MambaLLIE: Implicit Retinex-Aware Low Light Enhancement with Global-then-Local State Space
Authors: Jiangwei Weng
Zhiqiang Yan
Ying Tai
Jianjun Qian
Jian Yang
Jun Li
Venue: NeurIPS 2024 poster (2024)
Keywords: Low Light Enhancement
State Space Models
Feature Control
Primary area: machine_vision

Abstract: Recent advances in low light image enhancement have been dominated by Retinex-based learning framework, leveraging convolutional neural networks (CNNs) and Transformers. However, the vanilla Retinex theory primarily addresses global illumination degradation and neglects local issues such as noise and blur in dark conditions. Moreover, CNNs and Transformers struggle to capture global degradation due to their limited receptive fields. While state space models (SSMs) have shown promise in the long-sequence modeling, they face challenges in combining local invariants and global context in visual data. In this paper, we introduce MambaLLIE, an implicit Retinex-aware low light enhancer featuring a global-then-local state space design. We first propose a Local-Enhanced State Space Module (LESSM) that incorporates an augmented local bias within a 2D selective scan mechanism, enhancing the original SSMs by preserving local 2D dependency. Additionally, an Implicit Retinex-aware Selective Kernel module (IRSK) dynamically selects features using spatially-varying operations, adapting to varying inputs through an adaptive kernel selection process. Our Global-then-Local State Space Block (GLSSB) integrates LESSM and IRSK with layer normalization (LN) as its core. This design enables MambaLLIE to achieve comprehensive global long-range modeling and flexible local feature aggregation. Extensive experiments demonstrate that MambaLLIE significantly outperforms state-of-the-art CNN and Transformer-based methods. Our code is available at https://github.com/wengjiangwei/MambaLLIE.

MambaLLIE: Implicit Retinex-Aware Low Light Enhancement with Global-then-Local State Space
Jiangwei Weng's
Zhiqiang Yan', Ying Tai Jianjun Qian', Jiin Yang', Jun Li
School of Computer Science and Engineering,
Nanjing University of Science and Technology. Nanjing. 210094, China
Schooo of Intelligence Science and Technology, Nanjing University, Suzhou, 215163, China (wengj iangwei, yanzq, cs jsjqian, csjyang junsi) werijjusttuuu.... edu.cn cn.
yingtaiōnju. edu.
Abstract
Recent advances in low light image enhancement have been dominated by Retinex- based learning framework, leveraging convolutional neural networks (CNNs) and Transformers. However, the vanilla Retinex theory primarily addresses global illumination degradation and neglects local issues such as noise and blur in dark conditions. Morcover, CNNs and Transformers struggle capture global degrada- tion due to their limited receptive fields. While state space models (SSMs) have shown promise in the long-sequence modeling, they face challenges in combining local invariants and global context in visual data. In this paper, we introduce MambaLLIE, implicit Retinex-aware low light enhancer featuring global- then-local state space design. We first propose a Local-Enhancee State Space Module (LESSM) that incorporates augmented local bias within 2D selective scan mechanism, enhancing the original SSMs by preserving local 2D dependen- cies. Additionally, Implicit Retinex-aware Selective Kernel module (IRSK) dynamically selects features using spatially-varying operations, adapting to varying inputs through an adaptive kernel selection process. Our Space Block (GLSSB) integrates LESSM and IRSK with layer normalization (LN) its core. This design enables MambaLLIE achieve comprehensive global long-range modeling and flexible local feature aggregation. Extensive experiments demonstrate that MambaLLIE significantly outperforms state-of state-of-the-art CNN and Transformer based methods. Our code is available at Project Page
State
1 Introduction
Low light image enhancement is challenging task in computer vision due to insufficient lighting and sensor degradation. Consequently, images often suffer from poor global visibility and local issues such as color distortion and noise. These degraded images can adversely affect human perception and related vision tasks, such as object detection and depth estimation [57].
Traditional techniques, such as histogram equalization [ and gamma correction 77. enhance images through global mapping operations. However, these global operations often struggle address local degradation effectively. In recent years, many methods based on CNNs and Transformers have gradually come dominate this field [53] 64 15 56 3) CNN-based methods [53] 64 15) 38 [5] have achieved significant advancements by effectively aggregating local information, thus substantially improving performance in low light enhancement. Nevertheless, the limited receptive field and weight-sharing strategy of CNNs result in a local reductive bias, making the models less
adaptive to varying inputs. On the other hand, Transformer-basee methods [56] 62 achieve
Corresponding author
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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SNR-Nel
Retinexformer
MambalR
Mambal
200
100
CNNTTansformer based Methods
Vision State Space based Methods
Figure 1: The Effective Receptive Field (ERF) visualization 34 for SNR-Net [56). Retinexformer 31. MambalR [16] and our MambaLLIE. A broader distribution of bright areas signifies a larger ERF. The receptive field of SNR-Net is large but messy, due the SNR-awar aware mechanism, Retinexformer achieves a larger receptive field of the central point, and MambalR has the global receptive field, but presents the limited local perception. Only our proposed Mambal.LII. achieves global perception ability outwards from central point and preserves the large local receptive field.
larger and adaptive receptive field by emphasizing long-term dependencies through the self-attention mechanism. However, the vanilla attention mechanism scales quadratically with input size, resulting in significant computational overhead.
Recently, State Space Models (SSMs) 10 28 have garnered significant attention in the field of computer vision. These internal state space models demonstrate great potential for global information modeling with linear complexity. However, straightforward implementation models for low light image enhancement is inadequate. This is because SSMs are primarily designed for long-range modeling and lack the flexibility needed to capture local information effectively [66]. For instance, illustrated in Fig. [ the receptive field of MambalR [16), a simple yet effective vision state space model, achieves longer-range dependencies compared to CNN and Transformer-based methods. Nevertheless, it falls short in refining local interactions.
vision state space
In this work, we introduce MambaLLIE, a novel framework for enhancing low light images that integrates implicit Retinex-aware approach within global-then-locat state space model. Mam- bal.LIF LIE not only explores the capabilities of state space models in low light image enhancement but also incorporates Retinex- structure providing both explicit and implicit guidance. Our framework introduces a unique global-then- -hec--occll state space block, enhancing global long-range degradation modeling and local feature aggregation through augmented state space. Additionally, we incorporate Retinex-aware selective kernel mechanism in the enhancement process, enabling adaptive modulation of illumination strength through specific spatial operations.
Our contributions and main findings can be summarized as follows: 1) We introduce novel global- then-local state space block that integrates a local-enhanced state space module and implicit Rewie selective kernel module. This design effectively captures intricate global and local dependencies. 2) We devise an implicit Retinex- selective kernel mechanism to guide deeper neural representations, eliminating the need for complex structural design and constraints to estimate physical priors, the prior feature tends to segregate into independent positive and negative illumination components before integrating them, a capability lacking in explicit methods. 3) Experimental results on benchmark datasets and real-world evaluations consistently demonstrate the superior performance of our proposed method compared to state-of-the-art approaches.
2 Related work
Low Light Image Enhancement. Nowadays, the existing deep learning- learning-based methods have mainly been categorized into end-to-end and Retinex-basse based methods [27]. To the best our knowledge, LLNet
33 firstly introduced deep neural network for low light image enhancement by supervised learning.
