preserving original texture details. First, our color generation module employs a diffusion model conditioned on the input image to create a color reference I, D(I). This module, trained on our ARTISAN- IM dataset, leverages ControlNet [38) to learn sophisticated color transformations while carefully balancing expressiveness and artifact control through strategic blending of weights from different training stages. To address the inherent texture limitations of diffusion models, our texture preservation module I = T(I, I,) transfers color characteristics from the reference while maintaining the structural details of the input image. Finally, to counteract diffusion models' tendency to produce images with reduced contrast and vibrancy 224, global adjustment module I, = F(I,) applies five essential filters (brightness, contrast, highlight, shadow, and saturation) to enhance contrast and vibrancy while preserving the sophisticated color transformations.
Our complete enhancement pipeline can be expressed as: I. = ((T This formulation effectively combines the expressive power of diffusion models with precise texture preservation, enabling high-quality color enhancement that rivals professional human retouching
4.2
Color Generation
4.2.1
ControlNet-base Color Generation
As shown in Figure 4 we formulate color enhancement as conditional generation using the ControlNet architecture (88 This approach shows superior color expressiveness compared to traditional methods that rely on carefully designed filters with limited creative range. ControlNet adds an auxiliary network branch that conditions on a control signal derived from a color-distorted version of the input image AA /)) ) where A is random color adjustment function. We train ControlNet on our high-quality ARTISAN-IM IM dataset, using the original images as targets and color-distorted versions as conditioning inputs. The distortions range from strong deviations (extreme lighting conditions, restoration/colorization scenarios) to slight deviations (suboptimal color, fine color edits), creating a comprehensive training regime. This approach enables our method to tackle challenging inputs with high contrast lighting, difficult shadows, and many other complex scenarios that traditional methods struggle with. Although each image is adjusted globally, training over our large dataset enables the model to capture fine-grained color enhancement nuances that apply different image regions contextually, In addition, we have found weight blending strategy that can improve the
5

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Degradation
Task
Degradation
Texture Preserving Color Transfer
Input (1)
Color Adj
Down-ample In
Stroke)
Artifact Restoration (Small Color
Druial
Strokes)
Swin- Transfammr Block
CNN
Super-Resoluse Resolution (Low-Res I,)
Pxel- Shuftle
x4
Network
Architecture
Figure 5: Texture preservation module and its training data generation process. The network aims transfer the color characteristics from the reference image I, while preserving its structural details.
for performance of the color generation module, please refer to the appendix $ for details. We utilize null text prompt by default, textual information provides negligible contribution to this network, as validated in Appendix B.3
4.3 Texture Preservation Module
4.3.1 Problem Formulation
The texture preservation network T aims to transfer the color characteristics from the reference image I, generated by the color generation module D while preserving the high-frequency structural details from the input image I. This can be formulated
conditional generation problem: p P r,) (II, where I, is the enhanced output image, I represents the upsampling operation to match the input resolution, and / denotes the learnable parameters of T.
4.3.2 Degradation Process for Training Data Generation
In the absence of ground truth pairs, design degradation model generate synthetic training data that mimics the characteristics of our unique problem, including color differences, texture and structure similarities, and artifacts. As shown in Figure 5 the degradation process consists of:
1. A random circular shift operation that maintains similar overall texture and structure between images while avoiding exact pixel-level correspondence.
2. Global color distortion applied randomly across the entire image simulate the diverse
range of color transformations that may occur during enhancement.
3. Localized color deviation strokes added in randomly selected sparse regions to replicate
potential artifacts that could emerge during the color generation process.
4. Resolution adjustment through image resizing operations to account for potential differences in resolution between the input and reference images.
4.3.3 Network Architecture and Training
We propose Transformer- based network (Figure right panel) with residually connected feature extraction blocks integrating CNN and Swin-Trrasffrmer modules for effective local-global feature capture. The network is trained supervieevi using the color distorted image as ground truth and degraded image paired with the original input as training data, optimized with a combination of Huber, perceptual, and adversarial losses. During inference, the degraded image is replaced by output from the color generation network D. This unified framework effectively addresses color transfer, artifact reduction, and resolution enhancement while preserving the input image's 's structural details. Please refer to the appendix for more details.
Experiments
We present only key results here; appendix for comprehensive analyses.

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5.1
Datasets
We evaluate on three datasets: Advee-FiveK (5,000 raw images with expert retouches), PPRIOK [22 (11,161 portrait photos with edits and masks), and FreeRaw (142 high-quality raw photos). Results for Adobe-FiveK and PPRIOK are the main paper, with FreeRaw in the appendix.
5.2 Metrics
We evaluate using three categories of metrics: (1) Color Enhancement metrics including Q- Align [34] (emulates human judgment), NoR-HDR 9 (assesses dynamic range), and LIQE [39
(blind image quality evaluation); (2) Texture Preservation metries including TD (quantifies texture distortion), DISTS [S] (assesses structural/textuuaal similarity), and GMSD K5 (compares
gradient magnitude maps): and (3) Color Similarity metrics including W1 (measures color distribu-
tion similarity), Semantic W1 (captures fine-grainee color characteristics), and MS-SWD [ (latest metric aligned with human judgment).
5.3 Baseline Methods
We compare GenColor with state-of-tee of-tth-artttttt color enhancement methods: Exposure [4], Distort-and- Recover (30) 3D-LUT 37,, Deepl.PF [25], RSFNet [28), and ICELUT (36). For texture-preserving
color transfer, we compare with ColorMatcher 10 NLUT 37 Deep Preset [3], and hint color
propagation method from Colorization via Imagination originally proposed in UniColor [15] We
exclude style transfer methods like CAP-VSTNet [33] (poor texture preservation) and NeuralPre- (18] (unavailable code). Appendix performs slightly better than ColorMatcher but significantly worse than GenColor.
$ shows Tinge, an app. which uses NeuralPreset'' algorithm,
5.4 Image Enhancement Quantitative Results
Table presents a quantitative comparison of color enhancement quality on the Adobe-FiveK and PPR10K datasets. Our GenColor method achieves the best results on most metrics across all datasets, outperforming state-of-th of-the-artr the-art methods and approaching or surpassing the performance of a human expert retoucher on the FiveK and PPR 10K datasets. GenColor obtains the highest scores on Q-Align and NoR-VDPNet for all datasets, and the best LIQE on FiveK and FreeRaw, with its LIQE on PPRIOK being second only to the human expert. Please note that all supervised methods are trained on their respective dataset, while we provide additional results for unsupervised methods trained on our ARTISAN-IM dataset, which indicated by (ARTISAN).
Table 1: Evaluation of GenColor's color-enhancement quality against state-ot of-the-ar art methods on various datasets using five perceptual quality metrics: Q-Align (34), LAION B2). LIQE [39], NoR- VDP 99. and C- VAR (t). Higher values indicate better performance, with best results in bold.
Adobe5K
PPR10K
Method
Aesthetic
QualityDyn. Rxpgesxpress.
Aesthetic
Quality Dya. RangeExpress.
LAIIN LIQE NoR- VDP1C- Ceeer---A---iliiiiieeee AliliignLAAAON LIQE NoR- VDP1CC-AAT
LAION
3D-LUT 37
4.22
5.73
3.66
67.08
11.48
4.22
6.03
2.83
69.83
8.09
RSFNet
28
4.23
5.74
3.65
67.85
10.09
4.29
6.05
2.97
69.69
6.68
DeepLPF
25
4.16
5.79
3.53
69.08
10.97
3.92
6.08
2.55
71.07
11.33
ICELUT
4.17
5.70
3.55
66.40
10.44
3.97
2.49
68.18
7.21
D&R
30
2.39
5.33
1.35
64.43
10.29
2.44
5.30
1.21
67.78
5.68
D&R (ARTISAN)
2.31
5.21
1.31
63.92
9.92
2.31
5.23
1.18
67.21
5.41
Exposure
14
4.00
5.72
3.29
66.02
13.96
3.90
5.96
2.37
70.73
12.14
Exposure (ARTISAN)
3.93
5.66
3.25
65.69
13.48
3.86
5.88
2.29
70.32
12.00
GenColor
4.29
5.83
3.76
69.16
16.96
4.38
6.26
3.21
71.11
13.57

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Table Comparison with human expert and LLM preference evaluation.
Method
Q-Align fLAION 1LIQE NNoR- VDP TC-VAR t
Input Expert CGenColor
Human Expert C
4.24
5.72
3.65
67.86
10.91
Preference
0
38
62
GenColor (Ours) 4.29
5,83
3.76
69.16
16.96
(b) LLM (ChatGPT al) preference on Adobe5K [2] (first 100 images). GenColor outperforms Expert C, the widely-used benchmark expert.
(a) Head- to te-head comparison between GenColor and professional human retoucher (Expert C in Adobe5K (2). Results show Gen- Color matches exceeds human expert.
5.5 Analysis of Human Expert and LLM Preference Evaluation
Table 2 compares GenColor against human expert performance and LLM preferences. GenColor consistently outperforms Human Expert C across all five quantitative metrics. Notably, GenColor achieves substantial 55.5% improvement in C-VAR, indicating significantly better expressiveness.
The LLM preference evaluation provides additional validation. When ChatGPT ol evaluated 100 Adobe5K images, GenColor was preferred in 62% of cases compared to Expert C's 38%. This 24 percentage point preference gap demonstrates that GenColor exceeds professional human retouching quality perceived by advanced AI systems.
Table Texture preservation evaluation on Adobe5k. GenColor achieves excellent balance between preserving textures and maintaining color accuracy.
Texture Preservation
Color Similarity
Method
TD LDISTS GGMDD J.Sem Wi W LMS-SWD |
NLUT 37
0.95
0.15
0.12
13.01
12.76
0.87
Color Matcher 10
0.42
0.17
0.14
31.45
31.34
2.03
UniColor 15
0.95
0.15
0.14
38.40
38.20
2.18
Deep Preset (w/o PPL.)
13
1.17
0.15
0.12
33.09
32.88
2.38
Deep Preset 13
1.12
0.15
0.13
34.84
34.64
2.51
GenColor (Ours)
0.94
0.13
0.12
3,59
3.22
0.70
5.6 Texture Preservation Quantitative Results
As shown in Table GenColor achieves strong overall performance, with Color Matcher achieving the lowest TD score (0.42) while GenColor obtains competitive texture preservation metrics including tied for best GMSD (0.12) and best DISTS scores (0.13). GenColor excels particularly in color similarity with the lowest Semantic W1 (3.59), W, (3.22), and MS-SWD (0.70) values. The ablation study reveals the importance of key components in GenColor's texture preservation module training, such as the circular shift, which contribute to its balanced performance. When compared to existing methods like Color Matcher [IO] and hint color propagation [15] (3], GenColor demonstrates significant improvements across most metrics, highlighting its effectiveness in texture-preserving color transfer.
5.7 Visual Results
Figure 6 presents visual comparisons of GenColor with other methods representative images from the Adobe FiveK dataset. The qualitative results align with our quantitative findings, showing that GenColor generates more appealing and natural-looking enhancements compared to other methods. For texture preserving color transfer, Figure 7 shows that our method can better preserve the texture while achieving accurate color transfer. More results can be found in the supplementary website.
5.8 Robustness Evaluation
As shown in Figure & our method achieves superior performance on challenging cases, where prior methods often struggle. Additionally, Figure evaluates the robustness of our method across different

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Input
D&R 30
Exposure
[
3D-LUT
571
DPepLPF
26
RSFNet
2F
ICELUT
36
Expert
2
Ours
Figure 6: Visual comparison on the Adobe-FiveK dataset. GenColor produces more natural and visually pleasing results while preserving image details compared to other state-of-the-an methods.
SALE
FOR SALE
FOR SALE
SALE
FOR SALE
FOR SALE
SALE
00es se1
S 2055
DOGS
2065
0065
0008
-0085 aNI
BL
BU
BRY
BL
BL
Bu
Bs
Sa
Sut
Suc
Suc
Input
Diffusion Ref
NLUT
ColorMatcher
UniColor
15
DeepPreset
B3
Ours
Figure 7: Visual comparison of color transfer methods. Our GenColor method better preserves the texture details while achieving accurate color transfer compared to other approaches.
image resolutions. We have tested on various resolutions and found that GenColor, which incorporates a resizing operation during training data generation, maintains consistently high performance.
5.9 User Study
To evaluate the perceptual quality of GenColor, we conducted comprehensive user study involving 58 participants with varying levels of expertise in image processing and color enhancement, ranging from amateur to professional. Each participant was presented with 5 test images and their corresponding enhanced versions generated by all baseline methods. Participants rated the color enhancement results on a 5-point Likert scale based on color aesthetics. The results are presented in Figure IO Statistical analysis of the ratings demonstrates that GenColor the highest-rated method among users, confirming its ability to generate visually appealing and natural- enhancements that align with human perception. addition, we conducted pairwise comparison study further validate the superiority of GenColor, which is presented in the appendix S
6 Limitations and Future Work
Despite GenColor's overall effectiveness, exhibits limitations specific scenarios. Notably, performance is reduced when processing images with extensive regions of pure black white due to the inherent difficulty enhancing extreme luminance values. Additionally, the model may occasionally produce unintended hue transformations under extreme illumination or with ambiguously

