Learning Adaptive Lighting via Channel-Aware Guidance
Authors: Qirui Yang
Peng-Tao Jiang
Hao Zhang
Jinwei Chen
Bo Li
Huanjing Yue
Jingyu Yang
Venue: ICML 2025 poster (2025)
Keywords: Exposure Correction
Retouching
Image Enhancement
Primary area: applications->computer_vision

Abstract: Learning lighting adaptation is a crucial step in achieving good visual perception and supporting downstream vision tasks. Current research often addresses individual light-related challenges, such as high dynamic range imaging and exposure correction, in isolation. However, we identify shared fundamental properties across these tasks:
i) different color channels have different light properties, and ii) the channel differences reflected in the spatial and frequency domains are different. Leveraging these insights, we introduce the channel-aware Learning Adaptive Lighting Network (LALNet), a multi-task framework designed to handle multiple light-related tasks efficiently. Specifically, LALNet incorporates color-separated features that highlight the unique light properties of each color channel, integrated with traditional color-mixed features by Light Guided Attention (LGA). The LGA utilizes color-separated features to guide color-mixed features focusing on channel differences and ensuring visual consistency across all channels. Additionally, LALNet employs dual domain channel modulation for generating color-separated features and a mixed channel modulation and light state space module for producing color-mixed features. Extensive experiments on four representative light-related tasks demonstrate that LALNet significantly outperforms state-of-the-art methods on benchmark tests and requires fewer computational resources.  We provide an anonymous online demo at [LALNet](https://xxxxxx2025.github.io/LALNet/).

Learning Adaptive Lighting via Channel-Aware Guidance
Qirui Yang 12
Peng-Tat Jiang" Hao Zhang?
Jinwei Chen 2 Bo Li Huanjing Yue
Jingyu Yang'
Abstract
Exposure Corractionin SCIT
Image Retourhing HDR+ HDR++
48
FECNet
31
61 CoTF
Learning lighting adaptation is crucial step in achieving good visual perception and supporting downstream vision tasks. Current research of- ten addresses individual light-related challenges, such as high dynamic range imaging and expo- sure correction, in isolation. However, we iden- tify shared fundamental properties across these tasks: i) different color channels have different light properties, and ii) the channel differences reflected in the spatial and frequency domains are different. Leveraging these insights, we intro- duce the channel-aware Learning Adaptive Light- ing Network (LALNet), a multi-task framework designed to handle multiple light-related tasks efficiently. Specifically, LALNet incorporates color-separated features that highlight the unique light properties of each color channel, integrated with traditional color-mixed features by Light Guided Attention (LGA). The LGA utilizes color- separated features to guide color mixed features focusing on channel differences and ensuring vi- sual consistency across all channels. Additionally, LALNet employs dual domain channel modula- tion for generating color-separated features and a mixed channel modulation and light state space module for producing color-mixed features. Ex- tensive experiments on four representative light- related tasks demonstrate that LALNet signifi- cantly outperforms state-of-the-ar methods on benchmark tests and requires fewer computational resources. We provide online demo LALNet.
#2 FOCN
21.0
30
SLUT
1024
#5 C2lF
Retmasformm
23.0
#6 Retinedoonnss
#1 RettneeMaanta
29 #1 L.FUUT
45 Mambbll
28
22.5
27
2000
20.00
2262
21.5
2153
25
24
20:08
#1
02
#3
24
45
Ours
01.
#2
43
04
85
Ours
Low- Light Enbercement LOL.
Tone Mapging in HDRI Haven
26
CoTF
StPLUT
N01 LANet
$5.6
32
#2 OrT
45 Rastomer
1.23
#3 Restomer
24
46 RetinesManneee
DiftR
24.03
30
#5 Ratincn/Mamioo
Retinesdoumer
3,86
23
28
22
23073
26
21
20/0
24
Figure 1. Our LALNet significantly outperforms state-of-the-art methods on four representative benchmark tests of light-related image enhancement, including image retouching, tone mapping, low-light enhancement, and exposure correction.
images taken under poor lighting conditions often exhibit degraded quality, which not only affects visual presentation but also poses challenges for tasks such as object detection and tracking. Consequently. learning adaptive lighting has emerged as a pivotal step in achieving robust visual percep- tion and supporting downstream vision tasks. This process is analogous to the perception of the human visual system, that is, light adaptation, which enables visual perception across diverse lighting environments.
maintain stable
1. Introduction
Many tasks in computer vision aim to achieve light adapta- tion, including exposure correction (Li et al.. 2024a; Huang et al., 2023), image retouching (He et al., 2020; Zhang et al., 2024), low-light enhancement (Cai et al., 2023; Yi et al.,
Photography is the art of light. The quality of image is crucial for effective visual presentation and robust per- formance in subsequent computer vision tasks. However,
2023), and tone mapping (Cao et al., 2023; Yang al.,
'qqual contribution 'Tianjin University. Tianjin, China. Rvivo Mobile Communication Co., Ltd, Hangzhou, China. Correspon- dence to: Peng-Tac Tao Jiang <pt.jiang @ civo.com>, Jingyu Yang <yjy@rju.edu.cn>.
2022). The common goal of these light-related tasks is to adjust the light level of the scene to the perceptually optimal level, thereby revealing more visual details. However, due to the different characteristies of these light-related tasks, most of the current methods (Zeng et al., 2020; Li et al., 2024a) are designed to deal with the above tasks individu- ally and are difficult to apply to other light-related tasks. For
Proceedings of the 42nd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

---

Learning Adaptive Lighting via Channcl-Aware Guidance
20030
13020
Blue
Low-Light Low-Light
5000
Lowfree
HighFreq
2.0
Green Blue
15
Retouching
0.5
0.0
HighFreq
sRGB
R-channel
G-channel
B-channel
Eneryy Distribution
Figure 2. Motivation of LALNet. Different color channel differences and statistical DWT spectral energy distributions for different tasks.
example, exposure correction (Huang et al., 2022b; Zhang et al., 2019b) must adjust the brightness of both underex- posed and overexposed scenes to achieve clearer images; image retouching (Wang al., 2023; Su et al., 2024) aims to enhance the aesthetic visual quality of images affected by light defects, often requiring special attention to global light; low-light enhancement (Wang et al., 2022; Liu et al., 2021a) reveals more details by boosting the brightness of dark areas, but requires special processing of noise; and tone mapping (Zhang et al., 2022; Wang al., 2021) preserves rich details by compressing high dynamic range light low dynamic range, focusing more adaptation to high dynamic range light. The different characteristics of these tasks make existing methods inconsistent in performance multiple tasks. Although some works (Yang et al., 2023a) have attempted to perform light-related tasks with uni- fied architecture, the insufficient analysis of light-related task specificity has resulted in unsatisfactory performance compared methods designed for these individual tasks.
ties of different channels differ significantly and that there no fixed pattern between the different images. For example, for the first image, the G-channel exhibits a more balanced luminance distribution, while for the second image, the R- channel performs better in this regard. On the other hand, the frequency domain exhibits channel differences that are different from the spatial domain. For example, in the first image, the G-channel is brighter, but the G-channel does not have the highest energy distribution in the frequency domain. This illustrates that capturing channel differences in the spatial and frequency domains is different. Channel differences cannot be fully characterized in the spatial fre- quency domains alone. Moreover, it is well known that the specific attributes (Yang et al., 2023a; Zhang et al., 2024) of light-related tasks are mainly embodied in the low-frequency components, whereas the details of the contents are more related to the high-frequency components. These findings highlight the importance of learning adaptive lighting by leveraging distinetive features of different color channels in the spatial and frequency domains.
Interestingly, can framework be designed to handle these light-related tasks, just as the human visual system can adapt to a variety of lighting environments? Motivated by this question, we aim to design a framework capable of handling multiple light enhancement tasks separately.
Motivated by the above light properties, we propose learnable adaptive lighting network, namely LALNet. Our method leverages the potential channel light differences to guide effective adaptive lighting. We decompose the light adaptation problem into two sub-tasks: light adaptation, which addresses light variations under different light con- ditions, and (ii) detail enhancement, which preserves and refines image details while performing adaptive lighting. LALNet begins to learn adaptive light enhancement from the down-samplle version of the input image, optimizing for low computational complexity. To implement light adap- tation, we propose a dual-branch architecture comprising channel separation and channel mixing. The channel separa- tion branch employs the Dual Domain Channel Modulation module to extract color-separated features, focusing light differences and color-specific luminance distributions for each channel in the spatial and frequency domains. In the channel mixing branch, we apply Mixed Channel Modula- tion and Light State Space Module integrate color-mixed
To this end, delve deep into analyzing the common light properties of these light-related tasks and utilize them to inspire the design of multi-task framework. We observe two key insights from light-related tasks: i) differrntt color channels have different light properties; ii) the chan- nel differences reflected in the spatial and frequency domains are different. To analyze these differences, we employ the Discrete Wavelet Transform (Shensa al., 1992) to decompose the input image into low-frequency and high- frequency components, and statistics the energy distri- bution of the R/G/B channels based on the square of the pixel values separately. Fig. illustrates the color channel attributes two light-related task images the spatial and frequency domains. be observed that the light proper-
2