LightenNet 2 adopted the CNN for single image contrast enhancement. MBLLEN (35) proposed
the multi-branch fusion within CNNs to extract rich features. Besides, SNR-Net (6) Restormer
(2). I.LFormer [22] and SCENet (36] adopted the self-attention mechanism to achieve excellent
performance. However, all these end-to-end models mainly depend on the distribution of training

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dataset and ignore the inherent illumination prior. As contrast, ZeroDCE [5], RUAS [0] and subsequent works [8 9 [T] represent impressive solutions for image enhancement, ones precisely using physical priors to enhance the images. However, due to the absence of an ideal reference for guidance, these methods usually exhibit certain gap compared to supervised learning models.
As for supervised Retinex-based models, these methods aim to decompose the image into illumination and reflectance maps, and then enhance the image by optimizing these maps. For instance, Retinex- Net [53] divided image enhancement into decomposition, adjustment and reconstruction stages, which providing a good representation of image enhancement process. KinD 66 and URetinex-NG (55] further introduced the novel muulti-branee and multi-stage stage frameworks, respectively. However, striking a balance between complexity and efficiency remains challenging for these methods. Re- cently, Retinexformer [3] simplified a one-stage Retinex-basee low light enhancer with a efficient Transformer. Diff-Retinex [61] designed a mansformer-basee decomposition network and adopted generative diffusion networks to reconstruct the results. Overall, they typically applied the Retinex theory in direct way, which may limited for low light enhancement problem.
Vision State Space Model. State Space Model (SSMs) II
are emerging new sequence models for deep learning, which first swept the natural language processing (NLP) community such as language understanding [2),, content-based reasoning [66]. Recently, SSMs have also garnered considerable attention in computer vision (CV) tasks. To our knowledge. S4ND B9 first explored
state space mechanism into CV tasks by swapping Conv2D and self-attention layers with S4ND layers in existing models. VMamba [I] bridged the gap between ordered sequences and non-causal visual images, enabling the extension of vision selective state space model with global receptive fields. Vim 665 proposed the bidirectional state space modeling with positional awareness, achieved the global visual perception. Furthermore, Lacal [8] was focused on the local scanning strategy, preservation of local context dependencies. Efficient [41] designed a light-weight SSMs with an additional convolution branch to learn both global and local representational features. MambalR [6] employed convolution and channel attention to enhance the capabilities of the Mamba. But existing vision state space model do not pay enough attention on capturing local information, as vanilla SSMs are designed for long sequence and the invariant of local vision data is not taken into account in the existing vision state space models.
3 Methodology
This work aims to introduce novel implicit Retinex-aware low light enhancer with global-then-local state space. In this section, we revisit the Retinex theory and the state space model, offering concise overview. Following that, the details of our proposed MambaLLIE are introduced.
3.1 Preliminaries
Retinex Theory. The ideal Retinex theory 25 for low light enhancement assumes that the captured images can be decomposed into reflectance and illumination maps. Following [38 45). explicit Retinex-based methods emphasize estimating either an illumination map while regarding the re- flectance map the enhanced result, estimating concrete reflectance and illumination maps and then restoring the well-exposed images. Specifically, given low light image L E Hx x3 where H and W represent height and width respectively, the derived maps be denoted
L= R- N N L/I. N R
where- denotes the clement-wise multiplication. R E @#xW'33 x3 denotes reflectance map, a static property of captured objects; I HxW denotes illumination map; N E H W x3 denotes enhanced images; R. E Hx x3 denotes the estimated reflectance and illumination maps, respectively.
Consequently, the former assumption N L/Í disregards the noise and artifacts resulting from sensor degradation in the captured images, rendering pixel-wise illumination adjustments inadequate. The latter N = R I aims to restore the reflectance and illumination maps to enhance the images. However, this requires the design of multiple branches and constraints to guide the training (64]
State Space Model. The SSMs, such as structured state space sequence models (S4) 12 and
Mamba (10), can be regarded as the continuous linear time-invariant (LTI) systems
[54) Given an
3

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Image
Max Pricr
Mean Prior
Corv
Lager
3Le68
Lavet
Liwear
LEGOM
GLSS0
GL86a
Enhanced Space (LESSM)
Layyr Nom
UpBamppii
Relines-awre Selectin
GLSSS
IRSK
DWWOOTT
GELU
Gone
Skymoid
Churs
Oarpet
Glartal-lsnn-Laaal Space (GILSSH))
Pipoline
(c) Implicit
(IRSK)
The overall pipeline of the proposed Mam Our Global-then-Locaa State Space Block (GLSSB) integrates Local-enhanced state space module (LESSM) and implicit Retinex-awan selective kernel module (IRSK) with layer normalization its core, where the maximum and mean maps of low light images can be regarded as a rough illumination prior of GLSSB. Besides, The local-enhanced design essentially introduces the local invariance into state space model, which can integrate the existing directional scan with our local-enhanced term into state space.
Figure 2:
mie-dimension sequence x (t) E R, it projects into a new one-dimension sequence y (t) through the hidden state h (1) I the system be defined as linear ordinary differential equation:
(t))) = Ah(t) + Br(t), (t) Ch(t) Dz(t),
where m denotes the state size, A E R=xm B E Rmx1, C R Ixm and D E R denotes state, input projection, output projection parameters, and kernel selective parameters.
As the raw state-space models are continuous, the systems adopt the discrete versions before feeding the computer, in which the zero-order hold (ZOH) is used to transform the continuous parameters A and B to discrete parameters A and B as follows
exp (AA), B = (AA) (exp (AA) - I) AB,
where A denotes the step size. Overall, the discretized version can be rewritten as:
h Ah, Ba,,
Vt Che Dr,-
However, the current system remains static for varying inputs. To address this limitation, Mamba [10 introduces selective state space models, allowing parameters to adapt with the input, thereby enhancing selective information processing across sequences. This parameter selection mechanism can be expressed as:
B fB(E,,
C fc(a,), A A
where fB(*:), fe(x:) and fA(zz) are linear functions that broadens feature to the hidden state dimensions. As SSMs are tailored for long sequences. is limited in capturing complicated local information. As for visual data, VMamba [31]. Vim [65], etc., proposed the specific location-awas aware sean strategies to maintains the integrity 2D image structures. However, the specific directed sequences overlook the vision information of pixels neighborhood structure. Inspired by [66), we explore a glohal-then-loca state space, which receives the global perception before the details, supplementing the lack of local information.

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3.2 Overall Pipeline
We first present the overall pipeline of our MambaLLIE, an U-shaped architecture as shown in Fig. 2ia), which includes encoding and decoding parts with the convolutional downsampling and upsam- pling layers. The encoder features are concatenated with the decoder features via skip connections. Next, We propose global-then then-local state space block (GLSSB) the basic core of MambaLLIE, the max and mean priors concatenated with low light image are projected into GLSSB by convolutional layers. Therein, GLSSB is composed of the local-enhanced state space module (LESSM) and the implicit Retinex-aware selective kernel module (IRSK), interleaved with layer normalization.
x we employ a 3 x 3 convolution layer to project the neural features F E IR #X from input feature space, and then project features into each GLSSB, which will be described in Section 3.3. Besides, IRSK integrates original input, maximum prior Lmax E IR. HxW and mean prior Lmean E R as illumination prior Lp E RRxW
Illumination Prior. Given a low light image L E #xxx3
Lp = Concat (L, mL)n(L), max(L))
(6)
We first define F, is the output of GLSSB. Subsequently, the downsampling layer and following GLSSB achieve the feature extraction to acquire the deep feature, which can be denoted as F,, ## x # x2'C where i 0, 2. Moreover, the feature is later concatenated with the upsampling layer with symmetrical structure. Finally, using 3 3 convolution layer projects into F Fout R HRWW and the enhanced image can be expressed as N = Fou Fout + L.