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Bar (Bokeh)
(Mfotion)
Under- expostre
exposure
Aged Photo
Low Res
Bain-Streak
Film Grain
Input
GenColor
Input
GenColor
Figure 8: GenColor maintains robust performance on challenging cases.
User Rading
Weah Houx Brate
With Hote:
DAR
WiJ Roser
Exposure-
RSFNet
Decpl.PF
Wabh Bedter
Bode
GenColor (Ours)
ICELUT
(a) Texture
(b) Aesthetic
(c) Color
Figure 10: User study results. GenColor receives the highest rating among the compared methods.
Figure 9: Robustness on Various Resolutions.
colored objects. Furthermore, while trained the diverse ARTISAN dataset, the current optimization dynamics cause the model to prioritize a dominant aesthetic mode, resulting in nearly deterministic outputs rather than reflecting the full aesthetic variety of the data. Future work will focus on enhancing control and unlocking this diversity. We plan to incorporate hue preservation masks to mitigate undesired color shifts and investigate specialized loss functions weight blending for balancing fidelity and aesthetics. Crucially, we aim to explore conditional generation and style disentanglement techniques enable diverse outputs and facilitate data-efficient steering towards specific, user-desired styles using methods like LoRA.
alternative inference-time
7 Conclusion
In this paper, we have proposed GenColor, novel framework for high-quality image color enhance- ment. Our method leverages diffusion model generate expressive color transformations and employs a dedicated texture preservation network to maintain structural integrity while achieving fine-grained color adjustments. Extensive experiments demonstrate that GenColor outperforms state-of of-teo---teerrt methods on both color enhancement quality and texture preservation metrics, and generates visually appealing results that align with human perception. The introduction of the large-scale, high-quality ARTISAN-1M dataset further contributes to the advancement of color enhancement research. We have released the code and dataset the public to facilitate further research. Please refer to the project page: https : //tp//yydonnn.///// pro/pprojeccs//eeccooor
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Acknowledgment
This work was supported by Alibaba Group through Alibaba Innovative Research (AIR) Program and Alibaba-NTU Singapore Joint Research Institute (JRI), Nanyang Technological University, Singapore. We would like to thank the anonymous reviewers for their insightful comments and suggestions.
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A The ARTISAN-IM Dataset
A.1 Overview of the Dataset Collection and Curation Process
We collected 1.2M high-quality images from multiple online platforms, with Flickr being our primary source, Images were selected based on their visual quality, diversity in content, and color representation. We also manually curated the dataset to ensure its quality and diversity.
A.2 Data Sources and Criteria for Inclusion
The ARTISAN-IM M dataset represents significant advancement in image enhancement datasets, addressing the limitations of existing collections. While datasets like Adobe-FiveK (2) (5,000 images) and PPRIOK [22] (11,161 images) have been valuable for research, their limited size constrains the development of more sophisticated enhancement models. Similarly, while LAION-Aesther V2 [32]
large, its high-aesthetic subset primarily consists of non-photographic content like illustrations and
classic paintings.
A.3 Dataset Statistics
The ARTISAN-IM dataset contains 1.2. images. Table 11 shows the distribution of different attributes in our dataset. As shown in the table, our dataset includes 1.0M daytime images and 240.9K nighttime images. In terms of content, there is balanced distribution between images containing people (.17.0) and those without people (653.0K).
Distribution of ARTISAN-122 QA Scores
Attribute
Type
Count
25000
Time of Day
Day 1.0M
Count
20000
Night
240.9K
15000
10000
Object
People 617.0K
5000
w/o People
653.0K
3.8
4.4
4.8
5.0
QAlign Score
Figure 11: Left:
Distribution of attributes in ARTISAN- IM dataset. Right: Distribution of Q-Align scores across the dataset, showing our dataset's 'ss high aesthetic quality.
A.4
Quality Control
For robust quality control in our dataset curation process, we leverage Q-Align 34 a state-of-the-an aesthetic assessment framework that demonstrates superior correlation with human perceptual judg- ment compared to the CLIP+MLP predictor employed in the LAION-Aesthetic Dataset. Through evaluation by professional colorist, validate that Q-Align exhibits significantly higher consis- tency with human aesthetic assessment in photographic enhancement scenarios. Visual examples presented in Figure 12 demonstrate that Q-Align maintains high scoring fidelity for visually com- pelling photographs, whereas the LAION-Aesther attribution. These findings underscore the enhanced suitability of Q-Align for practical photographic applications compared to existing aesthetic prediction methodologies.
predictor shows substantial degradation in score
A.5 Comparison with Existing Datasets
Table 4 compares ARTISAN-IM with existing image enhancement datasets. ARTISAN-IM rep- resents advancement in both scale and quality, containing 1.2M professional-grade photographs with diverse artistic styles and lighting conditions. In contrast, Adobe-MIT FiveK [2) provides expert retouches but is limited to 5,000 images with global adjustments only. PPRIOK [22], while offering semantic masks and professional retouching, is constrained portrait photography. LAION-Aesthetics (20), despite its large scale, has its high aesthetic score subset dominated by non- photographic content (illustrations, paintings, digital art) with textures unsuitable for photographic enhancement, making it less applicable for real-world photography tasks.
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Q-Align: 4.58/5 LAION: 4.10/10
Q-Align: 4.26/5
Q-Align: 4.00/5
Q-Align: 4.14/5
LAION: 3.77/10
LAION: 3,51/10
LAION: 3.65/10
Figure 12: Comparison between Q-Align and LAION-Acstheticss CLIP+MLP predictor scores. Q-Align scores (higher is better) are more consistent with human perception of photographic quality compared to
predictor scores.
Table 4:
Detailed comparison of image enhancement datasets. ARTISAN-IM provides significantly
larger scale and broader diversity compared to existing datasets.
Dataset
Size Image Type
Annotations
Key Features
ARTISAN-IM
1.2M Web-crawled images Q-Align 3.8
Wide range of artistic styles
Diverse lighting conditions
Real-world photography
All professional-gee -rade photography
Adobe-MIT FiveK 2
5,000 Raw images
Expert retouches
Multiple expert retouchers
Global adjustments only
Limited scene diversity
Raw format with metadata
PPRIOK 22
11,161 Portrait photos
Expert edits masks
Portrait-focused focused
Semantic masks
Professional retouching
Limited to portrait genre
LAION-Aesthetics
12M Web-crawled images Aesthetic scores 2 6
High aesthetic quality subset Diverse sources: illustrations, paintings, and digital art No metadata for non- non-photographic content
B
The Color Generation Module
B.1
Motivation and Clarification on Expressiveness
The paper's claim regarding the proposed method's expressiveness, particularly in comparison to "Human Expert C" is rooted in specific and fundamental difference in adjustment paradigms. The comparison is not against human expert with an unrestricted toolset (e.g., Photoshop with masks), but rather against the global-only adjustment paradigm inherent to the Adobe5K dataset, for which "Expert C" serves well-known benchmark.
Global vs. Local Adjustment Paradigms The Adobe5K dataset, a cornerstone for supervised enhancement methods, primarily consists of edits made using global adjustment layers in Adobe Lightroom. This constrains expert (e.g., "Expert C") to applying single set of parameters (e.g.. exposure, saturation) uniformly across the entire image. Such global-only approach cannot, for instance, simultancously brighten subject's face while deepening the color of the sky, as one action would invariably counteract the other.
GenColor operates on a different principle. employs a generative model trained on the large-scale ARTISAN- M dataset. This dataset is not limited to global edits and contains 1.2 million high-quality images exhibiting diverse and sophisticated local aesthetics (e.g., selective color grading, dodging and burning). By training this data, GenColor learns to perform spatially-varying content-aware, and local adjusiments. develops a semantic understanding that allows to treat "sky," " "building," and "shadows" as distinct regions deserving different enhancements within the same operation.
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Table 5: Color adjustment parameters used training data generation. We carefully constrain hue rotation to [0, 0.3] and [0.7, 1.0] to avoid drastic shifts while maintaining expressive adjustments. For all other filters, we utilize the full range to improve model expressiveness, balancing enhancement quality and model versatility.
Parameter
Range
Used Range
Full Range
Description
Brightness
to 1.0
to
Additive brightness adjustment
Exposure
-5.0 to 5.0
5.0
Yes
Multiplicative brightness adjustment
Contrast
to 5.0
to
Yes
Expands/compress tonal range
Warmth
-1.0 1.0
to 1.0
Yes
Blue-yellow white balance shift
Saturation
5.0
5.0
Yes
Global color intensity
Vibrance
to
to
Selective color intensity boost
Gamma
to 10.0
t 10.0
Yes
Midtone brightness curve
Hue
Oo 1.0
0 to 0.3 & 0.7 1.0
No
Rotates colors around color wheel
Overcoming the Paired-Dats Bottleneck This generative approach is necessitated by key challenge in the field: the prohibitive cost and difficulty of collecting large-scale, paired datasets for fine-grained local enhancements. No such dataset for supervised local enhancement currently exists. The proposed method cleverly circumvents this data bottleneck by learning from a large, unpaired, high-quality image corpus.
Supporting Evidence The superior expressiveness of this local, content-aware approach is sup- ported by several results:
Visual Evidence (Figure 1): Visual comparisons and difference maps demonstrate this capability. GenColor can concurrently brighten a sunlit facade, deepen the blue of the sky, and lift shadows in the foreground. In contrast, the difference map for Expert C shows a uniform, global change, which is incapable achieving these distinct, targeted effects simultaneously.
Quantitative Metrics (Table 2): The method's higher expressiveness is quantitatively validated. On the C-VAR (Color Variance/Expressiveess) metric, designed measure fine- grained color transformations, GenColor achieves a score of 16.96, significantly surpassing Expert C's 10.91.
Perceptual Preference (Table 2b): In blind comparison, vision-language model pre- ferred GenColor's ' output for "visual beauty" over Expert C's 62 % of the time, suggesting the aesthetic benefits of these expressive, local adjustments.
In summary, the claim of greater expressiveness is direct reference GenColor's 's learned ability to perform local, content-aware enhancements, capability that fundamentally more powerful than the global-only adjustment paradigm that defines the "Expert C" benchmark.
B.2 Intensity of Color Adjustment Filters in Training Data Generation
The color generation module's training paradigm uses the original high-quality image as the target and color-adjusted version the condition, simulating real-world enhancement scenarios. This approach, using the ARTISAN-IM dataset, enables aesthetically pleasing results even with suboptimal input colors. We carefully control color adjustment intensities in training data generation balance enhancement quality and model versatility. As shown in Table 5 we constrain hue rotation to [0, 0.3] and [0.7, 1.0] to avoid drastic shifts while maintaining expressive adjustments. For all other filters, utilize the full range to improve model expressiveness. This represents a trade-off between preserving natural appearances and allowing creative enhancements, especially for objects with ambiguous color properties.
B.3 GenColor Not Sensitive to Text Prompt
GenColor's color generation module demonstrates robustness to text prompts, evidenced by the visual results in Figure [3] The model consistently produces similar outputs regardless of the prompt's content, including null, semantically incorrect, or color-incorrect prompts. This insensitivity text
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Empty Prompt
BI,IP Output There is man that is standing rocky hill
Color Mismatch There a man that is standing on green rocky hill
Semantic Error There is dog that is standing on the sea
Figure 13: GenColor's color generation module demonstrates robustness to various text prompts, consistently producing similar outputs regardless prompt content (null, semantically incorrect, or color-incorrect). This insensitivity to text input ensures stable and predictable performance across diverse scenarios.
Input Image
Seed Variant
Seed Variant 2
Seed Variant 3
Seed Variant
Figure 14: Visual demonstration of seed consistency. The model produces consistent outputs across different random seeds, with only minor variations in local details. The ensemble result further stabilizes the output.
input is advantageous for GenColor, color adjustments typically challenging to articulate verbally. Consequently, this characteristic ensures that GenColor's performance remains stable and predictable across various input scenarios.
B.4 GenColor Consistency Among Seeds
Our analysis of GenColor's sensitivity random seeds reveals strong output consistency, Figure demonstrates this visually, showing minimal variations across different seeds and stable ensemble result. This consistency is crucial for practical applications, ensuring reliable outputs regardless of the random seed used.
B.5 Multi-Stage Weight Blending
We analyze the model's s evolution during training, as visualized in Figure 15 Early stage outputs exhibit high saturation, middle stage outputs have the most appealing visual characteristics, and late stage outputs closely match the conditioning image. To balance aesthetic enhancement and texture preservation, strategically combine model weights from the middle and late training stages during inference. This blending yields a good balance between creative expressiveness and artifact control, as measured by the ratio of regions with strong hue changes RAH. and improves color consistency
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Input
(a) Early
(b) Mid
(c) Late
(d) Mid+Late
Stage
RAut
MS-SWDs4
(a) Early
19.1%
3.33
(b) Middle 3.2%
1.60
(c) Late
2.2%
1.60
(d) M+L
1.3%
0.76
Figure 15: Evolution of model output during training: (a) input image; (b) carly stage with unnatural colors; (c) middle stage with visually appealing results; (d) late stage matching conditioning image; (e) our proposed middle+late stage blending (M+L). M+L maintains visual quality while reducing strong hue changes and improving consistency across different generation seeds. The table shows reduced hue change regions (RAh) and improved color consistency (MS-SWDs) compared to individual stages.
across different seeds, quantified by the mean pairwise MS-SWD 11 between generated images MS-SWDs.
B.6 Comparing Weight Blending with Single Weight
We conducted an ablation study comparing different model checkpoints and their combinations to validate our weight mixing strategy. This includes the "mid" stage (high expressiveness), the "late" stage (high fidelity), single checkpoint taken from between these two stages ("mid-late") and our proposed 50/50 blend.
Table 6: Comparison of single checkpoints versus weight blending strategy. Our 50/50 blend of mid and late checkpoints significantly outperforms any single checkpoint, including the intermediate one, across all perceptual quality metrics while maintaining strong expressiveness.
Mid Weight
Mid-Late Weight
Late Weight
Q-Alignt
LAION
LIQE
NoR-VDPt
C-VAR1
1.0
0.0
0.0
4.08
5.66
3.54
68.96
22.91
0.0
1.0
0.0
4.24
5.77
3.72
68.8
17.87
0.0
0.0
1.0
4.25
5.77
3.73
69.09
16.69
0.5
0.0
0.5
4.29
5.83
3.76
69.16
16.96
The results in Table 6 demonstrate two key findings: (1) Simply selecting single intermediate checkpoint ("Mid-Late Weight") is suboptimal compared our blending strategy quality metrics (e.g., Q-Align 4.24 vs 4.29), and (2) Our 50/50 blend achieves the highest scores on all four perceptual quality metrics (Q-Align. LAION. LIQE, NoR-VDP) while maintaining strong expressiveness (C-VAR), Please note that C-VAR is not strictly "higher-is-better metric, as a high score can also be inflated by the significant hue change, noise and artifacts present in early-stage models. This makes the 50/50 blend clear, data-driven choice for balancing aesthetic expressiveness with photorealistic quality, validating our weight mixing approach over single checkpoint selection.
all key
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Table Detailed architecture of the SwinTrrnsffrmerr-aaste GenColor texture preservation module. B represents batch size. The network processes 1088x728 resolution images through 4 stages of feature extraction blocks (RSTB modules), each containing Swin Transformer Blocks, with careful dimension preservation and feature processing throughout.
Stage
Output Shape
Block
Details
Input Conv
[B. 60, 728, 1088]
Conv2d
Conv2d-1: 6-+60 channels, 3x3 kemel, stride=1, padding=1 LayerNorm-2: Normalizes fea- tures; PatchEmbed-3:, Embeds patches
Patch Embedding
[B, 792064, 60]
PatchEmbed
RSTB Block x4
[B, 792064, 60]
RSTB
Each RSTB contains 6 Swin- TransformerBlocks with residual connections
Patch Unembedding
[B, 60, 728, 1088]
PatchUnEmbed
Linear layers (60->120-ww dims) with GELU activation, fol- lowed by LayerNorm and re- shaping (pixel shuffling) back to image dimensions
Output Conv
[B, 3, 728, 1088]
Conv2d
Conv2d-2: 60-33 channels, 3x3 kernel, stride=1, 1, padding=1
C
The Texture Preservation Module
C.1 Detailed Architecture of the Transformer-based Network
We present the detailed architecture of the Swin Transformer- based texture preservation module, designed for preserving textures while transfering the diffusion generated color to the input image. As illustrated in Table 7 the model begins with an input convolutional layer that expands the channel dimension from 6 to 60 using a 3x3 kernel. This is followed by a Patch Embedding stage, which flattens the spatial dimensions and applies Layer Normalization. The core of the network comprises four Residual Swin Transformer Blocks (RSTB Block x4), each containing six Swin Transformer Blocks that integrate Window Attention mechanisms with six heads and a window size of 8, alongside Multi-Layer Perceptrons (MLP) with ratio of 2. These blocks facilitate deep feature extraction while maintaining spatial dimensions. Subsequently, the Patch Unembedding stage reshapes the features back to the original image dimensions, and final output convolutional layer reduces the channel dimension to 3, producing the restored RGB image. This architecture ensures meticulous dimension preservation and efficient feature processing, achieving robust performance in texture preserving color transfer scenarios.
C.2
Loss Functions and Their Weightings
The model is trained using combination of Huber loss, perceptual loss, and adversarial loss:
Liotal
CHuter + X2 ApCrrre + As Cadv
(1)
where \₁, A2. and Ag are the respective loss term weights.
Huber Loss: We employ the Huber loss [16 to measure the discrepancy between the model prediction and the ground truth.
Perceptual Loss: The perceptual loss computed between the VGG-19 feature maps of the output and ground truth, enabling similarity judgments in the semantic feature space.
Adversarial Loss:
We employ adversarial loss using a discriminator network D that distinguishes between real and fake images. The generator tries to create realistic outputs to fool D. The objectives are:
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Low Noise (Content Preserved)
High Noise (Content Distorted)
Canny: 0.0
Canny: 0.2
Canny:
Canny:
Canny:
Canny: 1.0
Top: Diffusion Reference Images Bettom: Texture Preservation Results
Figure 16: Effect of input-reference content similarity on texture preservation. We use Canny-based controlnct with GenColor's ' diffusion model at varying noise levels (0.0-1.0). Low noise values (0.0-0.4) maintain content similarity enabling effective texture preservation, while higher values (0.6-1.0) introduce distortions that compromise fusion quality.
Ladv D Be log D(x:)] + B log(1 D(G(=)))
(2)
Cadv G E Bz=p(=)] log D(G(=))
implementation, Ladv G uses a weighted binary cross-entropy loss. The total loss Ciota is utilized in backward propagation to update the generator, while the discriminator is optimized separately to enhance its proficiency in distinguishing between real and generated data.
C.3 Analyzing Content Affinity Requirements for Effective Texture Preservation
To rigorously evaluate content affinity requirements, we developed a dual-control methodology combining our first-stage color generation with Canny edge-based ControlNet conditioning. This integrated approach allows us generate reference images while systematically varying the content similarity between input and reference pairs across continuous range.
Our investigation demonstrates a strong relationship between texture preservation effectiveness and input-refcrence content similarity. Through systematic variation of Canny edge noise levels from 0.0 1.0 (as shown in Figure T6). we found that lower noise ranges (0.0-0.4) successfully maintain content affinity, enabling superior texture preservation in the fused results. Conversely, higher noise levels (0.6-1.0) introduce substantial content deviations that impair the fusion process.
It's important to note that in practical GenColor applications, only scenarios comparable to the first column of Figure ar relevant, noise factors. This experimental setup effectively demonstrates our algorithm's 's specialization in high-fideligg color transfer while maintaining robustness across varying conditions.
color generation module operates without additional
C.4 Challenging Examples for Texture Preservation
we present challenging cases for texture preservation. The image features a complex outdoor scene with a tropical resort setting, texture preservation module successfully maintains these fine details frrui the delicate veining in individual leaves to the subtle features
As shown in Figure 17
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Input
Diffusion Ref
Texture Preservation Result
Figure 17: Challenging texture preservation case. Our method successfully preserves fine details in complex scenes while enhancing colors. The scene contains intricate textures from vegetation, architecture, and human subjects that the diffusion reference distorts while producing appealing colors. Our texture preservation module effectively maintains these critical structural details while transferring the enhanced color palette.
of the human subject's appearance and foot. In contrast, the diffusion-only reference exhibits significant degradation, with smudged vegetation textures, loss of definition the human figure, and compromised detail in both facial features and foot. This example demonstrates GenColor's ability to achieve appealing color enhancement while preserving critical structural and textural elements that are essential for maintaining photorealistic image quality.
D The Global Filter Module
D.1 Detailed Implementation of the Global Filter Module
To enhance texture preservation performance, incorporate global filter module inspired by Harmonizer [19]. This lightweight yet effective component integrates seamlessly into the GenColor framework while preserving the sophisticated color transformations and texture details from previous stages.
The module employs five key filters (brightness, contrast, saturation, highlight, and shadow) that perform targeted adjustments to enhance image quality. A lightweight EfficientNet image encoder extracts features from which filter intensities are computed via simple MLP network trained end-to-end with the rest of the model.
For training. we utilize cleaned versions of the Adobe5K and HDRPlus datasets, ensuring overlap between training and test data. The global filters are trained for 100k steps with batch size 32, learning rate le-4, and Adam optimizer.
20