---

Learning Adaptive Lighting via Channel-Aware Guidance
lighting information, capturing inter-channel relationships and lighting patterns that achieve harmonious light enhance- ment. A key component of our framework is Light Guided Attention (LGA), which utilizee color-separated features to guide color-mixed light information for adaptive light- ing. This mechanism enhances the network's capability to perceive changes in channel luminance differences and en- sure visual consistency and color balance across channels. Consequently, our network is effectively adaptive to light variations while attending to feature differences across chan- nels. Finally, we employ an iterative detail enhancement strategy to recover the image resolution level by level while enhancing the details. We conduct comprehensive experi- ments and demonstrate the state-of-the-ar performance of our LALNet on four light-related tasks, as shown in Fig. 1. Our contributions can be summarized as follows:
offer efficient inference by learning compact representa- tions. CSRNet (He et al., 2020) leverages conditional ML.P for adaptive enhancement, and GAN-based methods (Chen et al., 2018; Ni et al., 2020) enable unpaired learning, though often at the cost of interpretability and training stability.
Tone Mapping, Recent advancements in tone mapping have leveraged deep learning methods (Zhang et al., 2022: Yang et al., 2022: Zhang et al., 2024; Hu et al., 2022) to address the nonlinear mapping from HDR to LDR images. Hou et al. (Hou et al., 2017) applied CNNs to tone mapping tasks, establishing foundation for subsequent research. Later works explored GANS for pixel-level accuracy (Cao et al., 2020; Rana et al., 2020; Panetta et al., 2021). Despite these advancements, issues such as halo artifacts and local incon- sistencies persist. Join(TM (Hu et al., 2022) combined tone mapping and denoising using discrete cosine transforms, while HSVNet (Zhang et al., 2019a) leveraged HSV color space manipulation to reduce halos and enhance detail re- tention. Despite notable progress, existing methods often struggle to balance global and local tone mapping, resulting in unsatisfactory results in other tasks.
We propose a multi-task light adaptation framework inspired by the common light property, namely the Learning Adaptive Lighting Network (LALNet).
We introduce the Dual Domain Channel Modulation to capture the light differences of different color channels and combine them with the traditional color-mixed features with Light Guided Attention.
Low- Image Enhancement. Recent advances in low- light enhancement are largely driven by deep learning (Yang et al., 2023b; Liu et al., 2021 b; Yang et al., 2025). Models like DeepUPE (Wang et al., 2019b) and Retinexformer (Cai et al., 2023) build on Retinex theory for illumination de- composition. Hybrid architectures such as SNRNet (Xu et al., 2022) and Restormer (Zamir et al., 2022) incor- porate Transformer designs for long-range dependencies. RetinexMamba (Bai al., 2024) introduces the State Space Model to improve efficiency. However, Retinex-based meth- ods (Cai et al., 2023; Liu et al.. 2021a; Bai et al., 2024) are based on the theory of separated illumination and reflec- tion, but they usually assume smooth and uniform lighting conditions, which may not hold in realistic scenes involv- ing complex lighting variations. Moreover, these methods typically work in luminance reflection space, where high- frequency details may be distorted during decomposition.
Extensive experiments on four representative light- related tasks show that LALNet significantly outper- forms state-of-the methods in benchmarking and that LALNet requires fewer computational resources.
2. Related Work
Exposure Correction. Exposure correction aims to balance image brightness under varying lighting conditions (Yang Nsampi et al., 2021; Huang et al., 2022b; Li et al., 2024b). Early methods like RetinexNet (Liu et al., 2021a) follow the Retinex theory to separately process il- lumination and reflectance. ZeroDCE (Guo et al., 2020) estimates pixel-wise curves without reference images. How- ever, these approaches mainly address underexposure and struggle with diverse real-world scenarios. LPNett (Afifi al., 2021) and FourierNet (Huang et al., 2022h) introduce multi-scale and frequency-aware designs for broader expo- sure handling. Recently, COTF (Li et al., 2024b) proposed collaborative framework for real-time correction, effectively integrating global and pixel-level adjustments.
et al., 2020;
3. Methods
3.1. Motivation
Previous studies (Cai et al., 2023; Li et al., 2024a; Zhang et al., 2024; Su et al., 2024) for light-related tasks, such as tone mapping and low-light enhancement, are often tai- lored to individual tasks, leading to suboptimal performance across multiple scenarios. These frameworks typically fail to account for the common properties shared across differ- ent lighting-related tasks, which limits their generalizability. As a result, many frameworks are either overly specialized or inefficient when faced with multiple tasks. This leads to performance inconsistencies, especially when frameworks designed for specific tasks are applied others. For in-
Image Retouching. Image retouching focuses on restoring natural luminance and color distributions in more per- ceptually faithful manner (Moran et al., 2020; Liang al., 2021a; Gao & Wu, 2021). Some approaches reformulate the problem as curve estimation (Kim et al., 2020; Li et al., 2020), while others like DeepLPF (Moran et al., 2020) opti- mize spatially adaptive filters for fine control. Lookup table (LUdT)-based models (Zeng et al., 2020; Liang et al., 2021a)
3

---

Learning Adaptive Lighting via Channel-Aware Guidance
xi+
Color-aperteel
Xir
IDE
Fix
IDE
Cha Awiit Embbedding
LGA
LGA
LGA
Diffewriial Pytarid
Fm
Pom
IDE
Coloe-ixxd
Cm
Pla
Pia
Pia
Goory
Group Comvolution
MCM
LSSM
Light State Space Module
DDCM Dual Domai Chammel Modiiation
MCM
Mixed Charnal Modilation
LGA
Light OdickoAAtttrtiic
IDE Iterative Detail Enhenvement
Figure 3. Architecture of LeLNet for light adaptation. The core modules of LALNet are: (a) dual domain channel modulation (DDCM) that extracts color-sep separrsee features, focusing on light differences each channel in spatial and frequency domains, and (b) light guided attention (LGA) utilizes color-separated features to guide color-mixed light information for light adaptation.
stance, Retincxformer focuses on separating reflection and illumination to enhance low-light images, but its underlying Retinex theory is inapplicable to tasks such as tone mapping and image retouching. This limitation evident scenarios where low-light enhancement methods struggle to maintain color fidelity during tone mapping.
modulation (MCM) combined with the light state space module (LSSM) to extract color-mixed feature F F.m- promot- ing cross chossechannel interaction and achieving balanced light enhancement. This can be expressed mathematically as:
F Fes = GConv(DDDM(X22.))
(I)
Our motivation is rooted in the observation that, despite the diverse nature of light-related tasks, there are key shared properties: distinct light properties across color chan- nels and channel differences in spatial and frequency domains. These channel differences manifest differently in both the spatial and frequency domains, further complicat- ing the task of adaptive lighting. To address these issues, aim to design multi-task framework that adapts to different lighting conditions more effectively than previous frame- works that focus on individual tasks. By analyzing these shared light properties across multiple tasks, our framework seeks capture the subtle differences between color chan- nels and ensure consistent and balanced visual outcomes across various lighting conditions.
F cm = LSSM(MCM(X))).F..
(2)
To emphasize the light differences in different channels, we introduce Light Guided Attention, which injects the color- separated features into color-mixed features to obtain the light adaptive feature Fie. which is described as:
Fa = LGA(FeFF cm Fos).
33)
This process ensures consistent and uniform light adapta- tion across the entire image and eliminates color distortion caused by channel crosstalk. Finally. we integrate the low- and high-frequency via learnable differential pyramid (Yang et al., 2024) and iterative detail enhancement, progressively refining image resolution and enhancing fine details.
3.2. Framework Overview
3.3. Light Adaptation
The overall pipeline of LALNet is illustrated in Fig. 3. LAL- Net is composed of two components: light adaptation and detail enhancement. Given low-quality (LQ) input image X our goal is to generate high-quality (HQ) output Y with optimal light. We begin to learn adaptive light from the down-sampled version of the input image X optimizing for low computational complexity. Subsequently, we em- ploy the two-branch structure for extracting light features, containing color separation and color mixing branches. The channel separation branch employs the DDCM and group convolution to extract color-separated feature Fes. focusing on light differences and color-specific luminance distribu- tions for each channel the spatial and frequency domains. In the channel mixing branch, we utilize mixed channel
In the literature, generally utilize the traditional convo- lutions to convolve with all channels for light-related tasks, generating (GB-mixed features. This operator can cap- ture the interaction information and shared features among channels. However, this also amplifies the luminance non- uniformity and noise existing the three channels. Notably, for light-related tasks, we have observed that characteris- tic differences between the RGB channels and the spatial and frequency domains exhibit different differences. There is also no consistent pattern across images. As shown in Fig. 2, the three channels exhibit distinct differences in lumi- nance, with one channel usually being closer to the ground truth. If we only utilize volor-mixcd features to adapt to light, the negative interference between channels will also
4

---

Learning Adaptive Lighting via Channel-Aware Guidance
spread to all channels. Therefore, we introduce an additional branch that extracts channel-separated features alongside the channel-mixed features. Channel-mixed features are respon- sible for capturing mixed luminance and color information, while channel-separated features guide the network to focus on channel differences. This design prompts the network to adapt to light while attending to differences across channels.
et al., 2023a), inspired by (Finder al., 2024), we employ wavelet transform to achieve channel-mixed features Fam- The process begins with the extraction of small-scale fea- tures using small convolutional kernel to capture local in- formation. These features are then passed through a wavelet transform (WT), where the generated large-scale features modulate the small-scale features, enabling the network to better integrate global light representation. The process can be represented as follows:
3.3. COLOR SEPARATION REPRESENTATION
Based on the analysis in Sec. 1, the spatial and frequency domains reflect different channel differences. Therefore, we design DDCM to capture the color-separated features.
cA, cH, cV, cD (T(Convaxs(X))
where cA, cH, cV, cD represent the components of the 2D wavelet transform. Afterward, the modulated features are concatenated and further passed the convolutional layer.
Dual Domain Channel Modulation. To avoid cross- channel interference between operating channels, we pro- cess each channel independently in the frequency and spatial domains and introduce learnable parameters to modulate the channels. After frequency domain processing, the images inverted back to the spatial domain. Then, to comple- ment the color-separated feature representation, we utilize channel attention to capture the separasee features in the spatial domain. Specifically, given an input image X, each channel of the image is denoted as X, (i = 3,). We perform a 2D fast Fourier Transform (FFT) for X, to obtain the frequency domain representation:
F cm = Convaxa(Concat(C) cH, evvcD)) (10)
To enhance the network's capability to capture global light information, we introduce the Light State Space Mod- ule (LSSM), which supplements mixed-channel modula- tion. The LSSM is designed to efficiently capture long- range dependencies with lower computational overhead than transformer-based methods. For feature integration and expansion, LSSM begins by integrating channel- mixed features with channel-separated features. This integrated feature is then expanded to a dimensionality of 2C via a linear layer. Following this expansion, the feature is divided into two distinct components. F1 and F2, according to the channel dimensions. Therefore, the channel-separater fea- ture F1 along with the newly formed F1 and Fz, serve as inputs to three parallel processing streams. First Stream: Feature F1 undergoes an initial expansion to n/C' channels through a linear layer, followed by depth-wise convolution, SiLU, 2D selective scanning (SS2D) (Guo et al., 2024), and LayerNorm. This sequence refines the representation of F,, emphasizing its spatial and channel-wise character- istics. Second Stream: Feature F's is processed directly using SS2D, capturing comprehensive global context with- out additional transformations. Third Stream: Feature F, is subjected only to SiLU, preserving its original characteris- tics while enabling non-linear transformations that enrich representation. Subsequently, the global information extracted from the first two streams is fused. This fused information is then multiplied with the output of the third stream. By doing so, the LSSM effectively integrates de- tailed local and global light patterns, enhancing the overall sensitivity of the network to varying lighting conditions. The whole process can be represented as follows:
S.(u,)) (u, = (X,),
(4)
where u, = (u, r) + [,, r). R. v) and I, (u, v) denote the real and imaginary parts, respectively. Then, we perform convolution operations on the R,(u, v) and I, (u, v), respectively:
(R, v) = Wn. R&(u, v), [.(,,)) = W., (L,, v).
(5) where WR. and W,, I, are the convolution kernels, * denote convolution operation. Subsequently, we reorganize the decoupled real and imaginary parts into frequency-doman signals, and perform the Inverse Fourier Transform to obtain the decoupled tine-domain information as follows:
St (u, v) = R((,,,) v) + [((,,)),
(6)
X = - FF
= ISF)
(7)
Finally, after concatenating channels, we capture the sepa- rated features of the image in the spatial domain through the channel attention module to further enhance the color- separated feature representation.
F = CABB(onnau(X,, X.)).
(8)
F1, F2 = Chunk F + F.))).
(11)
3.3.2. COLOR MIXING REPRESENTATION
F SS2D (SLD(())L SS2D(( (12)
F SIL,(F2), F'. MLP(LN((F)) F2)),, (13)
In parallel, we introduce mixed channel modulation for ex- tracting chiedel-mixed features. Since light patterns often exhibit global characteristics (Rieke& & Rudd, 2009; Yang
where Linear(-) denote linear projection. X denotes the Hadamard product.
5

