Title: GenColor: Generative and Expressive Color Enhancement with Pixel-Perfect Texture Preservation
Venue: NeurIPS 2025
Accepted: yes
Keywords: color enhancement
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\n- 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-17"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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?\n\nQUESTIONS:\nThe 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)\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n- 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.\n\nQUESTIONS:\nRelated 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?\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

comments: 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.

STRENGTHS AND WEAKNESSES:
Strenghts:

1. Very good resulting quality
2. Good methodology for result evaluation, it is important to use user studies for such type of tasks
3. Main SotA methods have been used for comparisson
4. New dataset is prepared

Mainly 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

QUESTIONS:
1. Is is possible to speed up proposed solution? 
2. Have you considered pair-wise image comparissons for more robust result?

LIMITATIONS:
All the limitation are described in a clear manner
TITLE: Official Review
CLARITY: 4
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-05