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The implementation details of each filter are identical to those in Harmonizer, with each filter designed preserve critical texture information while enhancing specific aspects of the image:
D.1.1 Brightness Filter
The brightness filter operates in HSV color space to modify the value (V) channel while preserving hue and saturation:
imageRGB imvge (h, 8,0) v)
For brightness parameter a E [-1,1] 1]. we compute an alpha multiplier:
1/((1 x e), x 1,
= 0
if
The adjusted value channel becomes v' v a, and the final image is converted back to RGB:
imageRGB HSV to RGB(h, s')
D.1.2 Contrast Filter
The contrast filter enhances differences relative to the mean luminance. For a contrast parameter
[-1,]]:
threshold mean(image"
2255/(256 [ 255]) -, if > otherwise
a,
The contrast adjustment is then applied as:
image' image + (image threshold) x
D.1.3
Saturation Filter
The saturation filter modifies color intensity while preserving luminance. First, we compute:
Gmin min(image imageg; image
Cmax max(image, n. image image
var Cmax
Gmin
ran Cmax + Cmin
mean ran/2
The saturation factor is calculated as:
var/ (ran + E), if mean < 0.5 var/(2- ran +e). otherwise
For saturation parameter & E [-1, 1J. we compute the adjustment factor:
1, if (a +)
0, otherwise
1/(s x m (1 (1- (1 m) +e) -1, if 2 0
1 +x.
if < 0
The final adjustment is:
image,
if 0 mean + (image mean) x a, otherwise
image'
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D.1.4 Highlight and Shadow Filters
These filters adjust the bright and dark regions respectively:
Highlight Filter (for parameter E [|)
x² + 1
image' = (nver(ccanp((ivee(((mggg)))))))) 0, 1))
Shadow Filter (for parameter [-1, 1)): x' -
image' Clampiimage" ,0,
E Experimental Setup
E.1
Justification for Metric Selection
Given the inherent subjectivity in color enhancement and texture preservation tasks, we adopt comprehensive suite of both classical and contemporary evaluation metrics.
For image quality assessment, our approach integrates perceptual measures such as Q-Align [4] LIQE [39], and LAION-Aestheties V2 [32), plus NoR-VDP [9] to handle no-reference dynamic range evaluation. Furthermore, C-VAR employed quantify the expressiveness or color variation introduced by enhancement.
For texture preservation evaluation, rely established metries like TD 7 (Texture Difference), GMSD [35] (Gradient Magnitude Similarity Deviation), and DISTS [5] (Deep Image Structure and Texture Similarity) to ensure structural fidelity. For color transfer assessment, advanced color distribution measures including Semantic Wi (Wasserstein- distance for regional color distribution), standard W1 [T] (Wasserstein-1 distance between global color distributions), and MS-SWD [I] (Multi-See Scall Sliced Wasserstein Distance) to capture alignment in color space.
employ
By leveraging these well-validated metrics across different aspects of enhancement, multi-metric strategy offers a robust quantitative evaluation of both enhancement quality and preservation accuracy.
E.2 Detailed Description of Evaluation Metrics
We use a comprehensive set of metrics to evaluate the performance of GenColor and other methods:
Color Enhancement Quality Metrics
Q-Align 34 (t): A non-reference perceptual quality metric that evaluates image aesthetic quality without requiring expert-retouched references, considering both color and structural aspects.
LAION (1): An aesthetic score from the LAION database, reflecting how well the enhanced result aligns with its dataset's learned aesthetic preferences.
-LIQE 39 (1): no-reference image quality assessment framework that employs multi-task learning jointly optimize classification and distortion identification tasks, enabling robust perceptual quality prediction without requiring reference images.
-oRRVDD 9 (t): A No-Reference Visual Difference Predictor specifically designed for high dynamic range content that assesses image quality without requiring reference image, focusing on natural image statistics. dynamic range perception, and human visual system response to luminance variations.
C-VAR (t): A measure capturing the "expressiveness" or color variation introduced by the enhancement. Higher values indicate more fine-grained or diverse transformations.
Texture Preservation Metrics
- TD 7 (D): Texture Difference score that quantifies the preservation of high-frequency details between input and enhanced images.
22