3.3 Global-then-Local State Space Block
As illustrated in Fig. bb), GLSSB follows the LayerNorm, LESSM, LayerNorm and IRSK flow, motivated by Transformer 46 and Mamba(10) usage of similar structures in basic block. Given the input feature, it first undergoes the LayerNorm and LESSM to capture the local-enhanced global information. the above process can be denoted as:
M LESSM (LN (F) 1) + F
And then, another LN and our proposed IRSK used for Retinex-aware guidence. The above process can be formulated as:
F IRSK (LN (M)) + M.
(8)
Overall, at the prior module of GLSSB, we capture global dependencies using local-cnhanced SSM. Because the SSM is better at learning global information, the subsequent module aims to handle more refined and complicated local dependencies.
State Space Module. Existing state space models 8 12 10 excels at capturing the causal processing of input data in long range dependencies. However, the unidirectional sean manner encounters difficulties in vision data to modeling non-causal relationships. To accommodate vision data, [65] 31 41 process the input data from different 2D sean directions. However, these methods ignore the local invariants of vision data as shown in Fig. 2c). In other word, the fixed scanming methods widen the distance between neighborhood data and snarl the causal relationships.
The most SSMs B1 can be regarded as the continuous linear time-ine inveiinaaiann systems, we further introduce the (Lp) augmented local bias, enhancing the original SSMs by preserving local 2D dependency. Following [58 19,, we propose novel global-thens
state space:
Ah Bzi h = Ch+ + Da. + e (Lp).
(9)
where e (Lp) is independent of the hidden state space. Hence, the model can be computed in simple way, given a feature Fi-1 E XXCC and illumination feature Lp E HxG we adopts the LayerNorm followed by our proposed LESSM to integrate the spatial long-term dependency. Following [10, the input feature are chunk into F, and F2 in two branches. The first branch projects convolution, SiLU activation function,
the feature into a linear layer, followed by a depth-wise accompanied by our proposed augmented local bias and LayerNorm. In the second branch, the features is also projected to a linear layer followed by the SiLU activation function. Finally, features
5

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Table Quantitative comparisons on LOL- LOL- SMID, SDSD-indood and SDSD-
outdoor datasets. The best result is in red color while the second best result is in blue Color.
Methods
Ref.
PSNR
LOL-V2-real
SSIM
PSNK
LOL-V2-syn
SSIM
PSNR
SMID
SSIN
PSNR
SDSD-indoor
SSIM
PSNR
SDSD-ouldoor
SSIM
FLOPS
Complexity
RetineNNet
HMVC 2018
15.47
0.567
17.13
0.79%
22.83
0.684
20.84
0.617
20.96
0.622
587.47
0.34
DeepUPE
CVPR 2019
13.27
0.452
15.08
0.623
23.91
0.690
21.70
0.662
21.94
0.698
1.02
SID
ICCV 2019
13.24
0.442
15.04
0.610
24.78
0.718
23.29
0.703
24.90
0.693
13.73
7.76
KinD
MM 2019
14.74
0.641
13.29
0.578
22.18
0.634
21.95
0.672
21.97
0.654
34,99
8.02
MIRNet
ECCV 2020
20.02
0.820
21.94
0.876
25.66
0.762
24.38
0.864
27.13
0.837
31.76
EnGAN
TIP 2021
18.23
0.617
16.57
0.734
22.62
0.718
23.29
0.703
24.90
0,693
61.01
114.35
Restormer
CVPR 2022
19.94
21.41
26.97
0.758
25.67
0.827
24.79
SNR-Net
CVPR 2022
21.48
0.849
24.14
0.928
28.49
0.805
29.44
0.894
28.66 28.66
0.866
26,35
4.01
QuadPrior
CVPR 2024
20.48
0.811
16.11
0.758
15,50
0.604
22.22
0.783
18.26
0.662
MambalR
ECCV 2024
21.25
0.831
25.55
0.929
27.07
0.774
28.97
0.884
29.75
0.861
60.66
4,30
Retinexformer
ICCV 2023
22,80
0.840
25.0 67
0.930
29. 15
0.815
29.77
0.896
29. 84
0,877
15.57
1.61
MamboLLIE
22.95
0.847
25,8
0.940
29.26
0.818
30.12
0.900
30.00
0.869
from the two branches are aggregated with the element-wise product and then are projected back to input feature space by linear layer. The entire process can be delineated as:
Fi LN (2DSSM (SiLU (DWConv (Linear (F1)))) + Conv(Lp)),
F2 =
SiLU (Linear (F2)).
(10)
our = Linear
(F, O F
Implicit Retinex-Aware Selective Kernel Module. In our framework, we propose Retinex-aware kernel selective mechanism (IRSK), where two coupled Retinex-aware priors are used to select the spatial context regions, the maximum and mean values of RGB images can be regarded illumination prior. IRSK constructs a sequence of depth-wise convolutions with an alterable kernel to select the feature with different receptive field, using a spatial selection mechanism by illumination prior. Inspired by LSKNet (29), for each of the feature maps from different selective kernel, a Sigmoid activation function is applied obtain the individual illumination maps from illumination prior. Fig. 2(d) shows a detailed conceptual illustration of IRSK module where we intuitively demonstrate how the implicit Retinex-aware module works. The above process can be formulated as:
rough
F
F out:
F Fx+1
JDwcony
F
(11)
The output of the Retinex-aware maps are concatenated with the input features via residual connec- tions, followed by depth- wise convolution, GELU activation function and convolution layer.
{S,, S2} = Chunk (Sigmoid (Conv (Ip))).
(12)
K
Fg = CowCCoCCCCa (BGLLUUDDDCon
Σ
F.S. + F F).))).
(13)
4 Experiments
4.1 Benchmark Datasets and Implementation Details
Datasets. We employ five paired low light image datasets for evaluation, including LOL-V2-reall
[9. LOL-v2-syn [59] SMID 4.. SDSD-indoor [48] and SDSD-outdowr [48] datasets. Therein, LOL-V2-real contains 689 low-normal light paired images for training and 100 pairs for testing;
LOL-V2-syn includes 900 paired images for training and the 100 pairs for testing; Besides, SMID is composed of the 15763 short-long exposure paired images for training and the remaining images for testing; SDSD-indoos and SDSD-outdoor are all subsets of SDSD dataset (the static version), which extract the paired images from 62 and 116 pairs for training, and the left 6 and 10 pairs for testing.