---

Learning Adaptive Lighting via Channel-Aware Guidance
3.3.3. LIGHT GUIDED ATTENTION
the light-adaption image YL Specifically, for the 4y pyra- mid, first up-sample the low-frequency image Y and concatenate it with the HF component X then feed it into residual network to predict a refinement mask M This mask allows pixel-by-pixed refinement of the HF com- ponent, which is subsequently added to the up-sampling Li to generate the reconstructed result of the current layer L
Although LSSM performs well in capturing long-range de- pendencies, it still faces problems such as local information forgetting and channel redundancy. Moreover, color mixed features ignore the feature differences between different channels, treating them equally in the network. However, in light-related tasks, we have observed significant differences between color channels, with no consistent pattern across images. These differences are crucial for adaptive lighting. For this reason, we propose to inject color-separated fea- tures into color-mixed features by light guided attention to perceive channel differences.
The process at the 4h pyramid is formulated as:
M (tesCConncttco Coniicat(U(YYiii LF), X
(17)
Y ((p(YL)) +
HF HF M
(18)
where Res(-) and Up(+) denote the residual block and up- sampling, respectively.
Specifically, for the first LGA module, we input the channel- mixed features F1 cm from LSSM and the channel-separater features F. from group convolution into the LGA. Subse- 1 convo-
4. Experiments
quently, the input F is processed through lution followed by depthwise convolution, producing K and V tensors with doubled the number of channels. This can be expressed mathematically as:
4.1. Experimental settings
Datasets. We evaluate our method on four representative light-related tasks: exposure correction (SCIE (Cai et al., 2018)), image retouching (HDR+ Burst Photography (Hasi- noff et al., 2016)), low-light enhancement (LOL dataset (Wei et al., 2018),, and tone mapping (HDRI Haven (Yang et al., 2024). Following the settings of (Huang et al., 2022a) for SICE, it contains 1000 training images and 24 test images. The HDR+ dataset is a staple for image retouching, espe- cially in mobile photography. We utilize 675 image sets for training and 248 for testing. The LOL dataset (Wei et al., 2018) contains 500 image pairs in total, with 485 pairs used for training and 15 test images. The HDRI Haven dataset is new benchmark for evaluating tone mapping (Su et al., 2021; Cao et al., 2023), which includes 570 HDR images of diverse scenes under various light conditions. We select 456 image sets for training and 114 for testing.
K, V = Convax Contsxx(Convi.aa cm
(14)
The query Q is then generated from the channcl-separated features F!.-
Q Convaxa(Coovvaa (CCoovixxGCCoos/// (GConvax3 (F..).
(15)
We compute the attention weights by the dot product be- tween Q and K, normalized by the softmax function, and multiplied by V obtain the updated features:
QK
Altention (erlion(Q,, K2 V)- = softmax
X XV),, (16)
Vax
where dx is the dimension of K and 7 denotes the scaling factor. can be remarked that we utilize channel-separated features as Q vectors to motivate the model to focus on channel differences. In summary, the design of LGA hances the adaptive representation of image features in both spatial and channel dimensions and improves the network's ability to capture dependencies between image channels. After LGA processing, we can obtain the low-resolution tight-adaption output - Subsequently, we utilize the iter- ative detail enhancement strategy to enhance the detail of Y Le. which is introduced in the following.
Implementation details. We implement our model with Pytorch on the NVIDIA L40s GPU platform. The model is trained with the Adam optimizer (31 0.9, 33 0.999) for 4 x 10 iterations. The learning rate is initially set to 1 x 10-4, We adopt traditional PSNR and SSIM metrics on the RGB channel to evaluate the reconstruction accuracy. We also employ TMQI (Yeganeh & Wang, 2013), LPIPS (Zhang et al., 2018), and CIELAB color space (Zhang et al., 1996) to evaluate image quality and perceptual quality, respectively.
3.4. Detail Enhancement
4.2. Comparison with State-of-the-Arts
To achieve faithful reconstruction, we apply a learnable differential pyramid (LDP) (Yang et al., 2024) to capture high-frequency details. Through LDP, obtain the multi- scale high-freyuency features XHF [XD
Quantitative comparison. The performance of the pro- posed multi-task framework is evaluated on four light- related image enhancement tasks, namely, (1) exposure correction, (2) image retouching, (3) low-light enhance- ment, and (4) tone mapping. We quantitatively compare the proposed method with a wide range of state-of-the- light-related methods in Tab. 1, Tab. 2, Tab. 3, and Tab. 4. For exposure correction, as shown in Tab. 1, our method
x(=1), W tapering resolutions from H x W to प x 2 L de- notes the number of pyramid levels (L=3 in our framework). Using the high- feeefrryeency information Xhr captured, we em- ploy an iterative detail enhancement to progressively refine
H
6

---

Learning Adaptive Lighting via Channel-Aware Guidance
Table Quantitative results exposure correction methods on the SCTE dataset. "f" denotes the unavailable source code. Metrics with and denote higher better and lower better. The best and second results are in red and blue, respectively.
Exposure Correction SCIE
Method
Under PSNR1
Over
Average LPIPS]
SSIM1
PSNR1
SSIM
PSNR
SSIM
NIQEL
MUSIQ1
URt (Wu et al., 2022)
17.39
0.6448
7.40
0.4543
12.40
0.5496
0.3549
12.78
49.11
DRBN (Yang et al., 2020)
17.96
0.6767
17.33
0.6828
17.65
0.6798
0.3891
12.06
48.77
SID (Chen et ul., 2018)
19.51
0.6635
16.79
0.6444
18.15
0.6540
0.2417
11.79
51.07
CSRNet (He et al., 2020)
21.43
0.6789
20.13
0.7250
20.78
0.7019
0.1390
10.59
61.79
MSEC (Afifi et al., 2021)
19.62
0,6512
17.59
0.6560
18.58
0.6536
0.2814
/
SID-ENC (Huang ct al., 2022a)
21.30
0.6645
19.63
0.6941
20.47
0.6793
0.2797
11.49
52.29
DRBN-ENC (Huang ct al., 2022a)
21.89
0.7071
19.09
0.7229
20.49
0.7150
0.2318
11.23
54.15
CLIP-LIT (Liang et al., 2023)
15.13
0.5847
7.52
0,4383
11.33
0.5115
0.3560
/
FRCNet (Huang et al., 2022h)
22.01
0.6737
19.91
0.6961
20.96
0.6849
0,2656
11.05
53.73
FECNe++ERI. (Huang et ul., 2023)
22.35
0.6671
20.10
0.6891
21.22
0.6781
1
Retinexlommer (Cai et al., 2023)
23.75
0.7157
22.
0,7466
22.94
0.7310
0.1714
10.37
55.67
CoTF (Li et al., 2024a)
22.90
0.7029
20.13
0.7274
21,51
0.7151
0.1924
10.19
51.61
RetinexMamha (Bai et al., 2024)
23.56
0.7212
21.59
0.7384
22.58
0.7298
0.1856
10.35
53.67
LAnyeeTTiny
23.86
0.7197
22.26
0,7510
23.06
0,7354
0.1280
8,93
63.01
LALNet
24.63
0.7270
22.95
0,7473
23.80
0.7372
0.1397
9.34
61.49
Table 2. Quantitative results of image retouching methods. "/" denotes the unavailable source code.
Image Retouching HDRPlus
Method
#Params
PSNR
SSIM
TMQIf
LPIPS
ΣE]
NIQEL
MUSIQT
HDRNet (Gharbi et al., 2017)
482K
24.15
0.845
0.877
0.110
7.15
10.47
68.73
CSRNet (He et al., 2020)
37K
23.72
0.864
0.884
0.104
6.67
10.99
67.82
PeepI.PF (Moran et al., 2020)
1.72M
25.73
0.902
0.877
0.073
6.05
10.35
70.02
LUT (Zeng et al., 2020)
592K
23.29
0.855
0,882
0.117
7.16
11.36
67.67
CLUT (Zhang et al. 2022)
952K
26.05
0.892
0.886
0.088
5.57
11.19
67.39
sLUU (Wang et al., 2021)
4.52M
26.13
0.901
0.069
5.34
SepLUT (Yang al., 2022)
120K
22.71
0.833
0.879
0.093
8.62
12.26
67.89
Restormer (Zamir et al., 2022)
26.1M
25.93
0,900
0.883
0.050
6.59
10.49
68.92
LLFLUT (Zhang al., 2024)
731K
26.62
0.907
0.063
5.31
CoTF et al., 2024a)
310K
23.78
0.882
0.876
0.072
7.76
11.54
68.07
Retinexformer (Cai et al., 2023)
161M
26.20
0,910
0.879
0,046
6.14
10.75
68.93
RetinexMamba (Bai et al., 2024)
4.59M
26.81
0,911
0.880
0.047
5.89
10.52
69.02
MambalR (Guo et al., 2024)
4.31M
28.09
0.943
0.879
0.028
5,31
10.76
70.05
LALNet-Tinn LALNet
230K
29.30
0.939
0,886
0,030
5.04
9,70
69.98
2.45M
30.24
0.944
0.888
0.027
4.52
9.82
70.25
improves 2.29 dB PSNR and 0.0221 SSIM compared to the CoTF (Li et al., 2024a) (CVPR24) method. For image retouching, as shown in Tab. 2. the proposed LALNet out- performs all the previous SOTA methods by a large margin. Specifically, our method significantly outperforms the SOTA methods MambalR (Guo et al., 2024). RetinexFormer (Cai et al., 2023), LLFLUT (Zhang al., 2024) and CoTF (Li et al., 2024a), RetinexMamba (Bai et al., 2024), improving PSNR by 2.15 dB in the HDR+ dataset. LALNet-Tiny is a lightweight variant of LALNet (fewer feature channels and fewer LSSM blocks.) LALNet-Tiny has only 230K parameters and 1.75 GFLOPs, but the performance is also significantly better than other SOTA methods. For low-light enhancement, our LALNet significantly outperforms SOTA methods on the LOL-v1 dataset while requiring moderate computational and memory costs. Compared to the best recent method RetinexMamba, LALNet improves PSNR by 1.23 dB and SSIM by 0.027, and LALNet only costs 16% (6.70/ 42.82) GFLOPs. For tone mapping, Tab. 3 reports
Table 3. Quantitative results of tone mapping methods. "/" denotes the unavailable source code.
Tone Mapping in HDRI Haven LPIPS]
Method
PSNK
SSIMF
TMQ
Z5EL
UPE (Wang et al., 2019a)
23.58
0.821
0.917
0.191
10.85
HDRNe (Gharbi al., 2017)
25.33
0.912
0.941
0.113
7.03
CSRNst (Ho 2020)
25.78
0.872
0.928
0.153
6.09
DeepLPF (Meran et 2020)
24.86
0.939
0.948
0.077
7.64
LUT (Zeng et 2020)
24.52
0.846
0.912
0.171
7.33
CLUT (Zhang et al., 2022)
24.29
0.836
0.908
0.169
7.08
LPTN (Liang 2021b)
26.21
0.941
0.954
0.113
8.82
(Yang ScpLUT
2022)
24.12
0.854
8.03
Restormer (Zamir et al., 2022)
27.30
0.954
0.948
0.032
5.67
CaTF (Li et al., 2024a)
26.65
0.935
0.948
0.098
5.84
Retinexformer (Cal al., 2023)
27.73
0.955
5.41
RetinexMamba (Bai et al., 2024)
28.60
0.955
0.953
0.032
5.12
LALNet- Tiny
31.17
0.962
0.959
0.026
4.23
LALNet
32.46
0.969
0.961
0.019
3.58
the quantitative results on the HDRI Haven dataset. We can see that our method has the best overall performance.
Qualitative results. Visual comparison of LALNet and state-of-thed the light-related image enhancement methods are shown in Fig. 4, Fig. 5, Fig. 9, and Fig 10. Please zoom in for better visualization. To better visualize the
7