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()): Gradient Magnitude Similarity Deviation that compares gradient magnitude maps.
GMSD 351
DISTS [S] (4): Deep Image Structure and Texture Similarity metric that assesses
structural and textural similarity.
Color Similarity Metries
Semantic W1 (L): Wasserstein-11 distance for regional color distribution, measuring color similarity for semantic regions.
-W (4) [TO] Standard Wasserstein-1 distance for global color distribution.
MS-SWD IT ()): Multi-Scale Sliced Wasserstein Distance that compares color distributions across different spatial scales.
For all metrics, (f) indicates higher is better, while (L) indicates lower is better.
E.3 C-VAR Metric Details
C-VAR (Color-VARianee VARiance Ratio) a superpixel-bane based metric that estimates the expressiveness of an image enhancement by contrasting how consistently each region is shifted with how differently between regions those shifts behave. Higher C-VAR values therefore indicate strong, region-aware color edits that remain locally coherent.
The C-VAR score is obtained through the following procedure:
1. Superpixel Segmentation: Segment the original RGB image into N 200 perceptually cohesive regions using SLIC soo_segments-200 compactness-10), The same label mask is reused for its enhanced counterpart I'.
2. Color-Space Transformation & Difference Map: Convert both images to CIELAB. For every pixel p compute the Lab shift Ac, = Lab(()) 77) Tab(T,) (p) E T
3. Per-Superpixel Statistics: For each superpixel 8; (i 1,..., : N) calculate
the mean color shift μ4
Σ Acp.
[s.]
PE8;
the within-region covariance E; Varpes, ACp))
4. Variance Aggregation:
Intra-class variance (local inconsistency)
N
Σ 1=1
U E.).
Cintr
N
Inter-class variance (regional diversity)
tr Var (Hi))
Finter
5. C-VAR Score:
C-VAR
Cinter
= 10-6
V
C-VAR captures two critical traits of a color edit:
Regional Diversity (Fimer): large variations between superpixels signal expressive, context- aware adjustments.
Local Coherence (ainma: small variations within superpixel ensure the edit is not noisy or spatially erratic.
By normalising inter-class variance with the square-root of intra-class variance, C-VAR becomes scale-invariant and rewards transformations that respect object boundaries while diversifying color makin- particularly suitable for evaluating style-transfer, HDR tonemapping, and other generative color-enhancement tasks.
23

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E.4 Large Language Model (LLM) Preference Evaluation
To evaluate the perceptual quality of our color enhancement approach, we conducted a LLM prefer- ence evaluation using GPT-ol. a state-of-the-ar vision-languag model. This evaluation provides an additional perspective on aesthetic quality that complements our quantitative metrics.
For each round, we presented GPT-ol with three image versions in random sequence:
1. The original unprocessed input image
2. The expert-retouched reference image (Expert C)
3. Our GenColor enhanced result
We prompted the model with the following instruction:
"You are a professional photo retoucher specializing in color grading. will provide 3 photos with distinct color treatments. Analyze each photo, clearly explain your reasoning regarding color aesthetics, mood, harmony, and visual appeal, and clearly indicate your choice of which photo is more visually beautiful from a color perspective. You must make a definitive choice."
Due to the budget limit GPT-ol, we only evaluate the first 100 images in Adobe5K. As shown in Table 8 GenColor is preferred over human expert retouching.
Table 8: LLM preference evaluation results. Higher is better.
Method
LLM Preferencet
Original DmiginalImmage
0
Human Expert C
36
GenColor (Ours)
64
E.5 Baseline Methods Implementations
For all baseline methods, we rely on their official implementations and pre-trained weights. When dataset-specific weights are available, we adopt them for the corresponding experiments; otherwise, we default to models trained on the Adobe FiveK dataset. In the case of Distort-and-Reconn which does not provide pre-trained weights, we replicate the authors' training pipeline using the Adobe FiveK dataset. Notably, the official sisoor-and-Recovve implementation only supports low-resolution images (224x224), limiting its applicability to smaller inputs.
(30),
E.6 Hyperparameter Settings and Training Procedures
E. Color Generation Module
Our implementation leverages the official ControlNet 38 with Stable Diffusion 2.1 serving as the base model. Training is conducted at 512x512 resolution with an Adam optimizer using learning rate of lc-5 and batch size of 8. We train two model checkpoints one for 450K iterations (middle stage) and another for 700K iterations (late stage). At inference time, we utilize 30 denoising steps and a classifier-free guidance scale of 7.5. The positive prompt automatically generated by BLIP (2I]. and the negative prompt is "low quality, bad quality, low contrast, low saturation, dark, black and white, color bleeding, bw, monochrome, grainy, blurry, historical, restored, desaturate" As detailed in our main manuscript, we find that combining predictions from both middle-stage and late-stage checkpoints yields superior results.
E.6.2 Texture Preservation Module
For training the texture preservation module with 256 x 256 resolution data, we employ combination of loss functions to ensure both low-level accuracy and high-level perceptual quality. The loss formulation combines Huber loss (A = 0.05) for pixel-wise supervision, VGG-based perceptual loss (A = 1.0) for maintaining semantic consistency, an adversarial loss term (A 0.05) for enhancing output realism. We optimize the network using AdamW with initial learning rate of

---

le-4 that follows cosine annealing schedule decreasing to le-6. The model trains for 1M iterations with batch size of 4.
E.7 Hardware and Software Specifications
All experiments were conducted using PyTTrch framework on a NVIDIA V100 GPU with 32GB memory. To maximize training efficiency, employed an optimized data loading pipeline that leverages both CPU prefetching and GPU-accelerated processing.
Additional Experimental Results
Additional Results on Color Transfer Compare with Other Methods
Tables 9 10 and 11 present a comprehensive comparison of GenColor against state-of- late-offthheart color transfer methods across 3 benchmark datasets: Adobe5K, PPRIOK and FreeRaw. Our evaluation focuses two critical aspects:
1. Texture Preservation: We measure how well each method maintains the original image structure using three complementary metries:
TD (Texture Difference): Quantifies pixel-level structural changes
DISTS (Deep Image Structure and Texture Similarity): Evaluates perceptual texture similarity
GMSD (Gradient Magnitude Similarity Deviation): Assesses edge and gradient preser- vation
2. Color Similarity: We evaluate color transfer accuracy using three distribution baased metries:
Semantic Wi: Wasserstein-1 distance for regional color distribution
Wi: Standard Wasserstein-1 distance for global color distribution
MS-SWD: Multi-Scale Sliced Wasserstein Distance for color space alignment
The results demonstrate that GenColor achieves an exceptional balance between preserving original textures and transferring accurate colors. Notably, GenColor achieves competitive texture preservation scores while dramatically outperforming all baselines in color similarity metrics.
While methods like Color Matcher excel in specific texture metrics, they significantly underperform in color transfer accuracy. Conversely, while other methods achieve similar texture preservation ability with GenColor, they struggle with overall color transfer accuracy. GenColor's s balanced performance across all metrics underscores its effectiveness as comprehensive color enhancement solution.
Table 9: Texture preservation and color similarity analysis on Adobe5K, GenColor achieves excellent balance between preserving textures and maintaining color accuracy. All metrics are lower is better. Bold values indicate best performance.
Texture Preservation
Color Similarity
Method
TD L
DISTS
GMSD
Sem W1 1
W1 $
MS-SWD
NLUT
0.95
0.15
0.12
13.01
12.76
0.87
Color Matcher
0.42
0.17
0.14
31.45
31.34
2.03
UniColor
0.95
0.15
0.14
38.40
38.20
2.18
Deep Preset (w/o PPL)
1.17
0.15
0.12
33.09
32.88
2.38
Deep Preset
1.12
0.15
0.13
34.84
34.64
2.51
GenColor (Ours)
0.94
0.13
0.12
3.59
3.22
0.70
F.2 Analyzing Sensitivity to Local Color Fine Details Compared with Color Transfer Methods
GenColor demonstrates superior precision in transferring local color details compared to existing color transfer methods. As shown in Figure [8] our approach accurately preserves and enhances the unique color characteristies of each individual object within complex scenes, maintaining distinct color identities even areas with intricate patterns and fine boundaries.
25

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Table 10: Texture preservation and color similarity evaluation on PPRIOK portrait dataset. GenColor demonstrates superior performance in both texture metrics and color similarity. All metrics are lower better. Bold values indicate best performance.
Texture Preservation
Color Similarity
Method
TD |
DISTS
GMSD
Sem Wi
Wi
MS-SWD
NLUT
0.46
0.10
0.12
13.81
13.49
0.87
Color Matcher
0.23
0.13
0.13
36.23
36.07
2.06
UniColor
0.48
0.13
0.13
44.10
43.86
2.37
Deep Preset (w/o PPL)
0.56
0.14
0.12
40.95
40.73
2.65
Deep Preset
0.53
0.14
0.12
38.77
38.54
2.53
GenColor (Ours)
0.50
0.09
0.11
4.20
3.91
0.65
Table 11: Performance comparison on high-quality FreeRaw dataset. GenColor achieves the best
texture preservation and color similarity scores, demonstrating its effectiveness high-quality raw images. All metrics are lower is better. Bold values indicate best performance.
Texture Preservation
Color Similarity
Method
TD 4
DISTS +
GMSD 4
Sem W1 +
W1 4.
MS-SWD 1.
NLUT
0.69
0.13
0.12
14.21
13.73
0.92
Color Matcher
0.38
0.17
0.14
35.35
35.10
2.13
UniColor
0.57
0.15
0.14
43.19
42.68
2.35
Deep Preset (w/o PPL)
0.91
0.15
0.12
37.54
37.05
2.51
Deep Preset
0.90
0.16
0.12
37.68
37.16
2.56
GenColor (Ours)
0.80
0.12
0.11
3.73
3.31
0.70
Color Matcher applies transformations based primarily global color statistics, often overlooking local color variations that are crucial for realistic enhancement. This results in homogenized colors that fail to respect object boundaries and fine details. While NLUT and Deep Preset methods attempt to address this limitation through more sophisticated transformations, they still struggle with preserving the distinct color identity of individual objects, particularly in complex scenes with multiple elements.
UniColor operates patch-level granularity, which represents an improvement over global methods remains insufficient for truly fine-grained color transfer. Additionally, its approach of fixing the L. channel (luminance) while only modifying the and channels in the Lab color space limits its ability to perform comprehensive color enhancements that might require luminance adjustments.
In contrast, GenColor's two-stage approach offers several advantages for preserving local color details:
1.
The Color Generation module creates reference images that maintain spatial coherence with the input, ensuring that color transformations respect object boundaries and local contexts.
Image Coter Uhsttbettes 2931 Mauaiaan
Image Ulirtbation EX1
Coke Mandeer
Ehatake
Dealyy
NEUT
NLUT
Nound Proset
Chlrritacher
NULT
1.
Gkelakher
NLUT
034
Duadty
Deadit
Figure 18: Comparison of color transfer methods objects with fine color details.
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2. Our Texture Preservation module, with its specialized degradation strategy, maintains precise alignment between the input and reference images, preserving fine-grained color details while transferring the enhanced color palette.
3. The transformer-based architecture in our texture preservation network excels at capturing long-range dependencies, allowing it maintain consistency across similar objects while respecting their unique color characteristics.
This sensitivity to local color details is particularly evident scenes with multiple objects of varying colors and textures. GenColor successfully preserves these nuances while applying aesthetically pleasing enhancements, resulting in more realistic and visually appealing results that outperform existing approaches.
F.3
Ablation Study of Degradation Texture Preservation Module
The additional datasets results are shown in Tables 12 and we demonstrate the effectiveness
of our texture preservation module. The results similar those in the main paper.
Our ablation study in Table reveals the importance of each component our degradation strategy. As further illustrated in Figure [9 each degradation technique contributes uniquely to the final result. Notably, circular shift plays crucial role in texture preservation, as removing it significantly increases the TD score from 0.94 to 1.37, indicating substantial texture degradation. This is visually evident in "w/o Circular Shift" version exhibits noticeable artifacts around the nose area, compromising overall texture quality.
Mcanwhile, stroke noise and random resize primarily influence color similarity metrics, with minimal impact on texture preservation scores. When these components are removed, we observe increased Semantic Wasserstein- and Wasserstein-1 distances, suggesting poorer color distribution matching. This is confirmed in where both the "w/o Random Resize" and "w/o Stroke Noise" variants display model.
higher color deviation, particularly in the face and hair regions compared
The Diffusion Reference demonstrates why texture preservation is essential note the misalignment with the input image, particularly in the eyeball position and clothes pocket details. Overall, our full degradation strategy achieves the best balance, with optimal performance in color similarity metries (Sem W1. W1. and MS-SWD) while maintaining competitive texture preservation scores, resulting in the most visually coherent enhanced image.
Input
Diftusion Reference
w/o
w/o
w/o
Full
Random Resize
Stroke Noise
Circular Shift
Figure 19: Visual comparison different degradation strategies. The Diffusion Ref shows texture misalignment with the input (note eyeball and pocket positions). w/o Random Resize and w/o Stroke Noise introduce colour drift in the face and hair, while w/o Circular Shift yields artefacts around the nose, highlighting the importance of each component.
F,4 Ablation Study on Different Components
In this section, we conduct comprehensive ablation study to evaluate the contribution of each component in our architecture. Tables 16 and present the results on Adobe5K, PPR10K and FreeRaw datasets, respectively.
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Table 12: Texture preservation technique analysis on Adobe5K dataset. Results show how different components affect texture quality and color similarity. All metrics are lower is better. Bold values indicate best performance.
Texture Preservation
Color Similarity
Degradation
TD
DISTS
GMSD |
Sem Wi
Wi (
MS-SWD
w/o Random Resize
0.76
0.14
0.12
6.21
5,96
0.78
w/o Stroke Noise
0.77
0.14
0.12
5.74
5.44
0.78
w/o Circular Shift
1.37
0.14
0.12
15.49
15.40
1.20
Full
0.94
0.13
0.12
3.59
3.22
0.70
Table 13: Analysis of texture preservation techniques on PPRIOK portrait dataset. Study reveals
impact of different operations texture and color. All metrics are lower is better. Bold values indicate best performance.
Texture Preservation
Color Similarity
Degradation
TD L
DISTS &.
GMSD L
Sem W1 4
Wi L
MS-SWD
w/o Random Resize
0.36
0.10
0.11
5.98
5.69
0.76
w/o Stroke Noise
0.37
0.10
0.11
5.23
4.87
0.78
w/o Circular Shift
0.76
0.09
0.11
12.69
12.57
0.99
Full
0.50
0.09
0.11
4.20
3.91
0.65
Our analysis reveals several key insights:
1. The Color Generation module (C) significantly enhances overall color quality and expres- siveness, as evidenced by the substantial improvements C-VAR scores. When this module is included, we observe a 65. 8% increase in C-VAR on Adobe5K (from 2.84 to 4.71) and 10.0% improvement on PPR (from 3.10 3.41). Additionally, the Color Genera- tion module contributes to better perceptual quality, shown by consistent improvements in Q-Align and LAION scores across both datasets.
2. The Texture Preservation module (T) plays crucial role in maintaining the original image details while allowing for color transformation. This is demonstrated by the significantly lower TD scores when this module is present. Without compromising enhancement quality, the texture module ensures that important structural elements of the image remain intact.
3. The Global Filter (G) further refines the overall image quality by providing global adjust- ments that complement the local operations of the other modules. While its individual contribution may appear subtle in some metrics, its presence in the full model helps achieve a balanced performance across all evaluation criteria.
The full model, which integrates all three components, achieves the most balanced performance across the diverse of metrics. This confirms that each component addresses specific aspect of the enhancement process, and their combination yields a robust and versatile enhancement system. As shown in Figure 20, each component contributes uniquely the final result, with the full model producing the most visually appealing output.
Table 14: Evaluation of texture preservation techniques on high high-quulity FreeRaw dataset. Results show how different strategies affect performance, with the full model achieving optimal balance. All metrics are lower better. Bold values indicate best performance.
Texture Preservation
Color Similarity
Degradation
TD L
DISTS
GMSD \
Sem W,
Wi L
MS-SWD
w/o Random Resize
0.57
0.13
0.12
6.30
5.83
0.77
w/o Stroke Noise
0.59
0.13
0.12
5.70
5.28
0.76
w/o Circular Shift
1.21
0.11
0.11
14.64
14.55
1.09
Full
0.80
0.12
0.11
3.73
3.31
0.70
28