Implementation Details. We implement MambaLLIE in PyTorch 40 on server with the 4090GPUs. Random cropping the image pairs into 128 x 128 patches as training samples, data augmentation is performed on the training samples such as rotation and flipping. The batch size is 8. In terms of optimization procedure, Adam (24 is adopted as the optimizer with 81 = 0.9 and 32 = 0.999; The training iterations is set to 1.5 10 The initial e learning rate set to 2 x 10-1 and steadily decreased by by the cosine annealing scheme. The loss criterion is mean absolute envor (MAF), thus peak signal-to- ratio (PSNR) and structural similarity (SSIM) (22) selected as the evaluation metrics for the paired datasets.

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Inpu
KmnD
Retinesfomee
QuadPtioe
MambaiR
Ous
GT
Figure 3: Qualitative comparison with previous methods on LOL-V2-real and L.OL-V2-syn datasets. Our MambaLLIE effectively enhances the illumination and preserves the color.
Inpur
SNR-NN
Reincsfomic
QuadPrior
MambelR
GT
Figure Qualitative comparison with previous methods on SMID, SDSD-indoor and SDSD-outdow datasets. Our LambaLLIE restore the texture and color under challenging degradation, such as the wooden bench and reflective glass.
4.2 Main Results on Benchmarks.
Quantitative Comparison. As shown in Tab. evaluated the performance of Mambal.L.t against 11 SOTA image enhancement methods, including RetinexNet [53], DeepUPE [49], SID 4
KinD 164) MIRNet (63), EnGan [21]. Restormer [62]. SNR-Net (56), QuadPrior (51). MambalR []]
and Retinexformer 3. Our MambaLLIE demonstrates superior performance than SOTA methods on the adopted benchmark datasets in terms of PSNR and SSIM, while achieves comparable results of SSIM with the SOTA methods in LOL-V2-real and SDSD-outdooy outdoor. Therein, when the parameters are roughly similar, our MambaLLIE achieves an average improvement of 0.2dB on benchmark datasets compared to the Transformer based SOTA method, i.e. Retinexformer. Compared with the earlier Transformer based SNR-Net, Mambal.L.I outperforms by average 1 PSNR on the all datasets. When compared to the MambalR, Mambal achieves 1.7, 0.32, 2.19, 1.15 and 0.25dB PSNR improvements on the adopted datasets, respectively. Besides, Our MambLLIE gains the improvements over 7 dB all datasets than traditional Retinex-based models, such as RetinexNet, DeepUPE and KinD.
Qualitative Comparison. Figs. the SOTA methods and traditional Retinex-base models. Existing methods suffer from insufficient illumination and fail to restore the details as shown in Fig. 3 As we can see, color distortion and image degradation also affect the enhanced results previous methods Fig. 4 yet our MambaLLIE not only enhances brightness but also faithfully preserves colors with reference to ground truth images, all while restoring the details.
report the vision results for comparing our method with latest
4.3 Real World Experimental Evaluation
Enhancing low-light images in real-world scenarios is exceptionally challenging because, to benefiting downstream tasks such as dark object detection, the enhanced images must also be visually pleasing to human perception.
in addition
7

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Liiht
Light
(e) Visuzl comparison of object detection
Visual comparison face
Low Light Image
Minital:1
Toy example study
Visual consparison of uupaired cattesct
Figure 5: Visual comparison of our MambaLLIE with recent SOTA methods. (a) Qualitative comparison object detection, (b) Qualitative comparison on face detection, (c) Toy example of user study, (d) Qualitative comparison of unpaired dataset.
Table 2: Low light object detection results on the ExDark dataset. The best result is in red color while
the second best result is in blue color.
Methods
Bicycle
Boat
Bottle
Bus
Car
Cat
Chair
Cup
Dog
Motor
People
Table
Mcan
RetinexNet
0.790
0.741
0.743
0.908
0.820
0.665
0.651
0.750
0.721
0.703
0.784
0.556
0.736
EnGAN
0.733
0.710
0.687
0.892
0.786
0.675
0.656
0.650
0.741
0.657
0.731
0.528
0.704
KinD
0,800
0.721
0.788
0.919
0.822
0.718
0.672
0.771
0.775
0.736
0.803
0.555
0.757
ZeroDCE
0,806
0.750
0.762
0.914
0.837
0.681
0.677
0.769
0.788
0.728
0.801
0,535
0.754
SCI
0.821
0.742
0.749
0.916
0.846
0.695
0.690
0.784
0.756
0.758
0.810
0.555
0.760
SNR-Net
0.802
0.721
0.750
0.932
0.840
0.694
0.677
0.758
0.763
0.755
0.789
0.559
0.753
Retinexformer
0.8009
0.769
0.753
0.914
0,814
0,688
0.689
0.763
0.766
0.769
0.805
0.543
0.757
MambalR
0.803
0.763
0.752
0.903
0.830
0.687
0.684
0.761
0.721
0.738
0.813
0.556
0.751
Mambal.IJI
0.779
0,846
0.701
0.692
0.800
0.751
0.812
0.560
0.768
Table 3: Face detection results on the Dark face dataset.
The best result is in red color while the
second best result is in blue color.
Methods mAP
Low light image
SCT
Retinexformer
MambalR 0,482
Ours 0.491
0.461
0.483
0,486
Low Light Object Detection. We utilized ExDark dataset 32 to compare the enhancement of preprocessing methods for high-level vision tasks. There are 7363 challenging low light images annotated with 12 bounding box classes, of which 5,890 for training and 1,473 for testing. Note that all supervised methods were pretrained on the LOL-V2-syn dataset, the low light image underwent different enhancement methods and then finetuned YOLOv3 43 as the object detector. As shown in Tab. 2 our methods achieved the best average result compared with other models, and yielded the best results on Car, Chair, Cup, People and Table classes. Fig. 5)a further reported the visual comparison, compared with suboptimal preprocessing method SCI, detector through our MambaLLIE can detect the objects in extreme dark regions including two persons and a chair, while other methods failed.
Face Detection We investigate the performance of low-light image enhancement methods on face detection the dark. We use the DARK FACE dataset [60] and randomly sample 300 images for evaluation. The RetinaFace methods. We show the results of different methods Fig. 5(b) and Tab. 3 In general, MambaL achieves the better mAP score and visual detection result. Please note that the effectiveness of face detection in low light conditions depends not only on the quality of the enhancement results but also on the specific face detection algorithm employed. We utilize the pre-trained RetinaFace model to assess the performance of different low light image cnhancement methods to some extent.
44 is used as the face detector and fed with the results of different LLIE
User Study. We conducted user study to evaluate the human visual perception quality of the enhanced results challenging scenarios. Due to the lack of the ideal reference for training, we selected the pretrained model from the benchmarks to enhance the photos. There are 7 random selected low light images from the benchmarks and ExDark datasets under different lighting conditions. Human perception primarily focuses on the presence of global visual effect, local detail, color distortion (noise), which significantly reflect the quality of the enhanced images. Thus, We assigned ratings on scale of I (worst) to 5 (best), evaluating the quality of the enhancements in terms of overall rating, local detail and color distortion(noise), respectively. Overall, 70 participants were invited to assess the visual quality. The average scores reported in Tab. 4 our MambaLLIE
8

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Table 4: User study on the challenging low light image enhancement.