---

Learning Adaptive Lighting via Channcl-Aware Guidance
(A)
-3
25
Velue
Input
CSRNet
FECNet
ENC-DRBN
15
10
5
CoTF
Retinex Mamba
Ours
GT
B
40
35
30
25
Input
CSRNet
FECNet
ENC-DRBN
20
15
10
CoTF
Ratin
Ours
GT
Figure 4. Visual comparisons between LALet and the SOTA methods the SCIE dataset. (Zoom in for best view.) The error maps the upper left corner facilitate a more precise determination of performance differences.
performance differences of various methods, present error map to show the differences between the results of each method and the target image, as shown in the upper left corner of the image. In the error map, the red area indicates larger difference, while the blue area indicates that the two are closer. Notably, error maps have no special units and only indicate errors. These figures illustrate that our LALNet consistently delivers visually appealing results light-related tasks. Results reveal that the proposed method usually obtains better precise color reconstruction and vivid color saturation. Meanwhile, our method faithfully recon- structs fine high-frequency textures. For instance, in Fig. 4, the newest method, CoTF, exhibits distortion and color cast, but LALNet still performs well. In Fig. 9, method exhibits excellent color fidelity and restores proper global brightness and local contrast, consistent colors, and sharp details. These results prove that our method produces more pleasing visual effects. More results and visual comparisons are presented our Appendix and LALNet.
Table 4. Quantitative results of LLE methods the LOLv1 dataset.
"g* denotes that the results are from reference papers.
Low- Light Enhancement
Method
GFLOPs
PSNR
SSIM
DeepUPE (Wang et al., 2019b)
21.10
14.38
0.446
DecpLPF (Moran et al., 2020)
5.86
15.28
0.473
UFormer (Wang al., 2022)
12.00
16.36
0.771
RentinexNet (Wei et al., 2018)
$87.47
17.19
0.589
EnGAN (Jiang al., 2021)
61.01
17.48
0.650
Sparse (Yang et al., 2021)
53.26
17.20
0.640
FIDE (Xu et al., 2020)
28.51
18.27
0.665
KinD (Zhang et al., 2019b)
34.99
20.35
0.813
(Zamir et al., 2020)
0.842
LANet (Yang
2023a)
21.71
0,810
Restormer (Zamir et al., 2022)
144.25
22.43
0.823
CoTF al.., 2024a)
1.81
20.06
0.755
Retinexformer (Cai et al., 2023ys
15.57
23.93
0.831
Diff-Retinex (Yi al.. 2023)
396.32
21.98
0.852
DitTIR (Xia et al., 2023)
51.63
23.15
0.828
RetinexMamba (Bui et al., 2024)
42.82
24.03
0.827
LALNet-Tiny
1.75
24.07
0.845
LALNet
6.70
25.26
0.855
tiveness of the MCM, DDCM, LGA, and LSSM modules, we set up different variants to validate the effectiveness of the proposed framework. The results are listed in Tab. 5. Variants #1 serve as the baseline model and represent the re- moval of modules and replacement with residual blocks. For Variants #2 apply convolution block replace the
4.3. Ablation studies
We conduct comprehensive breakdown ablations to evaluate the effects of our proposed framework.
Effectiveness of specific modules. To validate the effec-
8

---

Learning Adaptive Lighting via Channcl-Aware Guidance
(A)
Input
LUT
CSRNeet
CLUT
Restorer
SepLUT
CoTF
RetinexMamba
Ours
GT
(B)
30
Input
3D LUT
CSRNet
CLUT
Restomer
SepLUT
CoTF
Retinex
GT
Figure 5. Visual comparisons between our LALet and the state-of of the-art methods on the HDRI Haven dataset (Zoom-in for best view). The error maps in upper left comer facilitate more precise determination performance differences.
Table 5. Ablation studies of key components on SCIE dataset.
Table 6. Ablation study on the pyramid levels number. The "N.A."
Variants
MCM
DDCM
LGA
LSSM
PSNRT
SSIMT
result is not available due to insufficient GPU memory.
#1
x
20.29
0.6834
Metries
Number of Levels
#12
x
23.95
0.7137
n=
n=2 n=3
n=4
#3
/
X
23.47
0.7144
PSNR
N.A.
23.45
24.63
23.07
#4
X
23.53
0.7229
SSIM
N.A.
0,7102
0.7270
0.7094
X
22.81
0.7091
TMQI
N.A.
0.8735
0.8667
0.8783
LPIPS
N.A.
0.1240
0.1270
0.116
#6
24.63
0.7270
AE
N.A.
8.18
7.53
8.22
MCM with performance reduction of 0.65 dB PSNR. In Variants #3, use group convolution replacing DDCM to extract channel-separated features, and the PSNR is reduced by 1.16 dB. For Variants #4, we remove the LGA mod- ule and directly sum channel-mixed and channel-sene separated features for light guidance. The results confirm the effec- tiveness the separ-separated feature to guide the light adaptation, with a PSNR increase of 1.11 dB. For Variants #5, replace the LSSM module with residual blocks and the performance drops by 1.82 dB. The results show that proposed DDCM, LSSM, LGA, and MCM are effective compared to conventional feature extraction. These results consistently demonstrate the effectiveness of our method.
#Params
2.20M
2.29M
2,45M
2.81M
FLOPs
36.
12.20G
6.70G
5.51G
image resolution, leading to burst of computational mem- ory. Comparing 1 = and 1 = 3 demonstrates that despite the small input image resolution of the low-frequency path- way, high- frequeryy details can still recovered efficiently in our framework.
5. Conclusion
This paper proposes a unified framework for learning adap- tive lighting via light property guidance. In particular, we propose DDCM for extracting color-separated features and capturing the light difference across channels. The LGA utilizes color-separated features to guide color-mixed fea- tures for adaptive lighting, achieving color consistency and color balance. Extensive experiments demonstrate that method significantly outperforms state-of -fhth-artt methods, improving PSNR by 0.86 dB in the SCIE dataset, 2.15 dB in the HDR+ dataset, 1.12 dB in the LOL dataset, and 3.86 dB in the HDRI Haven dataset respectively compared with the second-ben method.
Selection of the number of levels. We validate influ- ence of the number of pyramid levels As shown in Tab. the model achieves the best performance on all tested resolutions when 3. When larger number of levels (1 2 4) results in significant decline in performance. This is because when is larger and the number of downsamples more, the model fails to reconstruct the high frequencies efficiently, resulting performance degradation. When
l 1, the fow-frequency image resolution equals the input

---

Learning Adaptive Lighting via Channel-Aware Guidance
Acknowledgement
2352cic.200.155.aar0044 URL http::ttpp//..t/// 0rg/10.2352//c00 1t1.000.........00044.... 1...00000000444
15.
This work supported by the National Natural Science Foundation of China under Grants 62231018 and 62072331.
Finder, S. E.. Amoyal, R., Treister, E., and Freifeld, O.
Wavelet convolutions for large receptive fields. arXiv preprint arXiv:2407.05848, 2024.
Impact Statement
The proposed LALNet framework represents a significant advancement in the field of image processing, offering a versatile and efficient solution for various light-related tasks. By adapting the training data, LAINett makes it easier for even non-experts process high-quality images, contribut- ing positively to areas where image quality is crucial. In addition, this rescarch can further inspire developments in areas such autonomous driving, where complex problems are solved in novel ways.
Fu, X., Zeng, D., Huang, Y., Zhang, X.PP. and Ding, X. A weighted variational model for simultaneous reflectance and illumination estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2782-2790, 2016.
Gao, Q. and Wu, X. Real-time deep image retouching based on learnt semantics dependent global transforms. IEEE Transactions on Image Processing, 30:7378-777 77900, 2021.
Gharbi, M., Chen, J., Barron, J. T., Hasinoff, S. W., and Durand, F. Deep bilateral learning for real-time image enhancement. ACM Transactions on Graphics, pp. 1-12, Aug 2017.
References
Afifi, M., Derpanis, K. G., Ommer, B., and Brown, M. S. Learning multi-scale photo exposure correction. In Pro- ceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pp- 9157-9167, 2021.
Guo, C., Li, C.,, Guo, J., Loy, C. C., Hou, J., Kwong, S., and Cong, R. Zero-reference deep curve estimation for low-light image enhancement. In Proceedings of the IEEF/CVF conference on computer vision and pattern recognition, pp. 1780-1789, 2020.
Bai, J., Yin, Y., and He, Q. Retinexmamba: Retinex-base mamba for low-light image enhancement. arXiv preprint urXiv:2405.03349 2024.
Guo, H. Li, Dai, T., Ouyang, Z., Ren, X., and Xia, S.-T. Mambair: A simple baseline for image restoration with state-space model. arXiv preprint urXiv:2402.15644 2024.
Cai, J., Gu, S., and Zhang, I.. Learning deep single image contrast enhancer from multi-exposure images. IEEE Transactions on Image Processing, 27(4):2049 2062, 2018.
Hasinoff, S. W., Sharlet, D., Geiss, R., Adams, A., Barron, J. T., Kainz, F., Chen, J., and Levoy, M. Burst photog- raphy for high dynamic range and low-light imaging on mobile cameras. ACM Transactions on Graphics, pp. 1-12, Nov 2016.
Cai, Y., Bian, H., Lin, J.. Wang, H., Timofte, R., and Zhang, Y. Retinexformer: One-stage retinex- transformer for low-light image enhancement. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 12504-12513, 2023.
He, J., Liu, Y., Qiao, Y., and Dong, C. Conditional sequen- tial modulation for efficient global image retouching. In Computer Vision-ECCV 2020: 16th European Confer- ence, Glasgow, UK. August 23-28, 2020, Proceedings, Part XIII 16, pp. 679-695. Springer, 2020.
Cao, C.. Yue, H., Liu, X., and Yang, Unsupervised hdr image and video tone mapping via contrastive learning. IEEE Transactions Circuits and Systems for Video Technology, 2023.
Cao, X., Lai, K., Yanushkevich, S., and Smith, M. R. Ad- versarial and adaptive tone mapping operator for high dynamic range images. In 2020 IEEE Symposium Series Computational Intelligence (SSCI), Dec 2020.
Hou, X., Duan, J., and Qiu, G. Deep feature consistent deep image transformations: Downscaling. decolorization and hdr tone mapping. arXiv preprint arXiv: 094422. 2017.
Chen, Y.S.., Wang, Y.C.,, Kao, M.-H., and Chuang, Y.-Y. Deep photo enhancer: Unpaired learning for image en- hancement from photographs with gans. In Proceedings of the IEEE conference computer vision and pattern recognition, pp. 6306-6314, 2018
Hu, L., Chen, H., and Allebach, Joint multi-scale tone mapping and denoising for hdr image enhancement. In 2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW), Jan 2022.
Huang, J., Liu, Y., Fu, X., Zhou, M., Wang, Y., Zhao, F,, and Xiong, Z. Exposure normalization and compensation for multiple-exposure correction. In Proceedings of the
Fairchild, M. D. The hdr photographic survey. Color and Imaging Conference, pp. 233-238, May 2023. doi: 10.
10