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Input
Global Filter Only
w/o Global Filter
Full
Figure 20: Visual comparison of different module configurations. The Global Filter alone provides basic adjustments lacks expressiveness and cannot achieve high brightness levels. Without Global Filter, the image shows improved color but insufficient contrast. The full model combines the strengths of all components, delivering optimal brightness, contrast, and color expressiveness for the most visually appealing result.
Table 15: Ablation study on Adobe5K dataset analyzing each component's contribution. C: Color Generation, T: Texture Preservation, G: Global Filter. TD (lower better), Q-Align, LAION, LIQE, NoR-VDP, and C-VAR (all higher = better). Best values in bold.
Architecture
Metrics
C
T
G
TD1
Q-Alignt
LAION1
LIQE!
NoR-VDP*
C-VAR
0.47
4.09
5.60
3.47
66.26
10.35
V
2.75
4.32
5.90
3.17
62.19
13.24
0.99
4.26
5.77
3.70
70.21
12.02
1.15
4.29
5.83
3.76
69.16
16.96
F5 Standard Deviation for all Metrics the Adobe5K Dataset
The standard deviation analysis reveals an important finding about performance consistency. For most metrics (Q-Align, LAION, LIQE), the standard deviations across all top-performing methods, including ours, are highly comparable, indicating similar levels performance consistency. While some methods like D&R show lower variance on certain metrics, they have significantly lower mean performance. In contrast, GenColor not only achieves the highest mean scores but does so with variance that is par with, even lower than (c.g.. NoR-V VDP), other leading approaches.
F.6 Denoising Steps Analysis
To optimize the computational efficiency of GenColor while maintaining high-quality results, conducted comprehensive analysis of the relationship between the number of denoising steps and performance metrics. The full results on the Adobe5K dataset are presented in Table 19
Table 16: Ablation study on PPR10K portrait dataset analyzing each component's contribution. C: Color Generation, T: Texture Preservation, G: Global Filter. TD (lower better). Q-Align, LAION, LIQE, NoR-VDo and C- VAR (all higher better). Best values in bold.
Architecture
Metries
C
TDJ
Q-Alignt
LAION1
LIQE!
NoR-VDP*
C-VAR
0.20
4.21
6.13
2.98
68.27
12.92
1.68
4.45
6.35
2.81
66.23
12.83
0.53
4.36
6.24
3.13
71.24
10.79
0.58
4.38
6.26
3.21
71.11
13.57
29