Methods
RetinexNet
ErGAN
SCI
QuadPrior
SNR-Net
Retinexformer
MambalR
MambaLLIE
Overall Rating
3.093
3.314
3.943
3.014
3.821
4.100
3,857
4.243
Local Detail
2.871
3.143
3.686
3.129
3.779
3,950
3.629
4.129
Artifacts and noise
2.914
3.164
3.776
2.929
3.657
3,971
3,750
4.100
Table 5: Perceptual evaluation results on the unpaired datasets
LIME
VV
NPE
MEF
DICM
Methods
MUSTQ
NIMA
MUSIQ
NIMA
MUSIQ
NIMA
MUSIQ
NIMA
MUSTQ
NIMA
Retinexformer
58.66
4.85
58.96
4.63
56.98
4.81
54.27
4.89
35.69
4,84
MambalR
56.31
4,70
59.29
4.70
56.38
4.75
53.84
4.88
57.01
4.84
DiffLI
55.39
4.66
58.62
4.55
53.54
4.65
52.14
4.91
55.77
4.82
Ours
58.42
4.86
60.22
4.78
56.70
4.82
$5.01
4.93
57.16
4.92
achieves the best score in the involved voting aspects. Fig. (5() shows the toy example of user study, which display the input and the random enhanced results and local details by different algorithms.
Perceptual Evaluation. We compare two non-reference perceptual metrics, MUSIQ [23] and NIMA [6]. on five unpaired datasets, including LIME [77. VV 47). NPE [50]. MEF 37I. and DICM [26]. Experimental evaluations in Tab. show the superiority of our method over SOTAS with better perceptual evaluation in most comparisons, in terms of the NIMA scores, our method also achieves competitive results across all datasets. Experimental comparisons against the diffusion-based model DiflLL (20] also underline our method's robustness. These results are visually supported in Fig. ()d), where our method demonstrates qualitative improvements that align well with its perceptual metrics. Overall, the evaluations illustrate the superiority of our enhancement approach, achieving enhanced perceptual quality and setting a new LLIE benchmark on unpaired datasets.
4.4 Ablation Study
Implicit Retinex-Aware Framework. We compare the improvement of using a implicit Retinex- aware model with the end-to-end and explicit Retinex-awart models. Specifically, Baseline- I is simple variant of our MambnaLLIE by removing Retinex-awe aware guidence, directly uses the standard vision state space module (VSSM) to process flattened vision data in our proposed UNet-shaped framework. following the Norm > VSSM + Norm + channel attention layer flow as referenced in 16.. Baseline-2 is designed to estimate the illumination map and then light up the low light image by element-wise multiplication, namely aims to estimate the illumination map instead of directly predicting the enhanced image, and then restores the enhanced result by N = R. Σ. Tab. reveals our implicit Retinex-Aware framework significantly outperforms Baseline-1 1 with the improvement of 1.25dB PSNR, while achieving a PSNR enhancement of 1 1.00 dB compared to Baseline-2 on SDSD-indoor dataset.
Global-then-Local State Space. As the core component, our GLSSB comprises the LESSM and IRSK. We demonstrate the effect of each component through ablation study. For example, The results on EDSD-indoor dataset, presented at Tab. [6] indicate that our LESSM achieves improvements of 0.33 dB and 0.08 dB in PSNR compared to Baseline-1 1 and Baseline-2, respectively, which utilize vanilla state space blocks. Additionally, our IRSK produces PSNR enhancements of 0.96 dB, 0.74 dB, and 0.63 dB compared to Baselines and when only applying LESSM. Our full version indicating that although LESSM improves the vanilla SSM with local enhanced modeling ability, IRSK should be considered for further improvements, when GLSSB integrats LESSM and IRSK, our Mambal achieves the highest PSNR and SSIM.
Selective Kernel Behavior. We further investigate the kernel selection behavior in our MambaL as shown in Fig. 6 We find the implicit Retinex-aware selection pattern tend learn two independent positive and negative illumination, resulting in complementary features. Compared with explicit Retinex-bar based methods, our IRSK can guide from a flexible deeper neural representation. The quantitative results are reported in Tab. 7 Different with LSKNet [29], we put small kernels in front and larger kernels higher levels. This is because object detection needs larger receptive field, thus adopts sequence of depth-wie wise convolutions with growing kernel and increasing dilation, while has to introduce a lots of padding. But image enhancement may suffers from padding operation at the edge of the image, especially upsampling further expands the padding values. Thus, the the former

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Table 6: Effects of design choices.
L.O--V2-real
SDSSD-indoor
SDSD-ooudoor
Methods
Params(M)
FLOPS(()
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
Baseline-1
2.14
18.39
22.06
0.834
28.87
0.865
28.86
0.852
Baseline-2
2.14
18.39
21.28
0.812
29.12
0.862
28.96
0.841
Ours w/o LESSM
2.26
20.64
21.83
0,846
29.83
0.889
29.20
0,866
Ours w/o IRSK
2.19
19.94
22.37
0.845
29.20
0.887
28.97
0.857
Ours
2.28
20.85
22.95
0.847
30.12
0,900
30,00
0.869
Table 7: Effeets IRSK.
Kernel Sizes
Params
FLOPS
PSNR
SSIM
3+3
2.25
20).47
29.55
0.899
5*55
2.31
21.23
29.48
0.896
5*7
2.35
21.79
28.88
0.892
5*3
2.28
20.85
29.31
0.892
3*5 (Ours)
2.28
20.85
30.12
0.900
3x3
x
5
5+3
x3
Chegse
Comt somprtttii
K
PSNR
25:80
74
21.77
26:13
28.11
Figure 6: The details of selective kernel behaviour, the LAM visualization 14 demonstrates influence of similar local information is higher than that of global dependence, our eccal-en strategy underscores the feature. Besides, the larger receptive fields can provide globally consistent results.
small kernels can quickly focus on local information and the the latter kernels contain larger receptive fields for better feature fusion.
5 Limitation and Discussion
We adopt an implicit Retinex- guidance within a global-then-lon state space framework to address global insufficient illumination and local degradation for low light enhancement. However, our approach has several limitations. 1) Unlike end-to-end methods, our technique requires the design of reasonable illumination prior, which relies on prior experience. 2) Most existing enhancement models, including ours, primarily focus on mean square error and use PSNR and SSIM to evaluate image quality. To mitigate inherent biases these metrics, we conducted additional real-world experimental evaluations to reconcile the bias and further validate the effectiveness of our approach.
6 Conclusion
In this paper, we introduced novel state space- based model, MambaLLIE. Our proposed core of GLSSB effectively combines global and local information by implicit Retinex-aware selective kernel into global-then-lee face detection, user study and perceptual evaluation demonstrate that our framework consistently achieves the best performance. Our future work is to address the dual challenges of local redundancy and global dependencies in low light video enhancement via efficient state space modeling. Broader impact and more visual results, please refer to Appendix A and Appendix B
state space. Extensive experiments on benchmarks, low light object detection,
10

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Acknowledgements
This work was partially supported by the National Science Fund of China, Grant Nos. 62072242 and 62361 166670. We sincerely appreciate the valuable feedback provided by the anonymous reviewers.
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A Broader Impact.
Low light image enhancement is the classical task that improves the quality of degraded images, exhibiting the promising value of research and application. Our proposed global-then-local state space enhances the feature extraction ability by integrating implicit Retinex-aware strategy. We believe our method has the potential advance other low-level tasks and may inspire future research state space models. However, there could be negative effects brought by the proposed method. For example, the inevitable deviations of training data distribution, the generated results for the real world scenarios may exist the color deviation.