---

Learning Adaptive Lighting via Channel-Aware Guidance
IEEE/CVF Conference on Computer Vision and Pallern Recognition, pp. 6043-6052, 2022a.
Liang, Z., Li, C., Zhou, S., Feng, R., and Loy, C. C. Iterative prompt learning for unsupervised backlit image enhance- ment. In Proceedings of the IEEE/CVF International Conference on Computer Vision. pp.
Huang, J., Liu, Y., Zhao, F., Yan, K., Zhang. J., Huang, Y., Zhou, M., and Xiong, Z. Deep fourier-based exposure correction network with spatial-frequency interaction. In European Conference on Computer Vision, pp. 163-180. Springer, 2022b.
2023,
Liu, R., Ma, L.,, Zhang, J., Fan, X., and Luo, 7. Retinex- inspired unrolling with cooperative prior architecture search for low-light image enhancement. In Proceed- ings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 10561-10570, 2021a
Huang, J., Zhao, F., Zhou, M., Xiao, J., Zheng. N., Zheng, K., and Xiong. Z. Learning sample relationship for ex- posure correction. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition, pp. 2023.
Liu, Z., Lin, Y., Cao. Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 10012-10022, 2021b.
Jiang, Y., Gong, X., Liu, D., Cheng, Y., Fang, C., Shen, X., Yang, J., Zhou, P., and Enlightengan, Z. W. Deep light enhancement without paired supervision., 2021, 30. DOI: https://doi. 0tg/10.1109// 109/TIP. pp. 2340-2200322 2349, 2021.
Moran, S.. Marza, P., McDonagh, S., Parisot, S., and Slabaugh, G. Deeplpf: Deep local parametric filters for image enhancement. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 12826-12835, 2020.
Kim, H.-U., Koh, Y. J., and Kim, C.-S. Global and local enhancement networks for paired and unpaired image enhancement. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXV 16, pp. 339-354. Springer, 2020.
Ni, Z., Yang, W., Wang. S., Ma, L.,, and Kwong, S. Towards unsupervised deep image enhancement with generative adversarial network. IEEE Transactions on Image Pro- cessing, 29:9140 91500 2020.
Li, C., Guo, C., Ai, Q., Zhou, S., and Loy, C. C. Flexi- ble piecewise curves estimation for photo enhancement. arXiv preprint arXiv:2010.1 134/2, 2020.
Nsampi, N. E., Hu, Z., and Wang. Q. Learning exposure correction via consistency modeling. In BMVC, pp. 12, 2021.
Li, 7., Zhang, F., Cao, M., Zhang, J., Shao, Y., Wang, Y., and Sang, N. Real-time exposure correction via collaborative transformations and adaptive sampling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.
Panetta, K., Kezebou, L.,, Oludare, V., Agaian, S., and Xia, Z. Tmo-net: A parameter-free tone mapping operator using generative adversarial network, and performance benchmarking on large scale hdr dataset. JEEE Access, pp. 99500-39517, 7, Jan 2021.
2024a.
Li, Z., Zhang, F., Cao, M., Zhang, J., Shao, Y., Wang. Y., and Sang, N. Real-time heal-time exposure correction via collaborative transformations and adaptive sampling. In Proceedings of the IEEE/CVF Conference on Computer Vision and 2024b.
Paris, S., Hasinoff, S. W., and Kautz, J. Local laplacian filters. In ACM SIGGRAPH 2011 papers, Jul 2011.
Pattern Recognition, pp.
Liang, J. Zeng, H., Cui, M. Xie, X., and Zhang, L. Pprl0k: A large-scale portrait photo retouching dataset with human-region mask and group-level consistency. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 653-661, 2021a. la.
Pizer, S. M., Amburn, E. P., Austin, J. D., Cromartie, R., Geselowitz, A., Greer, T., ter Haar Romeny, B., Zim- merman, B., and Zuiderveld, K. Adaptive histogram equalization and its variations. Computer vision, graphics, and image processing, 39(3):355-36 1987.
Liang, J., Zeng, H., and Zhang. L. High-resolutie photore- alistic image translation in real-time: A laplacian pyramid translation network. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2021b.
Rana, A., Singh, P., Valenzise, G., Dufaux, F., Komodakis, N., and Smolic, A. Deep tone mapping operator for high dynamic range images. IEEE Transactions on Image Processing, pp. 1285-1298, Jan 2020.
Liang, Z., Xu, J., Zhang, D., Cao, Z., and Zhang, L. A hybrid 11-10 layer decomposition model for tone mapping. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun 2018
Reza, A. M. Realization of the contrast limited adaptive histogram equalization (clahe) for real-time image en- hancement. Journal of VLSI signal processing systems for signal, image and video technology, 38:35 44, 2004.

---

Learning Adaptive Lighting via Channel-Aware Guidance
Rieke, F. and Rudd, M. E. The challenges natural images pose for visual adaptation. Neuron, 64(5): 605-616, Dec 2009, doi: 10.10166f.meuron.222 11.028. URL http: ddx..oii.......oooooooo.o orgiiorg//ee 1016/1. neuron 2009.11...... 11.....1...... 028.
Xia, B., Zhang, Y., Wang, S., Wang. Y., Wu, X., Tian, Y., Yang. W., and Van Gool, L. Diffir: Efficient diffu- sion model for image restoration. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 13095-13105, 2023.
Shensa, J. et al. The discrete wavelet transform: wedding the a trous and mallat algorithms. IEEE Transactions on signal processing, 40 10410)244422482, 1992.
Xu, K., Yang, X. Yin, B., and Lau, R. W. Learning to restore low-light images via decompssiioon-an--enanncee Proceedings of the IEEE/CVE conference on computer vision and pattern recognition, pp. 2281-2290, 2020.
In
Su, C.-C., Wang, R., Lin, H.-J., Liu, Y.-L., Chen, C.-P., Chang, Y.-L., and Pei, S.-C. Explorable tone mapping operators. In 2020 25th International Conference on Pattern Recognition (ICPR), pp. 10320-10326. IEEE, 2021.
Xu, X., Wang, R., Fu, C.-W., and Jia, J. Snr-aware low-light image enhancement. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 17714-17724, 2022.
Su, W., Wang. C., Liu, C.,, Han, F., Fu, H., and Liao, J. Styleretoucher: Generalized portrait image retouching with gan priors. IEEE Transactions on Visualization and Computer Graphics, 2024.
Yang. C., Jin, M., Xu, Y. Zhang, R., Chen, Y., and Liu, H. Seplut: Separable image-adaptive lookup tables for real-time image enhancement. In European Conference on Computer Vision, pp. 201-217. Springer, 2022.
Wang, H., Zhang, J., Liu, M., Wu, X., and Zuo, W. Learning diverse tone styles for image retouching. IEEE Transac- tions on Image Processing, 2023.
Yang, K.-F., Cheng, C., Zhao, S.-X., Yan, H.-M., Zhang, X.- S., and Li, Y.-J. Learning to adapt to light. International Journal of Computer Vision 131(4): 13424)0022-104 1041, 2023a.
Wang, R., Zhang, Q. Fu, C.-W., Shen, X., Zheng, W.-S., and Jia, J. Underexposed photo enhancement using deep illumination estimation. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2019a.
Yang, Q. Yue, H., Zhang, L., Liu, Y., Yang. J., et al. Learn- ing to see low-light images via feature domain adaptation. arXiv preprint arXiv:2312.0672. 2023b.
Yang, Q., Li, Y.. Jiang. P.-T., Cheng, Q. Yu, B., Liu, Y., Yue,
H., and Yang. J. Learning differential pyramid representa- tion for tone mapping. arXiv preprint arXiv:2412.01466. 2024.
Wang, R., Zhang, Q., Fu, C.-W., Shen, X., Zheng, W.-S., and Jia, J. Underexposed photo enhancement using deep illumination estimation. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. (8449-6557, 2019b.
Yang, Q. Cheng, Q. Yue, H., Zhang, L.,, Liu, Y., and Yang, J. Learning see low-light images via feature domain adaptation. IEEE Transactions on Image Processing, 2025.
Wang, T., Li, Y., Peng, J., Ma, Y., Wang, X., Song, F., and Yan, Y. Real-time image enhancer via learnable spatial- aware 3d lookup tables. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2471 2480, 2420,
Yang. W., Wang, S., Fang, Y., Wang, Y., and Liu, J. From fi- delity to perceptual quality: A semi-supervised approach for low-light image enhancement. In Proceedings of the IEEE/VVF conference on computer vision and pattern recognition, pp. 3063-3072, 2020.
Wang, Z., Cun, X., Bao, J., Zhou, W., Liu, J., and Li, H. Uformer: A general s-spa transformer for image restoration. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 17683- 17693, 2022.
Yang, W., Wang, W., Huang, H., Wang, S., and Liu, J. Sparse gradient regularized deep retinex network for ro- bust low-light image enhancement. IEEE Transactions on Image Processing,
2021.
Wei, C., Wang, W., Yang, W., and Liu, J. Deep retinex decomposition for low-light enhancement. arXiv preprint 1808.080886666 2018.
Yeganeh, H. and Wang, Z. Objective quality assessment of tone-mapped images. IEEE Transactions on Image Processing, pp. 657-667, Feb 2013.
Wu, W., Weng, J., Zhang, P., Wang, X., Yang, W., and Jiang, J. Uretinex-net: Retinex- based deep unfolding network for low-light image enhancement. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 5901-5910, 2022.
Yi, X., Xu, H., Zhang, H., Tang, I.., and Ma, J. Diff-retinex: Rethinking low-light image enhancement with a genera- tive diffusion model. In Proceedings of the IEEE/CVF International Conference on Computer Vision pp. 12302- 12311, 2023.
12