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Table 17: Ablation study on the high-quality FreeRaw dataset analyzing each component's contribu- tion. C: Color Generation, T: Texture Preservation, G: Global Filter. TD (lower better). Q-Align,
LAION, LIQE, NoR-VDP, and C-VAR (all higher better). Best values in bold.
Architecture
Metrics
C
TDL
Q-Alignt
LAION1
LIQET
NoR-V ARR-VDPT
C-VAR
0.29
4.36
5.77
3.94
63.18
9.62
2.42
4.53
6.03
3.51
60,46
13.97
0.85
4.47
5.89
4.15
68.19
10.94
0.96
4.51
5.96
4.22
67.03
14.93
Table 18: Standard deviation analysis for all objective metrics across the Adobe5K dataset. Lower standard deviation indicates more consistent performance across the dataset. Our method achieves competitive consistency while maintaining superior mean performance.
Method
Std Q-Align! Std LAION, Std LIQE, Std NoR- Std C-VAR!
3D-LUT
0.43
0.91
0.91
4.07
6.81
RSFNet
0.43
0.91
0.91
3.84
6.61
DeepLPF
0,45
0.92
0.92
3.72
6.50
ICELUT
0.45
0.92
0.91
4.17
6.64
D&R
0.31
0.83
0.45
6.33
9.24
D&R (ARTISAN)
0.29
0.85
0.42
6.31
9.07
Exposure
0.45
0.92
0.85
4.58
8.63
Exposure (ARTISAN)
0.44
0.91
0.84
4.67
8.52
GenColor (Ours)
0.43
0.93
0.84
3.45
8.18
Key Finding: Our analysis shows that while the performance is low with very few steps (1-5), the quality rapidly converges.
can reduce the number of denoising steps from 30 down to 15 15-effectively halving the runtime of our most computationally intensive module-with no obvious degradation in quality across all key metrics. Hence, the performance at 15 steps is virtually identical to that 30 steps. This offers favorable speed-quality trade-off, making the method more practical for roal-world applications.
Beyond this immediate optimization, the efficiency of Gen- Color can be further improved by incorporating techniques from the rapidly advancing field of fast diffusion sampling. Methods such as knowledge distillation (e.g., Progressive Distillation) or consistency models could potentially reduce the step count to as few as 1-4 steps while maintaining high quality, and we consider this as promising direction for future work.
Future Work and Broader Context:
G Additional User Study: Pairwise Comparison Study
To further validate our findings and provide additional statistical rigor, we conducted an additional, dedicated randomized pairwise comparison (2AFC) experiment.
Methodology: We recruited 25 participants, and each completed 20 comparison trials. In each trial, the system randomly selects two of the seven methods for head-to-head comparison on given image. Participants then choose the more visually appealing result. This process produces a total of 500 pairwise judgments.
We analyzed the data using the Bradley-Terry model derive preference score for each method. The results show that GenColor achieved the highest preference score, significantly outperforming all baselines. The complete ranking presented in Table 20
Results:
Crucially, the final ranking of all methods from this new pairwise study highly consistent with the ranking derived from our original side-by-side rating study. This agreement between two different comparative methodologies provides strong support for our conclusions.
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Table 19: Analysis denoising steps vs. performance on Adobe5K dataset. All metrics show higher is better except where noted. Performance rapidly converges after 15 steps, enabling favorable speed-quality trade-off.
Denoising Steps
Q-Align
LAION
LIQE
NoR-VDP
2.75
4,86
1.91
68.75
2
2.84
4.92
2.01
68.95
3
3,88
5,42
3,30
69.09
5
4.17
5.67
3.61
69.20
10
4.28
5,80
3.74
69.19
15
4.29
5.82
3.75
69.18
20
4.29
5,83
3.76
69.15
25
4.29
5.83
3.76
69.12
30
4.29
5.83
3.76
69.16
Table 20: Pairwise comparison results using the Bradley-Terry model. GenColor achieves the highest
preference score among all methods.
Rank
Method
Bradley-Terry Score
GenColor (Ours)
100.00
2
RSFNet
72.10
3
DeepLPF
67.07
4
ICELUT
58.00
5
3D-LUT
52.31
6
Exposure
25.92
7
D&R
17.18
H Additional Comparison with Style Transfer Methods
To provide comprehensive evaluation of our approach, we visually compare GenColor against state-of-the-ars style transfer methods, specifically CAPVST [33] and StyA2K (40). While these methods differ fundamentally from color matching/color style transfer approaches in that they modily both color and textural elements during style transfer, this comparison helps demonstrate the unique capabilities and advantages of our color-focused enhancement pipeline. As shown in Figure 21 method achieves more accurate color transfer while preserving the original image content.
I Additional Comparison with Commercial Color Transfer Tools: NeuralPreset and Adobe Photoshop
We further compare our method with NeuralPreset 18 state-of-the-art color style transfer approach.
While the official implementation is not publicly available, we evaluate against Tinge, an application that implements the same methodology. Additionally, we benchmark against Adobe Photoshop's color matching functionality, which serves as an industry standard baseline. Due the manual nature of generating results with these tools, we conduct this comparison on curated set of 20 representative images that span diverse scenarios and color distributions.
Table 21 presents a comprehensive quantitative comparison across three key aspects: texture preser- vation, quality, and color similarity. Our method, GenColor, achieves superior performance in most metrics. Notably, while Adobe PhotoShop shows strong texture preservation (TD: 0.801) and Neural Preset excels in DISTS (0.112), GenColor maintains competitive performance in these aspects while significantly outperforming in quality metrics (Q-Align: 0.023, LIQE Gain: 0.704, NoR-VDP Gain: 8.540). Most importantly, GenColor demonstrates substantially better color similarity scores across all metrics (Semantic W1: 4.544, W₁: 4.221, MS-SWD: 0.647), indicating more accurate color transfer while preserving image structure.
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Input
Diffusion Ref
CAPVST 33
StyA2K 440
GenColor (Ours)
Comparison with style transfer methods. While style transfer methods modify both color and texture, our method focuses on color enhancement while preserving the original image content.
Figure 21:
Table 21: Comparison results of commercial applications on texture preserving color transfer per- formance. Metrics categorized into Texture Preservation and Color Similarity. Best results bolded and second-best are in regular text.
Texture Preservation
Color Similarity
Method
TD
DISTS +
GMSD
Sem W1 +
W1
MS-SWD +
Adobe PhotoShop
0.80
0.14
0.13
12.19
11.18
0.95
Neural Preset
1.60
0.11
0.13
10.97
9.78
0.86
GenColor (Ours)
0.97
0.13
0.13
4.54
4.22
0.65
Limitations and Future Work
J.1 Failure Cases
Despite the overall effectiveness of GenColor, we observe occasional limitations in two specific scenarios. First, as demonstrated in Figure 23 the method exhibits reduced performance when processing images containing substantial regions of pure black and white values. This limitation stems from the inherent difficulty in meaningfully enhancing regions with extreme luminance values while maintaining natural appearance.
Second, as illustrated Figure 24 the model may produce unintended hue transformations when processing scenes containing extreme illumination conditions objects with perceptually ambiguous colors. While these outputs maintain aesthetic appeal, they might deviate from the desired color enhancement objectives. We note that this limitation seeds in the Diffusion Generator (e.g., the blue and purple clothes variations in Figure [4 first row). For future iterations, we propose incorporating a hue preservation mask as an additional conditioning signal during both training and inference to provide more precise control over color transformations.
often be mitigated by utilizing alternative
J.2 The Paradox of Deterministic Output from Diverse Data
Your observation is accurate. The nearly deterministic output stems from the optimization dynamics of our current training objective.
While ARTISAN's data distribution is highly diverse and multi-modal (representing many styles), the model strongly conditioned on the input image ControlNet. The training process optimized to find the most probable, high-quality enbancement. This encourages the model converge towards a nigh-probabiilie density region within the distribution- often resembling a "mean" or dominant aesthetic mode. The current objective function prioritizes robust, broadly appealing automatic solution rather than diversity of output.
J.2.1 Implications and Future Directions
As you rightly suggest, this realization opens up significant avenues for future work to build upon the GenColor foundation.
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Input
Diffusion Ref
Adobe PhotoShop
Neural Preset [18]
GenColor (Ours)
Figure 22: Visual comparison with commercial applications. Our method achieves more accurate color transfer.
Input
Dead Black & White
Diffusion Ref
Texture Preservation Result
Input
Dead Black & White
Diffusion Ref
Texture Preservation Result
Figure Examples showing how our method cannot handle input with large area of pure black and white regions.
To move beyond a single deterministic output and unlock the variety inherent in ARTISAN, the model needs mechanisms navigate the different modes of the aesthetic distribution. Future work could explore:
1. Unlocking the Aesthetic Variety within ARTISAN
Conditional Generation: The most promising direction is transitioning conditional generation. By incorporating style embeddings or exemplar images during training, the model could learn navigate the diverse aesthetic landscape within ARTISAN.
Disentanglement of Style Codes: Introducing explicit latent style codes during training could disentangle different aesthetic modes. During inference, sampling different codes would yield diverse, yet equally valid, outputs for the same input.
Input
GenColor
Figure 24: Example of undesired hue changes for objects with ambiguous colors. While the enhanced image remains visually pleasing, the hue shift may not be appropriate for some scenarios.
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2. Steering the Model Towards Desired Styles The current tight coupling between GenColor and the dominant aesthetic of ARTISAN can be adapted. The strength of our approach is that the model has already learned the complex task of high-quality, content-aware enhancement, providing a robust foundation for specialization.
Dataset Requirements for Steering: To steer the model toward specific style, another massive dataset is not required. As you hypothesized, a smaller, highly curated dataset exhibiting consistent target style would be effective for fine-tuning.
Data Efficiency and Adaptation: Because the model is already well-initialized by AR- TISAN, we anticipate this adaptation could be highly data-efficient, potentially requiring only a few dozen few thousand samples. Techniques like Low-Rank Adaptation (LoRA) could make this style specialization highly practical.
Model Behavior: "Snapping" vs. Interpolation The question of whether the model would "snap" into different sub-styles or interpolate between them is excellent. We hypothesize that the behavior would depend on the implementation of the steering mechanism:
Interpolation: Given the continuous nature of the diffusion latent space, if style is controlled via continuous embeddings or adjustable weights (e.g., varying the influence of LoRA or using classifier-free guidance), the model would likely be capable of smoothly interpolating between styles.
Snapping: If the steering is achieved by switching between distinct fine-tuned models, or the fine-tuning signal for style is very strong and distinet from the ARTISAN mean, the output would likely "snap" to the new style (mode-switching))
J.3 Alternative Approaches Weight Blending: Loss Function Design
While our weight-biending approach provides pragmatic and effective solution for balancing aesthetics and fidelity, we acknowledge that alternative approaches could explored. One promising direction, suggested by reviewers, would be to design specialized loss functions that directly guide the model to an optimal state during training, rather than blending weights at inference time.
Such an approach could potentially incorporate terms that explicitly penalize strong hue changes while maintaining fidelity the conditioning image, effectively learning the desired balance during the training process itself. This could involve multi-objective optimization techniques or carefully designed loss components that capture both aesthetic quality and color consistency.
However, exploring and properly tuning such loss functions would require extensive experimentation and series of lengthy training runs validate their effectiveness across different scenarios. Given the computational constraints and the proven reliability of our current weight-blending method, we leave this investigation as valuable future work. Our current approach offers the advantage of being robust. simple to implement, and achieving consistent results without altering the complex training dynamics of the diffusion model.
K
More Visual Results
Additional qualitative results and comparisons can be found on our supplementary website, which is included the supplementary materials package.
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Reviews:
[{"review_id": "AHq3JPR5hy", "reviewer": "Reviewer_g7My", "summary": "Color enhancement in digital photography remains a challenging task, requiring precise control, adaptability, and texture preservation. Existing methods often compromise one or more of these aspects. Authors present GenColor, a diffusion-based framework that reformulates enhancement as conditional image generation. By leveraging ControlNet and a custom training strategy, GenColor achieves high-quality, adaptable color transformations.", "questions": "1. Is is possible to speed up proposed solution? \n2. Have you considered pair-wise image comparissons for more robust result?", "limitations": "All the limitation are described in a clear manner", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strenghts:\n\n1. Very good resulting quality\n2. Good methodology for result evaluation, it is important to use user studies for such type of tasks\n3. Main SotA methods have been used for comparisson\n4. New dataset is prepared\n\nMainly I almost do not see weakness in this paper. The only think I would porpose is to show not only mean quality, but also variance", "was_revised": true, "final_justification": "I believe that this paper is pretty good. Authors spent ennormous amount of time to have these results and provide excelent formatting and description. In my opinion it is a good example for such a conference.", "rebuttal": "We thank this reviewer for the positive feedback and the insightful comments/suggestions for our work. We address your comments/questions below, and will update our paper accordingly.\n\n> Weakness: The only thing I would propose is to show not only mean quality, but also variance.\n> \n\n**Response:** We agree that understanding the variance, in addition to the mean, provides a more complete picture of the consistency and reliability of our method.\n\n**1. Variance in Our User Study:**\nWe would like to clarify that for our user study results in **Figure 20**, we have already included **error bars representing the standard deviation** of the user ratings. These bars visually demonstrate the spread of opinions for each method, directly addressing the need for variance analysis in our subjective evaluation.\n\n**2. Variance Analysis for Objective Metrics:**\nAs suggested, we have computed the standard deviation for all objective metrics across the Adobe5K dataset. The full results are presented in the following table.\n\n| **Method** | **Std Q-Align↓** | **Std LAION↓** | **Std LIQE↓** | **Std NoR-VDP↓** | **Std C-VAR↓** |\n| --- | --- | --- | --- | --- | --- |\n| 3D-LUT | 0.43 | 0.91 | 0.91 | 4.07 | 6.81 |\n| RSFNet | 0.43 | 0.91 | 0.91 | 3.84 | 6.61 |\n| DeepLPF | 0.45 | 0.92 | 0.92 | 3.72 | 6.50 |\n| ICELUT | 0.45 | 0.92 | 0.91 | 4.17 | 6.64 |\n| D&R | 0.31 | 0.83 | 0.45 | 6.33 | 9.24 |\n| D&R (ARTISAN) | 0.29 | 0.85 | 0.42 | 6.31 | 9.07 |\n| Exposure | 0.45 | 0.92 | 0.85 | 4.58 | 8.63 |\n| Exposure (ARTISAN) | 0.44 | 0.91 | 0.84 | 4.67 | 8.52 |\n| GenColor (Ours) | 0.43 | 0.93 | 0.84 | 3.45 | 8.18 |\n\n**Key Insight from the Results:** The above results reveal an important finding. For most metrics (Q-Align, LAION, LIQE), the standard deviations across all top-performing methods, including ours, are **highly comparable**. This indicates that these methods exhibit a similar level of performance consistency across the metrics. While some methods like D&R show a lower variance on certain metrics, they have a significantly lower mean performance. In contrast, GenColor not only achieves the highest mean scores but does so with a variance that is on par with, or even lower than (e.g., NoR-VDP) other leading approaches.\n\n> Question: Is it possible to speed up proposed solution?\n> \n\n**Response:** This is an interesting question/suggestion. The computational cost of GenColor's three-stage pipeline is dominated by Phase 1, the iterative denoising process in our diffusion-based Color Generation Module. Our other modules—Phase 2 (Texture Preservation) and Phase 3 (Global Adjustment)—are single-pass networks and are generally very efficient.\n\nTherefore, to speed up the overall runtime, we focus our analysis on the primary bottleneck. Prompted by your question, we have conducted a detailed analysis of the trade-off between the number of denoising steps in Phase 1 and the final enhancement quality. The full results on the Adobe5K dataset are presented below.