B
More Results.
Low light input
QuadPrior
RetinexFormer
S
MambaLLIE (Ours)
SeR-Net
MambalR
Mambal LLE
M $
Low light input
QuadPrior
RetinexFormett
#
+ 34
#
+u
MambaL..IE (Ours)
SNR-Net
MambalR
MambaLLIE
TR
iR
Low light input
QuadPrior
RetinexFormer
12
i
TR
MambaL LIE (Ours)
SNR-Net
MambalR
Mambal IIIIF
Low light input
QuadPrior
Retinex Former
MambaLLIE (Ours)
SNR-Net
MambalR
MambaLLIE
Figure 7: More qualitative comparisons with SOTAs. (Zoom in for best view)
15

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Low light
QuadPPour
Remner
Monbaal LIE (Cun)
SNR Net
Mamball
MambalLiE
Low light rnpur
QuadiPnor
Kennesd ommer
MambaLLIE (Ous)
SNR- Net
ManbaiR
MambalLIE
Low lisht impst
QuanlFaaw
Resine. ComnccFomeer
MambaLLIE (Cus)
SNR-Ne
Maniailt
MambalLIE
Pap
QuaalPour
MantaLLIE
SNR.Ne
Mantall
ManbulLIE
Figure 8: More qualitative comparisons with SOTAs. (Zoom in for best view)
Figure 9: Object detection qualitative comparisons with SOTAs. (Zoom for best view)
16

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Low
SC1
Low
S011
SC1
ManbalR
La
ManhalR
MambuLLIB
Reiieeffene
MumbalLIE
Figure 10: Qualitative comparisons on face detection performance. (Zoom for best view)
Figure 11: Qualitative comparisons of MambaLLIE and DifILL. (Zoom in for best view)
MambaLLIE
DiffLL
MambaLLIE
Retinex former
}
MambaLLIE
MambalR
Figure 12: Qualitative comparisons on unpaired datasets. (Zoom in for best view)
17

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Reviews:
[{"review_id": "TMzLxlWnTg", "reviewer": "Reviewer_ahrr", "summary": "This paper introduces a low-light image enhancement module (MambaLLIE). This module has a U-shaped structure and each GLSSB block follows the Transformer-based design. LESSM is proposed to capture the spatial long-term dependency. IRSK is proposed to introduce large and selective kernels for enhancing feature-capturing ability. IRSK also introduces illumination guidance by utilising attention mechanisms.", "questions": "1. Could this paper provide an analysis or explanation of why MambaLLIE has a larger receptive field in terms of global and local?", "limitations": "This paper discusses limitation in Sec 5 and give potential solutions.", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The usage of the state space model and large selective kernel greatly increases the effective receptive fields. By capturing longer dependency on features, the network can generate better outputs.\n\n2. MambaLLIE achieves state-of-the-art results on low-light image enhancement tasks across various datasets and evaluation methods.", "weaknesses": "1. Could this paper state the difference between LESSM and the Vision State Space Module in [14]? It seems these two blocks have similar designs.\n\n2. The core block GLSSB appears to be a combination of [14] and [23]. However, since [14] is proposed for image restoration tasks, this may limit the novelty of this paper.\n\n3. The descriptions of baseline-1 and baseline-2 are not clear. \n\n4. In Section 4.4 Ablation Study, which dataset is used for the ablation study? How about other datasets? If the dataset used in the ablation study is SDSD-indoor, then the baseline-1 result (PSNR 28.87, SSIM 0.865) is quite similar to [14] (PSNR 28.97, SSIM 0.884), which may indicate that the better performance of this paper relies on Retinex-aware guidance rather than a larger receptive field. However, Retinex-aware guidance has already been explored in [3] and \"Low-Light Image Enhancement with Multi-stage Residue Quantization and Brightness-aware Attention.\"", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Q1: The difference between LESSM and VSSM.**\n\n**A1:** Thanks for your comment, we would like to rebut that the **MAIN** innovation presented in LESSM is the integration of local 2D dependencies into VSSM and formulate a simple yet efficient local-enhanced state space module (LESSM) to aggregate both local and global information. We **formulate a novel state space** in low light image enhancement community as equation 10, while MambaIR merely introduces an additional convolution after the vanilla VSSM to restore neighborhood similarity. Ours local-enhanced design essentially introduces the local invariance of convolution on state space model, which integrates the directional scan with local-enhanced into VSSM.  This technique is depicted in Figure 2 (c). Besides, our local-enhanced term can be regarded as a local consistency constraint for the state space for vision data, guiding the module to learn neighborhood features and thus enhancing robustness. Similar motivation in state space has been discussed in the literature [47], [C1]. \n\n[C1] Pfeffermann, Danny, and Richard Tiller. Small-area estimation with state–space models subject to benchmark constraints. Journal of the American Statistical Association 101.476,1387-1397, 2006.\n\n**Q2: The novelty of GLSSB.**\n\n**A2:** We argue that the GLSSB is novel in terms of _*the new exploration of the vision state space model*_ and _*technical improvements for the Retinex-aware low light enhancement task*_.\n\nThe low-light enhancement task faces challenges of global color degeneration and local noise disturbance. Our motivation aims to take into account both global and local image restore. Hence, Our GLSSB mainly follow the _*Norm - LESSM - Norm - IRSK*_ flow. Firstly, leveraging the long-range dependency modeling of Mamba [8] with linear complexity, we inherited the global modeling capabilities of VSSM, yet we find that existing vision state space model does not pay enough attention on capturing local dependencies. As noted in **Q1**, our LESSM significantly differs from VSSM [14]. \n\nOur IRSK is introduced after LESSM to selectively aggregate Retinex-aware features, which is a structure design based on physical priors for low light enhancement tasks. Specifically, Our IRSK introduces the Retinex-aware prior to select the spatial context regions, primarily inspired by the selective kernel mechanism as referenced in [23]. However, we would like to point that large selective kernel mechanism is **not suitable** for low-level vision tasks. This is because that the padding operation required for expanding a single large depth-wise kernel generates massive invalid information at the edges of feature maps, which inevitably has a negative impact on image restoration. Additionally, the large selective kernel mechanism uses self-aware spatial kernel selection, which limits the network’s ability to focus on physically prior-aware spatial context regions. Given the global modeling ability of our LESSM, we focus on local selective modeling in our IRSK. Our designed Retinex-aware prior only requires a smaller kernel to learn the spatial features, thereby avoiding excessive padding operations and highlighting the Retinex-aware interested regions.\n\nTherefore, we believe our novelty and contribution is on the par with the SOTA methods.\n\n**Q3: The descriptions of baseline-1 and baseline-2.**\n\n**A3:**  Baseline-1 directly uses the standard vision state space module (VSSM) to process flattened vision data in our proposed UNet-shaped framework, following the Norm → VSSM → Norm → channel attention layer flow as referenced in [14] for image denoising tasks. Baseline-2 introduces retinex thory \\(L=R / I\\) to Baseline-1, where \\(I\\) denotes the low light image and \\(R\\) denotes the reflectance (enhanced image), \\(I\\) is the illumination map. Baseline-2 aims to estimate the illumination map instead of directly predicting the enhanced image, and then restores the enhanced result by \\(R=L/I\\), as referenced in [13],[43],[45].\n\n**Q4: About Ablation Study.**\n\n**A4:** Sorry for the confusion caused. The dataset used in the ablation study is SDSD-indoor in previous manuscript. As restated in Q3, Baseline-1 has a similar structure to [14], hence its results (PSNR 28.87, SSIM 0.865) are comparable to those of [14] (PSNR 28.97, SSIM 0.884). Compared with Baseline-1, the improved performance when using our model without IRSK benefits from LSEEM. Our results without LESSM indicate the superior performance of IRSK compared to the vanilla channel attention layer [14]. Meanwhile, we supplement the ablation study with tests on two other benchmark datasets to compare the improvements of LESSM and IRSK. Please refer to the Table R2. \n\n**Q5: Why MambaLLIE has a larger receptive field in terms of global and local.