---

Learning Adaptive Lighting via Channel-Aware Guidance
Zamir, S. W., Arora, A., Khan, S., Hayat, M., Khan, F. S., Yang, M.-H., and Shao, L. Learning enriched features for real image restoration and enhancement. In Computer Vision-ECCY 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXV 16, pp- 492-511. Springer, 2020.
Zamir, S. W., Arora, A., Khan, S., Hayat, M., Khan, F. S., and Yang, M.-H. Restormer: Efficient transformer for high-resolution image restoration. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 5728-5739, 2022.
Zeng, H., Cai, J., Li, L.., Cao, Z., and Zhang, L. Learning image-adaptive 3d lookup tables for high performance photo enhancement in real-time. IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-1, Jan 2020.
Zhang, F., Zeng, H., Zhang, T., and Zhang, L. Clut-net: Learning adaptively compressed representations 3dluts for lightweight image enhancement. In Proceedings of the 30th ACM International Conference on Multimedia, pp. 6493-6501, 2022.
Zhang, F., Tian, M., Li, Z., Xu, B., Lu, Q., Gao, C., and Sang, N. Lookup table meets local laplacian filter: pyra- mid reconstruction network for tone mapping. Advances in Neural Information Processing Systems, 36, 2024.
Zhang, N., Wang, C., Zhao, Y., and Wang, R. Deep tone mapping network hsv color space. In 2019 JEEE Visual Communications and Image Processing (VCIP), pp. 1-4. IEEE, 2019a.
Zhang, R., Isola, P., Efros, A. A., Shechtman, F.,, and Wang, O. The unreasonable effectiveness of deep features as a perceptual metric. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 586-595, 2018.
Zhang, X., Wandell, B. A., et al. A spatial extension of cielab for digital color image reproduction. SID in- ternational symposium digest of technical papers, vol- ume 27, pp. 731-734. Citeseer, 1996.
Zhang, Y., Zhang, J., and Guo, X. Kindling the darkness: A practical low-light image enhancer. Proceedings of the 27th ACM international conference on multimedia, pp. 1632-1640, 2019b.
13

---

Learning Adaptive Lighting via Channel-Aware Guidance
Appendix
In this Appendix, we provide additional results and analysis.
A. Further Analysis of Motivation
Different wavelengths of light exhibit different response characteristics when an image sensor captures photons for photoelectric conversion. After processing by an image signal processor, these differential responses are sometimes amplified or minimized but are difficult to eliminate. In addition, the differences in the Bayer pattern of different image sensors also result in different channels showing different responses to luminance and noise. Meanwhile, light sources in natural scenes are usually non-uniform, which also leads the fact that sunlight, shadows, reflections, and other factors can cause RGB channels to respond differently to the same scene.
Recall that in Sec. 1, we discussed two observations that serve the motivation design LALNet. We show more motivation cases in Fig. 6 (the exposure correction and tone mapping tasks). In particular, (a) different color channels have different light properties, and (b) the channel differences reflected in the time and frequency domains are different. To further analyze our first motivation, we visualized the frequency domain images of the different channels using the Fourier Transform and compared them. The results show that, as time domain, significant differences are exhibited between the different channels the frequency domain. Based on the observations Fig. and Fig. 6, the common properties of several light-related tasks investigated in this paper are verified, which also contribute the design of network.
20
1.
Exposure Correction
0.5
LowFreg
HighFreq
sRGB
R-channel
G-channel
B-channel
Energy Distribution
LowFreq
R-FFT
G-FFT
B-FFT
HighFreg
Tone
Mapping
LowFree
HighFree
sRGB
R-channel
G-channel
B-channel
Energy Distribution
LowFreq
R-FFT
G-FFT
B-FFT
HighFreq
Figure Motivation. Visualization of the light-related task images diffcrent color channels and their corresponding DWT spectra energy distribution. R-FFT denotes the Fourier Frequency Domain diagram of the channel. LowFreq and HighFroq are low- frefureuency and frequncyy images.
B. Visualization in the Network
We demonstrate the Iterative Detail Enhancement modules (IDE), and Light State Space Module (LSSM) in Fig. 7 and Fig. 8. To reduce the computational resources, we implement light adaptation at low resolution. To compensate for the loss of details, we use an iterative detail enhancement module to recover we first up-sample the wow-frequeccy mapped image Y and concatenate with the HF component X then feed into a residual network to predict the mask M,-1. This mask allows pixel-by-pin refinement the HF component, which subsequently added the up-sampling Y to generate the reconstructed result of the current layer Y
details. Specifically, as shown in Fig. 7,
14

---

Learning Adaptive Lighting via Channcl-Aware Guidance
Ane
Cane
ResBBock
Res
ResBlock
Carv
Upsempling
Figure 7. The architecture of Iterative Detail Enhancement module progressively restores resolution and fine details.
SS2D
Fes
Corv
Linear
cm
Fla
silu
Figure 8. The architecture of the Light State Space Module.
C. More Results
We further evaluate the effectiveness of our model on the exposure correction (Afif et al., 2021), HDR Survey (Fairchild, 2023), and UVTM (Cao et al., 2023) datasets, all of which present more complex lighting conditions.
The MSEC dataset (Afifi et al., 2021) provides images rendered with relative exposure values (EVs) ranging from -1.5 to +1.5, comprising 17,675 training images, 750 validation images, and 5,905 test images. Table presents the quantitative results on MSEC. As shown, our method achieves the best overall performance, with PSNR of 23.93 dB, SSIM of 0.8734, and LPIPS of 0.0791.
Table Quantitative results exposure correction methods on the MSCE dataset.
Exposure Correction in MSCE
Method
Under
Over
Average SSIM1
PSNR1
SSIM
PSNR
SSIM
PSNR
LPIPS
He (Pizer al., 1987)
16.52
0.6918
16.53
0.6991
16.53
0.6959
0.2920
CLAHE (Reza, 2004)
16.77
0.6211
14.45
0.5842
15.38
0.5990
0.4744
WVM (Fu et al., 2016)
18.67
0.7280
12.75
0.645
15.12
0.6780
0.2284
RetinexNet (Wei et al., 2018)
12.13
0.6209
10.47
0.5953
11.14
0.6048
0.3209
URtinexNet (Wu al., 2022)
13.85
0.7371
9.81
0,6733
11.42
0.6988
0.2858
DRBN (Yang al., 2020)
19.74
0.8290
19.37
0.8321
19.52
0.8309
0.2795
SID (Chen ct al., 2018)
19.37
0,8103
18.83
0,8055
19.04
0.8074
0.1862
MSEC (Afifi ct al., 2021)
20.52
0.8129
19.79
0.8156
20.08
0.8145
0.1721
SID-ENC (Huang al., 2022a)
22.59
0.8423
22.36
0.8519
22.45
0.8481
0.1827
DRBN-ENC (Huang al., 2022a)
22.72
0.8544
22.11
0.8521
22.35
0.8530
0.1724
CLIP-LIT (Liang et al., 2023)
17.79
0.7611
12.02
0.6894
14.32
0.7181
0.2506
FECNet (Huang et al., 2022b)
22.96
0,8598
23.22
0,8748
23.12
0.8688
0.1419
LCDPNet (Zhang et al., 2019b)
22.35
0,8650
22.17
0.8476
22.30
0,8552
0.1451
FECNet+ERL (Zamir al., 2020)
23.10
0.8639
23.18
0.8759
23.15
0,8711
CoTF (Yang al., 2023a)
23.36
0.8630
23.49
0.8793
23.44
0.8728
0.1232
LALNet
23.78
0.8638
24.01
0.8787
23.90
0.8713
0.0801
To further demonstrate the robustness and generalization ability of our model, we evaluate it third-party con-homologous HDR image (HDR Survey) and video (UVTM) datasets, as summarized Table The HDR Survey dataset comprises 105 HDR images and is widely adopted for benchmarking HDR tone mapping methods (Cao al., 2020: Rana et al., 2020; Panetta et al., 2021; Liang et al., 2018 8; Paris et al., 201 1), though it does not provide ground-truth references. Likewise, the UVTM dataset contains 20 real-world HDR videos, also without ground truth. It is important to note that both the HDR Survey and UVTM datasets are used solely for testing purposes. As shown in Table 8, our method significantly outperforms existing approaches on both benchmarks in terms of TMQL. Specifically, our model achieves a TMQI score of 0.9296 on the HDR Survey and 0.9584 on the UVTM, surpassing all competing methods.
15

---

Learning Adaptive Lighting via Channel-Aware Guidance
Table 8. Validating generalization on third-party datasets, including HDR Survey and UVTM video datasets.
Datasets
Metrics
HDRNet
CSRNet
3D LUT
CLUT
SepLUT
IVTMNet
CoTF
Ours
HDR Survey
TMQI
0.8641 0.8281
0.8439
0.8165
0.8140
0.8085
0.9160 0.8991
0.8612
0.9296
UVTM
TMQI
0.8973
0.8787
0.8799
0.8629
0.9006
0.9584
40
30
Input
LUT
CSRNet
CLUT
Restormer
20
10
SepL.UT
Retinex amba
Ours
GT
(B)
40
30
Input
LUT
CSRNet
CLUT
Restomee
20
Enarr
10
SepLUT
CoTF
RatinexMamba
Ours
GT
Figure 9, Visual comparisons between LALet and the ttate-of-the methods the HDR+ dataset.
D. Ablation Study
To validate the effectiveness of the SS2D module, use Self-Attention and Residual Block to replace the SS2D module in the original published model. We use the Self-Attention module released by Restormer (Zamir al., 2022), and ResBlock is constructed from two convolutional layers and activation functions. The results, as shown in Table 9, show that using SS2D as part of the base module effectively captures global features and strikes a balance between performance and efficiency. Notably, the same excellent results are obtained using the Self-Attention module, which is attributed to the design of our overall framework, further demonstrating the effectiveness of our proposed adaptive lighting framework.
Further, we use DDCM capture color-separated features, and avoid channel mixing during information propagation, group convolution to keep the color channels separated. To verify the effectiveness of the design, we use traditional convolution to replace group convolution. The experimental results are shown in Table 10, where the channel mixing caused by the conventional convolution leads to a performance degradation. This phenomenon shows the necessity of color channel separation and the effectiveness of using color-separated features to guide light adaptation.
Table 9. Ablation study on the LSSM modules.
Variants
Replaced Modules
#Params
FLOPs
PSNR
SSIM1
TMQI
LPIPS
AEJ
#1
ResBlock
2.99M
7.13G
22.81
0,7091
0.8635
0.1291
8,480
#2
Self-Attention
2.25M
6.48G
24.41
0.7253
0.8657
0.1257
7.525
#3
Ours
2.45M
6.70G
24.62
0.7227
0.8667
0.1297
7.529
16