\n\n| **Denoising Steps** | **Q-Align↑** | **LAION↑** | **LIQE↑** | **NoR-VDP↑** |\n| --- | --- | --- | --- | --- |\n| 1 | 2.75 | 4.86 | 1.91 | 68.75 |\n| 2 | 2.84 | 4.92 | 2.01 | 68.95 |\n| 3 | 3.88 | 5.42 | 3.30 | 69.09 |\n| 5 | 4.17 | 5.67 | 3.61 | **69.20** |\n| 10 | 4.28 | 5.80 | 3.74 | 69.19 |\n| **15** | **4.29** | 5.82 | 3.75 | 69.18 |\n| 20 | **4.29** | **5.83** | **3.76** | 69.15 |\n| 25 | **4.29** | **5.83** | **3.76** | 69.12 |\n| 30 (Our Paper) | **4.29** | **5.83** | **3.76** | 69.16 |\n\n**Key Finding:** Our analysis shows that while the performance is low with very few steps (1-5), the quality rapidly converges. We can reduce the number of denoising steps from **30 down to 15**—effectively **halving the runtime** of our most computationally intensive module—with **no obvious degradation in quality** across all key metrics. Hence, the performance at 15 steps is virtually identical to that at 30 steps. This offers a favorable speed-quality trade-off, making the method more practical for real-world applications.\n\n**Future Work and Broader Context:**\nBeyond this immediate optimization, the efficiency of GenColor can be further improved by incorporating techniques from the rapidly advancing field of fast diffusion sampling. Methods such as knowledge distillation (e.g., Progressive Distillation) or consistency models could potentially reduce the step count to as few as 1-4 steps while maintaining high quality, and we consider this as a promising direction for future work.\n\n> Question: Have you considered pair-wise image comparisons for more robust result?\n> \n\n**Response:** We thank the reviewer for this insightful suggestion. We would like to point out that our original evaluation framework has already designed around this core principle, and how we may further strengthen it with a new study inspired by your suggestion here.\n\n**1. Our Existing Framework was Built on Comparative Judgment:**\n\nOur original evaluation already employed two distinct methods to encourage or enforce comparison:\n\n- **Side-by-Side Comparative Rating (User Study):** While our primary user study collected ratings on a 5-point scale, it was designed as a **comparative task**. For each source image, we presented the outputs from **all 7 competing methods simultaneously** on a single screen. This side-by-side presentation naturally forces participants to make relative assessments before assigning a score to any individual image. This design effectively mitigates the noise of isolated, absolute scoring and produces Mean Opinion Scores (MOS) that reflect a strong comparative user preference.\n- **Forced-Choice LLM Evaluation:** To complement this, our evaluation with GPT was a strict **three-alternative forced-choice (3AFC) task**. The model was required to select the single best result from a set including our method, the original, and the high-quality Expert C baseline. GenColor's definitive win in this direct comparison provides strong, preference-based evidence that corroborates our user study.\n\n**2. New Randomized Pairwise Study for Further Validation:**\n\nTo provide even more direct evidence and fully embrace your suggestion, we have conducted an additional, dedicated **randomized pairwise comparison (2AFC) experiment**.\n\n- **Methodology:** We have recruited 25 participants, and each completed 20 comparison trials. In each trial, the system **randomly selects two of the seven methods** for a head-to-head comparison on a given image. Participants then choose the more visually appealing result. This process produces a total of 500 pairwise judgments.\n- **Results:** We have analyzed the data using the Bradley-Terry model to derive a preference score for each method. The results show that **GenColor achieved the highest preference score, significantly outperforming all baselines** .\n\n| **Rank** | **Method** | **Bradley-Terry Score↑** |\n| --- | --- | --- |\n| **1** | GenColor (Ours) | **100.00** |\n| 2 | RSFNet | 72.10 |\n| 3 | DeepLPF | 67.07 |\n| 4 | ICELUT | 58.00 |\n| 5 | 3D-LUT | 52.31 |\n| 6 | Exposure | 25.92 |\n| 7 | D&R | 17.18 |\n\nCrucially, the final ranking of all methods from this new pairwise study is **highly consistent** with the ranking derived from our original side-by-side rating study. This agreement between two different comparative methodologies provides strong support for our conclusions."}, {"review_id": "RwnTismQmh", "reviewer": "Reviewer_vwzM", "summary": "This paper presents a new no-reference fully automatic method for expressive color enhancement for photos. The proposed method starts with a new diffusion-based color generation module, trained on a new high quality dataset. The results from this module may contain artifacts, so a second new texture preservation module is in charge of transferring just the colors over the original textures at the original resolution. Finally, some global adjustments are applied to compensate for some contrast limitations of the first module. Results are of high quality, showing consistency and robustness with respect to some parameters in the pipeline. Evaluation is extremely thorough, analyzing almost every technical decision made.", "questions": "Related to the weaknesses listed before:\n- Figures 12 and 13 show the robustness and predictability of the method. But these, along the way the ARTISAN dataset was collected and used for training, raise questions about user control, and the ability of the method to learn specific styles through other carefully curated datasets. More comments on this would be helpful. Otherwise, a tool that is only able to generate a single type of style without allowing user control feels quite limited, specially the kind of network models involved.\n\n- Table1 shows methods [28] and [13] perform worse when trained over ARTISAN. Why is that? Could that mean that ARTISAN is more useful for approaches like the one presented in this paper? That would limit the impact of the dataset.\n\n- The evolution shown in Figure 14 is quite interesting. Could there be a way to force the model to stabilize in between 'mid' and 'late' through some additional losses, rather than the current proposal, which works but feels a bit ad-hoc?", "limitations": "The authors discuss a couple limitations in Appendix J that seem minor compared with the points I mentioned above. User control, personalization of styles, and general usefulness of the ARTISAN dataset seem more crucial to the potential impact of the paper.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "- Strengths:\n- New fully-automated method that generates high quality results according to many different metrics and human judgements.\n- New diffusion-based expressive color generation module.\n- New texture-preserving color transfer module.\n- New dataset of high quality photographic color edits, way larger than existing ones.\n- Extensive analysis of the method.\n\n- Weaknesses:\n- The method seems to be almost deterministic for a given input. This poses questions about user control, or what kind of data is required to capture personalized styles.\n- Figure/Table placement could be improved.\n- Previous methods trained on the proposed dataset seem to perform worse, raising questions about its general usefulness.", "was_revised": true, "final_justification": "The discussion with the authors was very insightful. I recommended adding that to the paper. I was already recommending acceptance, and based on this and the other reviews and discussions I’m keeping it.", "rebuttal": "We thank this reviewer for the positive and constructive comments on our work. We address your comments/questions below, and will update our paper accordingly.\n\n> Weakness/Question: The method seems to be almost deterministic... raise questions about user control, and the ability of the method to learn specific styles... a tool that is only able to generate a single type of style without allowing user control feels quite limited.\n> \n\n**Response:** We thank the reviewer for this insightful comment, which touches on the important distinction between automatic and user-guided enhancement.\n\nOur primary focus is on the **fully-automatic task**, a grand challenge in this field, that many previous works (e.g., 3D-LUT, DeepLPF) address. User studies, particularly with **mobile phone users**, consistently show that a powerful, one-click automatic solution is one of the most significant demands. The difficulty lies in creating an algorithm with enough expressive power to replicate expert-level aesthetics without any guidance. Our main contribution is in advancing the state-of-the-art for this specific, highly challenging domain.\n\nWe view **automatic enhancement and user control as largely orthogonal goals.** Our work **presents an effective solution** for the former, providing a high-quality automatic baseline. **This approach**, however, is not at odds with user control; in fact, it can serve as an ideal starting point upon which such features can be built.\n\nAs the reviewer rightly suggests, extending our pipeline to be **equipped with user control** is a valuable direction for future work. For instance, our framework could be conditioned on user-provided examples or fine-tuned for specific styles. We will revise the paper to clarify this distinction and expand on these exciting future possibilities in the limitations section.\n\n> Weakness/Question: Table1 shows methods [28] and [13] perform worse when trained over ARTISAN. Why is that? Could that mean that ARTISAN is more useful for approaches like the one presented in this paper? That would limit the impact of the dataset.\n> \n\n**Response:** This is an excellent question that allows us to clarify the distinct value of ARTISAN. The reviewer's observation is correct, and it stems from a fundamental difference in the learning challenge posed by our dataset compared to previous datasets.\n\n1. **Learning from a Stylistically Consistent, Curated Source:** Earlier datasets like Adobe5K provide a valuable but constrained learning signal. Each of the five experts provides a high-quality but stylistically consistent set of retouched images. For a learning algorithm, the task becomes to model a mapping to one of these few, relatively uniform aesthetic distributions. This is a well-defined problem, and methods like [28] and [13] are well-suited for it. However, the resulting models are inherently tuned to these specific styles, which can limit their general expressiveness.\n2. **Modeling a Vast, 'In-the-Wild' Distribution:** ARTISAN introduces a different and more complex learning paradigm. With over **1.2 million images**, it does not represent a handful of curated styles. Instead, it captures a massive, 'in-the-wild' distribution of what constitutes a high-quality photograph. The learning task is no longer to mimic a specific expert, but to model this incredibly complex distribution, which contains **numerous distinct and equally valid aesthetic styles.** For any given scene, a model must learn to produce a coherent and aesthetically pleasing result from a wide range of possibilities, which requires a significantly higher model capacity.\n\nThis shift in task complexity explains the performance difference. The architectures of prior models are not primarily designed to capture such a broad and varied data space. Their difficulty with ARTISAN is not an indictment of their capabilities, but rather a testament to the new level of challenge presented.\n\n> Weakness/Question: The evolution shown in Figure 14 is quite interesting. Could there be a way to force the model to stabilize in between 'mid' and 'late' through some additional losses, rather than the current proposal, which works but feels a bit ad-hoc?\n> \n\n**Response:** This reviewer proposes to design a new loss function to directly guide the model to an optimal state, rather than blending weights at inference. This is a very interesting suggestion.\n\nOur current weight-blending method is a **pragmatic and effective solution** that we find to be robust and simple to implement. It reliably achieves the desired balance between the aesthetics of the 'mid' stage and the fidelity of the 'late' stage without altering the complex training dynamics. Exploring and properly tuning new loss functions would require a series of lengthy training runs, which unfortunately is not feasible within the limited rebuttal period. However, we believe this is a valuable path for future investigation.\n\nWe will add a brief discussion in **Appendix B.4** to acknowledge this reviewer's suggestion as a promising direction for future research, while justifying our current method as a practical and effective choice.\n\n> Formatting: Some Figures and Tables are quite far from the text that addresses them.\n> \n\n**Response:** We thank this reviewer for the carefully reading of our paper. We will revise the placement of all figures and tables in our revision, to improve the paper's readability."}, {"review_id": "kUETLLlJBN", "reviewer": "Reviewer_mrwF", "summary": "This paper proposes a three-stage approach to image enhancement: (1) perform conditioned generation from a diffusion model to predict an enhanced version of the input image, (2) which is then corrected to preserve texture from the input image, (3) before a final global adjustment performed with standard global filters. Along with the method, authors also introduced a new dataset for image enhancement composed of 1.2M curated public images. By combining such a rich dataset and the presented method, they demonstrate state-of-the-art performance on two standard datasets, i.e. Adobe5K and PPR10K, compared with unsupervised, supervised and generative baselines on diverse metrics.", "questions": "The following questions are related to mentioned weaknesses:\n\n- Q1: Could the authors leverage a diffusion model that does not require a textual prompt within the Color Generation Module? (See W1)\n- Q2: Could the textual preservation module be trained on a dataset generated from outputs of the Color Generation Module? (See W2)\n- Q3: What makes the proposed method more expressive than the human expert? (See W3)", "limitations": "Yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**:\n- S1: The paper is well written and the contributions are properly motivated, with proper reference to previous work and their pros and cons.\n- S2: Leveraging diffusion models for image enhancement is a relevant direction, in particular as authors propose a way to address some of the challenges diffusion models face.\n- S3: Many qualitative examples are presented, and the interactive html page is particularly appreciated.\n\n\n**Weaknesses**:\n- [Major] W1: The Color Generation Module expects a textual prompt as input. To the best of my knowledge, such text is not used (indeed Figure 12 in appendix shows the robustness of the generation to diverse prompts), but is rather a consequence of using Stable Diffusion as a part of the pipeline. Wouldn’t it be possible to have a generation module without textual modality? In addition, maybe such prompt should not be presented as a part of the method in Figure 4 as it is in fact ignored by the model.\n- [Major] W2: The need to generate a dataset to train the texture preservation model is not clear to me. Couldn’t authors train it on outputs from the Color Generation Module instead, as it would allow to train exactly on the distribution of inputs that will be fed to the preservation module at inference time.\n- [Minor] W3: L81-82: the paper explains that the proposed method is more expressive than the human expert. Could authors elaborate a bit more on why this would be the case?", "was_revised": true, "final_justification": "All other reviewers and myself were already suggesting acceptance before the rebuttal period. I raised a few concerns that were properly answered by the authors and am thus updating my rating to \"Accept\", conditioned on the integration of changes and additional discussions promised by authors in their rebuttal.", "rebuttal": "We thank this reviewer for the positive feedback on our paper writing (S1), motivation (S1), and novelty (S2-S3). We address your comments/questions below, and will update our paper accordingly.\n\n> [Major] W1/Q1: The Color Generation Module expects a textual prompt as input... Wouldn’t it be possible to have a generation module without textual modality?\n> \n\n**Response:** We agree that the role of the text prompt was not clearly defined in our paper. Prompted by your question, we have conducted a rigorous ablation study to determine its true impact.\n\n**Our findings confirm your intuition - the text prompt is not a critical component.** The model's performance is driven by the visual conditioning from ControlNet. As shown in the following new ablation results, using a **null text prompt** yields virtually identical performance to using BLIP-generated captions.\n\n| **Method** | **Q-Align↑** | **LAION↑** | **LIQE↑** | **NoR-VDP↑** | **C-VAR↑** |\n| --- | --- | --- | --- | --- | --- |\n| GenColor (w/ Null Text) | **4.29** | **5.83** | **3.76** | 69.15 | **16.98** |\n| GenColor (w/ BLIP) | **4.29** | **5.83** | **3.76** | **69.16** | 16.96 |\n\nThese results demonstrate that the BLIP captioning step adds unnecessary complexity without obvious performance benefits. Therefore, to make our method cleaner and more accurately reflect its core mechanism, we have streamlined our pipeline.\n\n**Action:**\n\n1. We will **revise Figure 4** to remove the BLIP component, simplifying the diagram to show a fully automatic, visually-conditioned pipeline.\n2. We will **update Section 4.2.1** to state that our method uses a null text prompt by default, clarifying that text is not a required input.\n3. The ablation table above will be added to the appendix to fully justify this improved design choice.\n\nWe thank the reviewer again for this valuable suggestion, which has helped us simplify and improve our model as well as the clarity of our paper.