**\n\n**A5:** We would like to answer this question from two perspectives: 1) **LESSM**.  Our proposed LESSM addresses this drawback by introducing a local-enhanced term into the VSSM, which retains the global modeling capability and improves the local receptive field. 2) **IRSK**. Our IRSK further enlarges the local receptive field through the Retinex-aware selective mechanism. As pointed out in [23], [C2], selective kernels can capture context information by adaptively adjusting their receptive field sizes according to the input. In our MambaLLIE, our LRSK has a comparatively smaller kernel than SKNet and LSKNet, but combining LESSM with LRSK achieves a larger receptive field in terms of both global and local information.\n\nAs shown in Figure 1 (paper), compared with MambaIR, our MambaLLIE indeed has a larger local receptive field, which is an improvement over VSSM. Additionally, ours have a larger receptive field in terms of both global and local information compared to recent Transformer-based methods.\n\n[C2] Xiang Li, Wenhai Wang, Xiaolin Hu, and Jian Yang. Selective Kernel Networks. In CVPR, pages 510-519. IEEE, 2019.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "IA27bjYHpC", "reviewer": "Reviewer_DUEm", "summary": "The authors proposed a Mamba-inspired method for LLIE, which is designed to address some challenges of the existing method, MambaIR. By integrating GLSSB consisting of LESSM and IRSK, the authors could make the model capture a large local receptive field while preserving global understanding natures. The overall pipeline is explained in detail and motivated by sufficient reasoning. MambaLLIE achieved SOTA performance on several benchmarks, significantly outperforming prior works.", "questions": "Although the ERF visualization in Figure 1 is impressive, the authors are expected to provide some numeric results for quantitative performance comparisons. \nIt is not clear why it is called \"retinex-aware selection\" in Figure 2(d). \nThere are several methods dedicated for face detection in low light. It will be interesting to compare face detection performance compared to such methods,", "limitations": "The authors adequately addressed the limitations; but some complexity analysis will be helpful for readers to understand the method.", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The overall pipeline is explained in detail and motivated by sufficient reasoning. MambaLLIE achieved SOTA performance on several benchmarks, significantly outperforming prior works. Considering emerging attention on Mamba for low-level vision tasks, the proposed work is timely and can thus attract attention to the community.", "weaknesses": "The proposed method is somewhat incremental in that it makes small (although relevant) changes to MambaIR. Since this is not the first work that introduces Mamba for LLIE, a more in-depth analysis and exploration is expected; however, the proposed is a slightly better engineered network structure. The authors are recommended to discuss MambaIR in more detail and clarify technical contributions over MambaIR.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Q1: Technical contributions over MambaIR.**\n\n**A1:** We want to emphasize that our target and contribution are distinct from MambaIR. 1). Our MambaLLIE is specifically designed for the low-light enhancement (LLIE) task, whereas MambaIR is proposed for image restoration, including image super-resolution (SR) and denoising tasks. Our quantitative and qualitative comparisons indicate that MambaIR exhibits suboptimal performance in the LLIE task. 2). Our aim is to leverage global and local context dependency in the state space module, *while MambaIR merely introduces an additional convolution after the vanilla VSSM to restore neighborhood similarity.* Our local-enhanced design essentially introduces the local invariance of convolution on state space model, which integrates the directional scan with local-enhanced into VSSM, and propose a novel state space. By additionally convolving 2D feature into directional scan, our method ensures a closer arrangement of relevant local tokens, enhancing the capture of local dependencies. This technique is depicted in Figure 2 (c). Next, we follow the selection mechanism by parameterizing the VSSM parameters based on the input, allowing the model to filter out irrelevant information and remember relevant information indefinitely, as pointed in [8]. Our proposed LESSM brings new insights into how to aggregate local context features in existing VSSM, addressing this gap and achieving significant improvements in LLIE and even SR tasks. Besides, our IRSK is a novel block designed for the LLIE task, its Retinex-aware prior selects the spatial context regions, primarily inspired by the selective kernel mechanism as referenced in [23]. The ablation study indicates that our IRSK shows better performance than the channel attention layer adopted in MambaIR.\n\n**Q2: About ERF visualization.**\n\n**A2:** Thanks for your positive comment and worthy suggestion. Most existing models, including ours, primarily depend on visualization results to understand the receptive field.  We further statistics the results as follow\n\n| Method| Global Mean Brightness | 20*20 Center Region Mean Brightness |\n|--------|-------------------------|-------------------------------|\n|SNR-Net| 97.59                   | 180.11                        |\n|RetinexFormer| 90.72                   | 187.00                        |\n|MambaIR| 94.08                   | 163.99                        |\n|MambaLLIE| 98.10                   | 198.42                        |\n\n\n**Q3: It is not clear why it is called \"retinex-aware selection\".**\n\n**A3:** In our framework, we propose a Retinex-aware kernel selective mechanism, where two coupled Retinex-aware priors are used to select the spatial context regions, each with a different receptive field. Hence, we term it as \"Retinex-aware selection\" in our framework. According to [13], [43], [45], [C3], illumination is a key prior for low-light enhancement, and the maximum values of RGB images can be regarded as a rough illumination prior. We use it in an implicit manner instead of restoring the illumination map or using a single illumination feature for feature guidance.\n\n[C3] Guo X. LIME: A method for low-light image enhancement. In ACM MM. pages 87-91. 2016.\n\n**Q4: Face detection performance.**\n\n**A4:** We investigate the performance of low-light image enhancement methods on face detection in the dark. We use the DARK FACE dataset and randomly sample 300 images for evaluation. The RetinaFace [C4] is used as the face detector and fed with the results of different LLIE methods. We show the results of different methods in Figure R2 and Table R3. In general, MambaLLIE achieves the better mAP score and visual detection result. Please note that the effectiveness of face detection in low-light conditions depends not only on the quality of the enhancement results but also on the specific face detection algorithm employed. In our evaluation, we utilize the pre-trained RetinaFace model to assess the performance of different low-light image enhancement methods to some extent.\n\n[C4] Sefik Serengil and Alper Ozpinar. A Benchmark of Facial Recognition Pipelines and Co-Usability Performances of Modules. In Journal of Information Technologies, volume 17, number 2, pages 95-107, 2024.\n\n**Q5: Complexity Analysis.**\n\n**A5:**  In the domain of low-light image enhancement, the primary challenges encountered include global color degeneration and local noise disruptions.  