---

Learning Adaptive Lighting via Channel-Aware Guidance
Table 10. Ablation study on the group Convolution (G-Conv) and traditional Convolution (T-Conv).
Variants
Replaced Modules
#Params
FLOPs
PSNR1
SSIM
TMQIt
LPIPS
AEl
#1
T-Conv
2.50M
6.73G
23.98
0.7121
0.8521
0.1363
8.146
#2
G-Conv
2.45M
6.70G
24.62
0.7227
0.8667
0.1297
7.529
E. Loss functions
The proposed framework obtains faithful light enhancement by optimizing the reconstruction loss, perceptual loss, and high-freeng high-frequency loss. We utilize three objective losses to optimize our network, including reconstruction loss (Lre and TssIM), perceptual loss (Lr), and high-frequency loss (ThF). To summarize, the complete objective of our proposed model is combined as follows:
Total = TR + B LSSIM + LHF + Lp,
(19)
where 8, and 17 are the corresponding weight coefficients.
(A)
35
Input
CSRNet
Restormer
PetinexFormen
-20
Emot
10
5
CoTF
Retinex Mamba
Ours
GT
(B)
25
Input
CSRNet
Restormer
RetinexFormer Former
Erro
10
CoTF
RetinexMamby
Ours
GT
Figure Visual comparisons between our LALet and the SOTA methods on the LOLy1 dataset.
17