\n\n> [Major] W2/Q2: The need to generate a dataset to train the texture preservation model is not clear to me. Couldn’t authors train it on outputs from the Color Generation Module instead?\n> \n\n**Response:** This suggestion to train the texture preservation module using the outputs of our color generation module is interesting. However, this direct approach may not be feasible in our case due to the lack of ground truth, which is why we proposed a self-supervised training strategy to effectively address this limitation.\n\n**1.**    **The Lack of GT Problem:**   \n\nSupervised training of our texture network would require a target image that possesses both the **exact colors** of the diffusion-generated reference and the **pixel-perfect texture** of the original input. Such a ground truth image **does not exist**—it is, in fact, the desired output of the network itself. Without this target, a direct supervised training objective cannot be formulated.\n\n**2.**     **Our Solution: Principled Self-Supervised Training Strategy:**\n\nxiaTo solve this, we designed a self-supervised task that precisely simulates the problem we want the network to solve.\n\n- **Synthetic Data Generation:** We start with a high-quality source image and synthetically degrade it to mimic the key artifacts of a diffusion output. This degradation specifically introduces **texture inconsistencies alongside color shifts and minor spatial misalignments.**\n- **The Training Objective:** The network is then tasked with reversing this specific degradation. It takes the degraded image as input and, **using the original, high-quality image as a texture guide,** learns to reconstruct the original's pristine texture while retaining the color profile of the degraded version.\n\nThis self-supervised strategy is a principled solution, which not only circumvents the \"missing ground truth\" issue but also provides two critical advantages:\n\n- **Generality:** It forces the network to learn the general skill of texture-aware color transfer, rather than overfitting to the specific artifacts of our single generator.\n- **Training Stability:** It provides a fixed, consistent training objective, avoiding the instability of learning from the \"moving target\" of a generator's evolving outputs.\n\nIn short, our self-supervised training strategy is a necessary and robust solution to an otherwise intractable problem.\n\n> [Minor] W3/Q3: L81-82: the paper explains that the proposed method is more expressive than the human expert. Could authors elaborate a bit more on why this would be the case?\n> \n\n**Response:** We thank the reviewer for this excellent question and for pointing us to this specific sentence. You are right to ask for more elaboration here, and we appreciate the opportunity to provide a more detailed explanation.\n\n>\"It demonstrates how our approach achieves more expressive results than Human Expert C—a limitation inherent to the supervised learning methods, which have relied on Adobe5K due to the absence of fine-grained paired datasets...\"\n\nThe comparison in this sentence is indeed very specific. It refers to 'Human Expert C' from the Adobe5K dataset, whose edits serve as a well-known example of a global-only adjustment paradigm. We are happy to elaborate below on why this particular comparison is meaningful for understanding our contribution.\n\n**1. The Inherent Limitation of Global Adjustments in Adobe5K**\n\nThe Adobe5K dataset, while a cornerstone of image enhancement research, primarily features edits created using **global adjustment layers** in Adobe Lightroom. This means that a single set of parameters (e.g., for exposure, contrast, saturation) is applied uniformly across the entire image. This is analogous to applying one filter to the whole photograph.\n\n- **Expert C's Constraint:** \"Expert C\" is one of the five experts who provided such global retouches for the dataset. While their aesthetic judgment is professional, their *method of interaction* with the image is limited to these global tools. They cannot, for instance, brighten a subject's face without also brightening the background behind it.\n\n**2. GenColor's Expressiveness through Content-Aware Local Edits**\n\nGenColor operates on a fundamentally different principle. By leveraging a diffusion model (ControlNet) trained on our large-scale ARTISAN-1M dataset, it learns to perform **spatially-varying, content-aware, and local adjustments**.\n\n- **Learning from Diverse Edits:** The ARTISAN-1M dataset is not limited to global edits. It contains 1.2 million high-quality photographs curated for their professional aesthetic, which often includes sophisticated local adjustments (e.g., dodging and burning, selective color grading, local contrast enhancement).\n- **Semantic Understanding:** The diffusion model learns complex relationships between semantic content and color. This enables GenColor to understand that \"sky\" should be treated differently from \"building\" and \"shadowed grass\" within the same image.\n\nThis allows for a much higher degree of expressiveness. For example, in **Figure 1**, our method:\n\n- **Selectively brightens** the sunlit facade of the building, enhancing its golden tones.\n- **Simultaneously deepens** the blue of the sky to create a rich, vibrant contrast.\n- **Intelligently lightens** the shadowed areas on the lower right without washing out the entire scene.\n\nAn expert limited to global adjustments (like Expert C in the Adobe5K workflow) cannot achieve all these effects simultaneously. Increasing global saturation might make the sky bluer but would also oversaturate the building. Increasing global exposure would brighten the shadows but also blow out the highlights on the facade. GenColor's ability to apply these distinct, targeted edits within a single operation is what makes it fundamentally more expressive in this context.\n\nThis approach is necessitated by a fundamental challenge in the field: collecting paired data for fine-grained, local enhancements (e.g., images with before-and-after local adjustment masks) is prohibitively expensive and difficult to scale. Consequently, no large-scale dataset for supervised local enhancement exists, making it currently infeasible to train a model directly for this task. Our generative approach, trained on unpaired but high-quality images, cleverly circumvents this data bottleneck.\n\n**3. Supporting Evidence from Our Results**\n\nOur quantitative and qualitative results strongly support this claim:\n\n- **Quantitative Metrics (Table 2):** Our method surpasses Expert C on key perceptual metrics like **C-VAR** (Color Variance/Expressiveness), scoring **16.96** vs. Expert C's **10.91**. This metric is specifically designed to measure fine-grained, diverse color transformations, and our superior score quantitatively demonstrates this higher expressiveness.\n- **LLM Preference (Table 2b):** When we asked a vision-language model to choose the most \"visually beautiful\" photo from a color perspective, it preferred GenColor's output over Expert C's **62%** of the time. This suggests that the local, expressive adjustments provided by GenColor lead to more aesthetically pleasing results than the global-only edits.\n- **Visual Evidence (Figure 1):** The difference map for Expert C shows a uniform, global change, whereas the difference map for GenColor reveals highly selective, region-specific adjustments, visually confirming its advanced capability.\n\nIn summary, our claim is not that GenColor is superior to a human expert with an unrestricted toolset (e.g., Photoshop with masking). Rather, it is that GenColor's learned, local, content-aware enhancement capability is fundamentally more expressive and powerful than the global-only adjustment paradigm that defines the \"Expert C\" benchmark in Adobe5K.\n\nWe sincerely appreciate the reviewer's constructive suggestions and believe that the additional experiments, analysis, and explanations help significantly improve the quality of our submission."}, {"review_id": "0xadPfvAzP", "reviewer": "Reviewer_85RF", "summary": "This paper presents GenColor, a diffusion model-based framework for image color enhancement. By reformulating the color enhancement task as a conditional image generation problem and introducing a dedicated texture preservation network, it achieves expert-level fine-tuning under complex lighting and content conditions while maintaining pixel-perfect texture fidelity. The authors also constructed ARTISAN-1M, a high-quality image enhancement dataset containing 1.2 million samples, and conducted both objective and subjective evaluations across multiple benchmark datasets.", "questions": "- Regarding the weight mixing strategy, would it be possible to simply select an intermediate weight between the Mid and Late stages to achieve a comparable effect? In addition, please clarify how the mixing ratios are allocated in the current weight mixing strategy.\n\n- It is also recommended to include an ablation study where the generative module is removed, retaining only the texture preservation module and the global filter, in order to further validate the contribution of the generative module to the overall performance.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths\n- The methodology is well-designed, innovatively proposing a color enhancement framework integrating diffusion models and a texture-preserving color transfer network, which effectively improves image quality.\n- By simulating degradation processes to construct synthetic training data, the approach not only reduces reliance on real-world data but also significantly enhances the model’s generalization capability.\n- A large-scale, high-quality dataset, ARTISAN, was constructed, providing solid data support for training high-performance color enhancement models.\n- Comprehensive comparative experimental analyses are provided in the appendix and supplementary materials, demonstrating that the proposed method achieves superior performance across multiple benchmark datasets, exhibiting significant advantages in both objective evaluation metrics and subjective visual quality.\n\nWeakness\n- Many of the existing comparison methods include lightweight models; however, the method proposed in this paper still has a relatively long runtime, which is one of its main drawbacks.", "was_revised": true, "final_justification": "The rebuttal has addressed my concerns. I maintain my rating as \"Accept\" and look forward to the authors releasing the dataset and relevant code.", "rebuttal": "We thank this reviewer for the positive feedback and for highlighting the different strengths of our work. We address your comments/questions below, and will update our paper accordingly.\n\n> Weakness: the method proposed in this paper still has a relatively long runtime\n> \n\n**Response:** We agree that the inference speed is a crucial consideration for real-world applications. The computational cost of GenColor's three-stage pipeline is dominated by Phase 1, the iterative denoising process in our diffusion-based Color Generation Module. Our other modules—Phase 2 (Texture Preservation) and Phase 3 (Global Adjustment)—are single-pass networks and are generally very efficient.\n\nTherefore, to speed up the overall runtime, we focus our analysis on the primary bottleneck. Prompted by your question, we have conducted a detailed analysis of the trade-off between the number of denoising steps in Phase 1 and the final enhancement quality. The full results on the Adobe5K dataset are presented below.\n\n| **Denoising Steps** | **Q-Align↑** | **LAION↑** | **LIQE↑** | **NoR-VDP↑** |\n| --- | --- | --- | --- | --- |\n| 1 | 2.75 | 4.86 | 1.91 | 68.75 |\n| 2 | 2.84 | 4.92 | 2.01 | 68.95 |\n| 3 | 3.88 | 5.42 | 3.30 | 69.09 |\n| 5 | 4.17 | 5.67 | 3.61 | **69.20** |\n| 10 | 4.28 | 5.80 | 3.74 | 69.19 |\n| **15** | **4.29** | 5.82 | 3.75 | 69.18 |\n| 20 | **4.29** | **5.83** | **3.76** | 69.15 |\n| 25 | **4.29** | **5.83** | **3.76** | 69.12 |\n| 30 (Our Paper) | **4.29** | **5.83** | **3.76** | 69.16 |\n\n**Key Finding:** Our analysis shows that while the performance is low with very few steps (1-5), the quality rapidly converges. We can reduce the number of denoising steps from **30 down to 15**—effectively **halving the runtime** of our most computationally intensive module—with **no obvious degradation in quality** across all key metrics. Hence, the performance at 15 steps is virtually identical to that at 30 steps. ****This offers a favorable speed-quality trade-off, making the method more practical for real-world applications.   \n\n**Future Work and Broader Context:**\nBeyond this immediate optimization, the efficiency of GenColor can be further improved by incorporating techniques from the rapidly advancing field of fast diffusion sampling. Methods such as knowledge distillation (e.g., Progressive Distillation) or consistency models could potentially reduce the step count to as few as 1-4 steps while maintaining high quality, and we consider this as a promising direction for future work.\n\n> Question: Regarding the weight mixing strategy, would it be possible to simply select an intermediate weight between the Mid and Late stages to achieve a comparable effect? In addition, please clarify how the mixing ratios are allocated in the current weight mixing strategy.\n> \n\nOur weight mixing strategy is a key component for achieving optimal results, and its design is based on empirical evidence from comparing both single checkpoints and blended weights.\n\nWe have conducted an ablation study to analyze the effect of different model checkpoints and their combinations. This includes the \"mid\" stage (high expressiveness), the \"late\" stage (high fidelity), a single checkpoint taken from between these two stages (\"mid-late\"), and our proposed 50/50 blend.\n\nThe results below demonstrate that a **50/50 blend of the 'mid' and 'late' checkpoints significantly outperforms any single checkpoint**, including the intermediate one.\n\n| **Mid Weight** | **Mid-Late Weight** | **Late Weight** | **Q-Align↑** | **LAION↑** | **LIQE↑** | **NoR-VDP↑** | **C-VAR↑** |\n| --- | --- | --- | --- | --- | --- | --- | --- |\n| 1.0 | 0.0 | 0.0 | 4.08 | 5.66 | 3.54 | 68.96 | **22.91** |\n| 0.0 | 1.0 | 0.0 | 4.24 | 5.77 | 3.72 | 68.87 | 17.87 |\n| 0.0 | 0.0 | 1.0 | 4.25 | 5.77 | 3.73 | 69.09 | 16.69 |\n| **0.5** | **0.0** | **0.5** | **4.29** | **5.83** | **3.76** | **69.16** | 16.96 |\n\n**Key Finding:** This data directly answers both parts of the question.\n\n1. **Intermediate Checkpoint vs. Blend:** Simply selecting a single intermediate checkpoint (\"Mid-Late Weight\") is **suboptimal** compared to our blending strategy across all key quality metrics (e.g., Q-Align 4.24 vs 4.29).\n2. **Allocation and Optimality:** Our final method uses a **50/50 blend** (Mid Weight = 0.5, Late Weight = 0.5). This configuration achieves the **highest scores on all four perceptual quality metrics (Q-Align, LAION, LIQE, NoR-VDP)** while maintaining a strong C-VAR score. This makes the 50/50 blend a clear, data-driven choice for balancing aesthetic expressiveness with photorealistic quality.\n\n> Question: It is also recommended to include an ablation study where the generative module is removed...\n> \n\n**Response:** We agree that understanding the contribution of the generative module is essential. In fact, this experiment was included in our comprehensive ablation studies presented in **Tables 14, 15, and 16** and visualized in **Figure 19**.\n\nSpecifically, a pipeline without the Color Generation module (`C`) would not have a color reference to transfer, making the Texture Preservation module (`T`) inapplicable. Therefore, the pipeline effectively reduces to just the final **Global Filter module (`G`)**. The configuration labeled \"**G**\" (first row) in our ablation tables represents exactly this scenario. As the results show, removing the generative module leads to a dramatic drop in performance across all key metrics, especially expressiveness. For instance, in Table 14 (Adobe5K), the C-VAR score plummets from **16.96** (full model) to **10.35** (Global Filter only). This quantitatively confirms the critical and substantial contribution of our generative module.\n\n> Formatting: Line 72： to to -> to\n> \n\n**Response:** We thank this reviewer for the careful reading of our paper. We will correct this typo in our revision."}]

Author rebuttal:

Meta-review: This paper introduces GenColor, a three-stage diffusion-based framework for expressive and texture-preserving color enhancement, trained on a new 1.2M-image dataset (ARTISAN-1M). The approach integrates a diffusion-based generation module, a texture-preserving transfer network, and a global adjustment step, achieving state-of-the-art results on standard benchmarks and producing outputs comparable to expert edits. Code, dataset, and supplementary demos are promised to be released.
The paper was reviewed by four reviewers who recommended its acceptance.  

Strengths: High-quality results, innovative methodology combining diffusion with texture transfer, valuable dataset contribution, and thorough evaluation (metrics, user studies, ablations). The paper is clearly written with transparent limitations.

Weaknesses: Limited user control/personalization, questions about ARTISAN’s generality, reliance on unused text prompts, and an ad hoc global adjustment stage. Runtime is longer than lightweight baselines.

Rebuttal: Strongly addressed runtime (showed quality maintained with fewer diffusion steps), clarified weight mixing superiority, and confirmed necessity of the generative module via ablations. Broader concerns (personalization, dataset generality) remain but are secondary.

Recommendation: Spotlight accept . It is agreed by the reviewers and myself that this is a technically strong paper with broad interest and practical impact.
Decision: Accept (spotlight)