Reviews:
[{"review_id": "jnUUaZ40yi", "reviewer": "Reviewer_2AwK", "summary": "This paper proposes a unified framework, LALNet, for handling multiple light-related tasks in computer vision, such as exposure correction, image retouching, low-light enhancement, and tone mapping. The authors identify common properties across these tasks, such as varying light properties in different color channels and their differences in both spatial and frequency domains. LALNet leverages these properties by using a dual-branch architecture that separates and mixes color channels, incorporating a Light Guided Attention mechanism to ensure visual consistency across channels. The framework is designed to adapt to light variations while preserving image details. Extensive experiments demonstrate that LALNet outperforms state-of-the-art methods in several tasks and requires fewer computational resources.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewers for their feedback and the opportunity to clarify our work. Below, we address the concerns raised.\n> Q1. Motivation and Justification\n\n**A1:**  \nAs detailed in the “Introduction” section, many light-related tasks—such as exposure correction, image retouching, low-light enhancement, and tone mapping—share a common goal: adjusting scene lighting to achieve perceptual optimality. However, each task has its own subtle emphasis:  \n- **Exposure correction** balances under/overexposed regions.  \n- **Image retouching** enhances aesthetics via global illumination adjustments.  \n- **Low-light enhancement** brightens dark areas while suppressing noise.  \n- **Tone mapping** compresses HDR content while preserving details.  \n\nExisting methods typically rely on task-specific architectures and training strategies. We are not claiming that handling these tasks separately is inherently a drawback. Instead, our key motivation is that treating these tasks under a unified framework offers significant advantages:  \n1) **Improved generalization** across diverse lighting conditions (Figure 1).  \n2) **Enhanced efficiency** by reducing the need for multiple specialized models.  \n3) **Consistent performance** across tasks without significant trade-offs (Table 1, Table 2, Table 3, and Table 4).  \n\nIndeed, prior works have attempted unified treatments for some of these tasks but often sacrificed performance compared to specialized models. Along this avenue, we aim to design a unified framework that effectively generalizes across diverse light adaptation tasks. Our proposed LALNet achieves this goal by delivering robust performance across multiple light-related tasks (Figure 1).  \n\n> Q2. Modular Approach Lacking Coherent Integration\n\n**A2:** LALNet’s design is grounded in two key insights from light-related tasks:\n\n(i) Different color channels have different light properties; \n\n(ii) The channel differences reflected in the spatial and frequency domains are different.\n\nTo effectively leverage these observations, LALNet employs a dual-branch architecture:\n* **MCM (Mixed Channel Modulation):** Capture channel-mixed features, focusing on inter-channel relationships and lighting patterns.\n* **DDCM (Dual Domain Channel Modulation):** Extract color-separated features, focusing on light differences and color-specific luminance distributions for each channel in the spatial and frequency domains.\n\nTo ensure harmonious fusion of these complementary features, we introduce:\n* **LSSM (Lighting State Space Model):** Integrates color-separated and color-mixed features to enhance illumination consistency across channels.\n* **LGA (Lighting-Guided Attention):** Uses color-separated features as queries, guiding the learning of inter-channel illumination and noise differences for adaptive light restoration.\n\nOur ablation studies (Table 5) confirm that each component is indispensable, as removing any one leads to performance degradation. We will further clarify these interdependencies in the revised manuscript.\n\n> Q4. Unified Framework\n\n**A4:** We appreciate the reviewer’s concern regarding whether LALNet constitutes a truly unified framework. While it is true that each task is optimized separately within LALNet, these tasks share a common architecture and design principles. This shared structure provides a promising advantage over entirely separate task-specific models, considering practical streamlined deployment for low-level tasks. A single framework can be adapted to multiple tasks with minimal adjustments, simplifying hardware deployment. We will clarify this point further in the revised manuscript.\n\n>Q4. Writing and Presentation Improvements\n\n**A4:** We sincerely appreciate the reviewer's feedback regarding the writing style and structure of our manuscript. We acknowledge that certain aspects, such as missing citation (\"Mamba\"),  the improper use of transitions (e.g., \"however\"), and disjointed arguments, may have affected the clarity and coherence of our key points. To address these concerns, we will carefully revise the manuscript to enhance the logical flow, strengthen the motivation, and improve the articulation of our contributions and methodology, ensuring they are more accessible to readers.", "claims_and_evidence": "The motivation of the paper is unclear. In the abstract, the author states that current research addresses light-related tasks, such as HDR imaging and exposure correction, individually. However, it's not clear why handling these tasks separately is a significant drawback. The use of \"however\" seems to be a transition to introduce the author's work, but logically, there is no strong connection between the two statements, which weakens the paper's fundamental motivation. The writing in other parts of this paper also has not explained this point clearly yet.", "methods_and_evaluation_criteria": "This paper proposes multiple components, such as MCM, DDCM, LGA, and LSSM. However, from Table 5, it seems that LSSM is relatively crucial for performance improvement. Here, LSSM appears to be a structure borrowed from mamba. The author should clearly elaborate and explain this and provide proper citations. In addition, it is not clear what the relationship is between these components and the motivation claimed by the author in Figure 2. The author only lists the necessary materials and steps like writing a recipe or instruction manual (even so, there is still much room for improvement in writing).", "theoretical_claims": "There are no proofs for theoretical claims.", "experimental_designs_or_analyses": "Just from the text expounded in this article, the experimental designs and analysis are reasonable.", "relation_to_broader_scientific_literature": "Previous work in this paper has used the unified framework to handle lightness adaptation. This paper should emphasize the differences from these works and further prove the rationality of its own motivation. The current manuscript appears to be a collection of various designed modules without a clear, engaging integration.", "essential_references_not_discussed": "No essential references are not discussed.", "other_strengths_and_weaknesses": "Although the network proposed by the author has achieved a great improvement in final performance, I still have the following concerns.\n\n1. Weak Motivation and Unclear Justification: The manuscript claims that current methods address light-related tasks in isolation, but fails to explain why this is a significant issue. The authors introduce their work by stating \"however,\" but there is no clear link between the shortcomings of existing methods and the necessity of their approach. This weakens the foundational argument for the proposed framework.\n\n2. Modular Approach Lacking Coherent Integration: The paper presents several modules, such as Dual Domain Channel Modulation and Light Guided Attention, but they feel disconnected. While each module may perform well individually, there is insufficient explanation of how these modules are effectively integrated into a unified framework that addresses multiple light-related tasks. This lack of integration diminishes the overall impact of the framework.\n\n3. Writing Style Needs Improvement: The writing style of the manuscript has significant room for improvement. The current structure and presentation make it difficult to grasp the key points clearly. The arguments are often disjointed, and the flow of ideas lacks coherence, which hinders the reader's understanding of the main contributions of the paper. \n\n4. Lack of True Unified Framework: Although the paper claims to present a unified framework, it seems that, if I am not mistaken, the framework still requires separate training for different tasks. In practice, this means that multiple sets of parameters are needed for various tasks, which raises the question of how this approach offers any advantages over training task-specific networks separately. From a practical application standpoint, this limitation undermines the claim of achieving a truly unified solution.", "other_comments_or_suggestions": "see above.", "questions_for_authors": "see above.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "pCscJo82ih", "reviewer": "Reviewer_kmer", "summary": "This paper proposes a unified light adaptation framework, Learning Adaptive Lighting Network (LALNet), based on the finding that the color statistics on spatial and frequency domains differ from different light-related tasks. On top of this observation, the paper introduces a Dual Domain Channel Modulation module that captures and aggregates color-separated and color-mixed embeddings for optimal light to the target task. The method shows superior performance by evaluating the model on four light-related datasets, such as exposure correction, image retouching, low-light enhancement, and tone mapping.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate the reviewer’s constructive feedback and valuable suggestions. Below, we address the key concerns and outline our planned improvements.\n\n>Q1. Improving the Readability of the Method Section\n\n**A1:** To improve the clarity of the Method section, we have restructured it into a more logical and intuitive organization. The revised structure is as follows:\n* 2.1 Motivation: We introduce the fundamental challenges of lighting adaptation and explain why a unified framework is necessary.\n* 2.2 Framework Overview: We provide a high-level description of the proposed framework, detailing its architectural design and how different components interact.\n* 2.3 Color Separation Representation: Our model first extracts color-separated features to decouple illumination-related information in both the frequency and spatial domains. This step enables the network to learn distinct representations for different color channels, mitigating color distortion and channel-dependent illumination inconsistencies.\n* 2.4 Color Mixing Representation: We introduce mixed channel modulation for capturing inter-channel relationships and lighting patterns that achieve a harmonious light enhancement.\n   - 2.4.1 Mixed Channel Modulation: This subsection introduces mechanisms to effectively integrate cross-channel lighting information.\n  - 2.4.2 Light State Space Module (LSSM): We introduce the LSSM, which leverages state-space representations to refine the global illumination representation dynamically, improving stability and adaptability under varying lighting conditions.\n* 2.5 Light-Guided Attention: We propose a novel light-guided attention mechanism that utilizes color-separated features to guide color-mixed light information for adaptive lighting. This mechanism enhances the network’s capability to perceive changes in channel luminance differences and ensure visual consistency and color balance across channels.\n\nThis restructuring ensures that the methodology follows a progressive flow from problem motivation to implementation details, making it easier for readers to understand. \n\n>Q2. Loss Function for Model Training\n\n**A2:** Thank you for your valuable feedback. Our framework optimizes light adaptation using the following objective function: \nThe overall objective function is formulated as follows:\n\n $L_{\\mathrm{total}} = \\alpha L_{Re} + \\beta L_{SSIM} + \\gamma L_{HF} + \\eta L_{p}$,\n\nwhere:\n* $L_{Re}$: reconstruction loss, \n* $L_{p}$: perceptual loss, \n*  $L_{HF}$: high-frequency loss,\n* $L_{SSIM}$: structural similarity loss.\n\n$\\alpha$, $\\beta$, $\\gamma$, and $\\eta$ are the corresponding weight coefficients. \n\nTo justify our design, we conducted an ablation study (Table R2):\n\nTable R2: Ablation studies on different loss functions.\n| $L_{Re}$ | $L_{HF}$ | $L_{SSIM}$ | $L_{p}$ | PSNR↑ | SSIM↑ |\n|:-:|:-:|:-:|:-:|:-:|:-:|\n| &#x2714; | &#x2716; | &#x2714; | &#x2714; | 30.14 | 0.944 |\n| &#x2714; | &#x2714; | &#x2716; | &#x2714; |29.88 | 0.941 |\n| &#x2714; | &#x2714; | &#x2714; | &#x2716;  | 29.72 | 0.939 |\n| &#x2714; | &#x2714; | &#x2714; | &#x2714; | 30.36 | 0.946 |\n\nThese results demonstrate that incorporating all four losses leads to the best overall performance, underscoring the necessity of each component. We will clarify this in the revised manuscript.\n\n>Q3. LALNet-Tiny Design Description\n\n**A3:** LALNet-Tiny is a lightweight variant of LALNet, designed to improve efficiency while preserving strong performance. The key differences include:\n* Reduced channel dimensions: Fewer feature channels to reduce computational complexity.\n* Fewer LSSM modules: A more compact design with fewer LSSM modules.\n\nWe will include a brief section describing LALNet-Tiny design and relationship to LALNet.\n\nWe appreciate the reviewers' valuable insights. Our key improvements include:\n1. Method Section Restructuring: Introducing explicit subsections and improving explanations with figures.\n2. Loss Function Clarification: Providing a detailed breakdown, justification, and coefficient selection strategy.\n3. LALNet-Tiny Performance Analysis: Add a detailed description.\n\nWe believe these enhancements will further strengthen our work and address the reviewers' concerns effectively. Thank you for your constructive feedback!", "claims_and_evidence": "The main finding of the paper is the color statistics on spatial and frequency domains differ from different light-related tasks. This observation leads to a unified framework that learns adaptive light for various light-related tasks. The finding and its corresponding architecture design sound good to me. And its effectiveness is well supported by experimental results.", "methods_and_evaluation_criteria": "The paper proposed a unified light adaptation framework for four light-related tasks, including exposure correction, image retouching, low-light enhancement, and tone mapping. For this purpose, the paper evaluates the method on exposure correction (SCIE (Cai et al., 2018)), image retouching (HDR+ Burst Photography (Hasinoff et al., 2016)), low-light enhancement (LOL dataset (Wei et al., 2018)), and tone mapping (HDRI Haven (Zhang et al., 2019a), along with common image evaluation metrics, such as PSNR, SSIM, etc.\nThe proposed method, dataset, and evaluation criteria make sense for the paper's problem definitions.", "theoretical_claims": "No theoretical claims.", "experimental_designs_or_analyses": "The experiments are conducted on four different light-related tasks. One concern was that the used dataset seems small-scale, so the scalability of the proposed method is curious. But, supplementary material (Sec E) shows more complex and larger-scale experiments on low-light enhancement, HDR survey, and UVTM dataset. So, the overall experimental design and setup are sound good to me. \nHowever, the LALNet-Tiny is not described in the manuscript how designed from LALNet.", "relation_to_broader_scientific_literature": "The paper's key contribution is a unified framework for various light-related tasks, which can influence low-light enhancement, exposure correction, HDR, retouching, etc.", "essential_references_not_discussed": "The related works on a unified framework, low-light enhancement, exposure correction, and tone mapping are adequately cited in the main paper and supplementary material.", "other_strengths_and_weaknesses": "[Weakness]\n* The presentation for the method section can be improved by adding subsection and subsubsection. For instance, the color mixing representation section is composed of the wavelet transform part, the Light State Space Module (LSSM) part, and its first, second, and third streams. They can be divided rather than describing them in a single paragraph. This is the same for other method sections. In general, the current presentation reduces the readability of the method sections.", "other_comments_or_suggestions": "* I think I might have missed it, But I can not find the loss function to train the model. \n* LALNet-Tiny is not described in the manuscript how designed from LALNet.", "questions_for_authors": "Please answer the weakness (Q1), other comments (Q2, Q3)", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {"ethical_review_concerns": "No ethical concerns"}}, {"review_id": "sYuVJeadBd", "reviewer": "Reviewer_UEvw", "summary": "The paper uses the channel differences in spatial and frequency domains for lighting adaptation and mixes them for enhancement. The paper is well organised and the experimental results show the effectiveness and efficiency of the proposed method. The authors can give more detail about the structure of DDCM and MCM in Figure 3, which is not required in the rebuttal period.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate the reviewer’s insightful comments and provide detailed responses below.\n\n>Q1. What do cA, cH, cV and cD mean in equation 11?\n\n**A1:** These variables represent the components of the 2D wavelet transform applied to the input image:\n1. cA denotes the approximation coefficients of the wavelet transform.\n2. cH denotes the horizontal detail coefficients.\n3. cV denotes the vertical detail coefficients.\n4. cD denotes the diagonal detail coefficients.\n\nThe wavelet transform decomposes the image into different frequency bands, which enables effective separation of illumination and detail information. These components are then leveraged in our color mixing representation to enhance adaptive lighting adjustment. We will clarify this in the revised manuscript to improve readability.\n\n>Q2. The structure of Light Guided Attention seems basic SSM(Mamba) and Attention structure, do you make any improvements?\n\n**A2:** We acknowledge the reviewer’s concerns regarding the relationship between our Light State Space Module (LSSM) and Mamba. While LSSM shares similarities with state-space modeling principles, we introduce several key improvements beyond MambaIR:\n1. Gated MLP instead of CAB: Unlike MambaIR, which uses the CAB module to mitigate channel redundancy, we found through activation visualizations that such redundancy was not significant in our task. Instead, we propose a Gated MLP module, which jointly processes spatial and channel-wise features, enhancing adaptability.\n2. Channel separation in LSSM: We inject channel-separated features into the light adaptation process, enabling the network to better perceive channel brightness variations, thereby improving color consistency and balance across RGB channels.\n3. LSSM as an optional component: While LSSM enhances feature extraction, it is not irreplaceable. We experimented with substituting LSSM with residual blocks and self-attention modules, and the model still achieved performance on par with state-of-the-art methods.\n\nAdditionally, LGA utilizes channel-separated features to guide color-mixed features, ensuring effective lighting adaptation while maintaining cross-channel consistency—a novel application not explored in prior works. We will provide clearer explanations and appropriate citations in the revision.\n\n\n>Q3. How about the running speed of the proposed method for each image? Does it have advantages over other methods?\n\n**A3:** We recognize the importance of computational efficiency and provide a running time comparison with state-of-the-art methods.\n\nTable R3: Inference Speed Comparison (Seconds per Image on 480p).\n| Method          | Run Time |\n|:-:|:-:|\n| FECNet          | 0.012    |\n| COTF            | 0.010    |\n| CSRNet          | 0.012    |\n| RetinexMamba    | 0.4778   |\n| RetinexFormer   | 0.028    |\n| MambaIR         | 0.4595   |\n| Ours            | 0.018    |\n\nOur method achieves a competitive inference time of 0.018s per image, which is faster than RetinexFormer, RetinexMamba, and MambaIR, while maintaining superior enhancement quality. This demonstrates a favorable balance between efficiency and effectiveness.\n\n>Q4. What are the differences between LALNet-Tiny and LALNet?\n\n**A4:** LALNet-Tiny is a lightweight variant of LALNet, designed to improve efficiency while preserving strong performance. The key differences include:\n* Reduced channel dimensions: Fewer feature channels to reduce computational complexity.\n* Fewer LSSM modules: A more compact design with fewer LSSM modules.\n\nWe will include a brief section describing LALNet-Tiny design and relationship to LALNet.\n\nWe appreciate the reviewer’s recognition of our contributions and their thoughtful feedback. We believe these clarifications and improvements will further strengthen our work. Thank you for your time and valuable insights.", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "Yes", "experimental_designs_or_analyses": "Yes", "relation_to_broader_scientific_literature": "The paper can promote the development of image post-process.", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "The paper is generally well-written.", "other_comments_or_suggestions": "No", "questions_for_authors": "1. What do cA, cH, cV and cD mean in equation 11?\n2. The structure of Color Mixing Representation and Light Guided Attention seems basic SSM(Mamba) and Attention structure, do you make any improvements?\n3. How about the running speed of the proposed method for each image? Does it have advantages over other methods?\n4. What are the differences between LALNet-Tiny and LALNet?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper presents LALNet, a unified framework designed to tackle multiple light-related image processing tasks such as exposure correction, image retouching, low-light enhancement, and tone mapping. Through extensive experiments, the authors demonstrate that LALNet outperforms state-of-the-art methods across several benchmarks while maintaining computational efficiency.

The idea of unifying these tasks into a single model is practically valuable, and reviewers acknowledged the empirical performance shown in experimental results.


Reviewers initially raised concerns regarding the motivation for the architectural design choices, the clarity of the representation strategy, and the depth of the ablation analysis. However, the authors provided a detailed and convincing rebuttal, addressing these issues with additional explanation and justification. 

That said, the level of innovation remains moderate, as many of the modules and design elements are adapted from existing works. While the unified framework is well-engineered and well-validated, the contribution may not be sufficiently novel particularly for the ICML audience, given the fact that there is no theorical contribution.
Decision: Accept (poster)