To address these issues, we have integrated the Mamba architecture into our framework, which excels at capturing the global dependencies of input data to restore. While Mamba-based models have demonstrated commendable performance in tasks such as image super-resolution and segmentation, with linear or near-linear scaling complexity, they tend to yield less than optimal performance in  low-light enhancement task. This limitation is largely attributed to the current state space models’ inadequacy in capturing local dependencies, which is crucial for detailed restoration. Moreover, conventional end-to-end models typically fall short in low-light tasks due to their neglect of illumination priors. To overcome these shortcomings, we introduce two innovative components: the local-enhanced state space module (LESSM) and the implicit retinex-aware selective kernel module (IRSK). These modules are specifically designed to effectively model both local and global degradations within our framework.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "Pmc31QsRWU", "reviewer": "Reviewer_1bAi", "summary": "This paper presents MambaLLIE, an implicit Retinex-awre low light image enhancement framework with modified state space blocks. Specifically, a global-then-local state space block (GLSSB) is designed, which incorporates a local-enhanced state space module (LESSM) and an implicit Retinex-aware selective kernel (IRSK) module. By enhancing the original SSMs with local bias, the proposed MambaLLIE outperforms state-of-the-art CNN and Transformer-based methods.", "questions": "Please refer to the `Weaknesses'.\n\nMoreover, it appears that the color in the last column of Table 2 is incorrectly marked.", "limitations": "The authors have discussed the limitations in the paper.", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1.\tThe main idea of this paper is well illustrated.\n2.\tExperimental results and user studies demonstrate that the paper achieves better performance than the compared approaches.", "weaknesses": "1. The novelty of this paper is limited. Like many Transformer-based works that embed local modeling capabilities in ViTs, the main contribution of this paper is incorporating local dependencies into SSMs. However, the advantages of the proposed GLSSB compared to ViTs and original SSMs have not been thoroughly analyzed. Furthermore, given the authors' design, the proposed GLSSB should have advantages in many other visual tasks, which need to be demonstrated through more experiments.\n\n2. The writing of this paper needs improvement as there are many unclear descriptions. For example, how is the illumination prior in the paper obtained, how does the proposed IRSK module work specifically, and what are the specific settings for the different variants in the ablation study?\n\n3. Some state-of-the-art works have not been compared (e.g., DiffLL[1]), and the experimental results in this paper do not seem to outperform them.\n\n4. As the low-light enhancement task typically encounters issues like color distortion or other visual artifacts, the authors should compare more perceptual evaluation metrics. Merely reporting PSNR and SSIM is not sufficient.\n\n[1] Jiang et al, Low-Light Image Enhancement with Wavelet-based Diffusion Models. Siggraph asia 2023.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Q1: About novelty and the advantages of GLSSB.** \n\n**A1:** We argue that the GLSSB is novel in terms of _*the new exploration of the vision state space model*_ and _*technical improvements for the Retinex-aware low light enhancement task*_.\n\nOur **Motivation** aims to take into account both global and local image restore. As we know, the low-light enhancement task faces challenges of global color degeneration and local noise disturbance. Global color degeneration suffer from the decrease of illumination, which tends to introduce a global-aware estimator, while the local degradation may contains the large or small regions. Hence, we intuitively introduce Mamba [8] into low light enhancement task, which excels at capturing the global dependencies of input data to restore. Compared with previous CNN and Transformer-based methods, Mamba-based model has exhibit promising performance with **linear or near-linear scaling complexity** in image super-resolution, image segmentation, etc., but still has the **suboptimal results** in low light enhancement task. We posit that the existing state space model is limited in modeling local dependencies, leading to a failure in restoring details. Besides, end-to-end models may exhibit suboptimal performance in low-light enhancement tasks because they do not consider illumination priors. \n\nBased on the two aforementioned assumptions, we propose the local-enhanced state space module (LESSM) and implicit retinex-aware selective kernel module (IRSK), which is the modeling of local and global degradation coupled in our framework for low light enhancement task. Therefore, we believe our novelty and contribution is on the par with the SOTA methods.\n\nTherefore, we believe our novelty and contribution are on par with the SOTA methods.\n\nSince IRSK is specially designed for low light enhancement task, We replaced the VSSM of MambaIR with LESSM to demonstrate the effectiveness of ours.  As shown in Figure R1 and Table R1, we demonstrate the effectiveness of the proposed LESSM in MambaIR on image restoration tasks, indicating that our approach also has a positive impact on other low-level vision tasks, as discussed by \\textbf{Reviewer 1bAi}. While the differences between LESSM and the vanilla VSSM may appear relatively small in description, each has its key motivations and shows significant improvements on common benchmarks over the previous models. We believe that simplicity combined with effectiveness is one of the most favored traits in the field of computer vision.\n\n**Q2: How is the illumination prior in the paper obtained and how does the proposed IRSK module work specifically.** \n\n**A2:** Sorry for the confusion caused. In our framework, we propose a Retinex-aware kernel selective mechanism, where two coupled Retinex-aware priors are used to select the spatial context regions, the maximum and mean values of RGB images can be regarded as a rough illumination prior. We use it in a implicit manner instead of restore the illumination map or use a single illumination feature for feature guidance. Base on this,  we clarify the work process of IRSK as stated in Lines 184-194.\n \n\n**Q3: what are the specific settings for the different variants in the ablation study?**\n\n**A3:** Baseline-1 directly uses the standard vision state space module (VSSM) to process flattened vision data in our proposed UNet-shaped framework, following the Norm → VSSM → Norm → channel attention layer flow as referenced in [14] for image denoising tasks. Baseline-2 introduces retinex thory \\(L=R \\ast I\\) to Baseline-1, where \\(I\\) denotes the low light image and \\(R\\) denotes the reflectance (enhanced image), \\(I\\) is the illumination map. aims to estimate the illumination map instead of directly predicting the enhanced image, and then restores the enhanced result by \\(R=L/I\\), as referenced in [13],[43],[45]. \n\n\n[C3] Guo X. LIME: A method for low-light image enhancement. In ACM MM. pages 87-91. 2016.\n\n**Q4: Some state-of-the-art works have not been compared (e.g., DiffLL[1]).**\n\n**A4:** As reported in [C4], DiffLL indeed outperforms ours on some benchmark datasets in terms of PSNR and SSIM. As **Reviewer 1rUY** commented, computational complexity and memory cost are also important parts of the LLIE task. We find that more performance and parameters (inference time over 0.1s, model size over 100M) of DiffLL may be required in the case of comparable performance, while our inference time is below 0.1s and model size is only about 2M. Besides, comparing MambaLLIE and the state-of-the-art method on unpaired datasets, our MambaLLIE outperforms DiffLL in terms of perceptual evaluation metrics and visual results. Please refer to Table R4 and Figure R3. In our revised version, we will add citations to this work and discuss it.\n\n[C4] Jiang et al, Low-Light Image Enhancement with Wavelet-based Diffusion Models. Siggraph Asia 2023.\n\n**Q5: The authors should compare more perceptual evaluation metrics.**\n\n**A5:** Please refer to Table R4. We compare two non-reference perceptual metrics, MUSIQ and NIMA, on five unpaired datasets, including LIME, VV, NPE, MEF, and DICM. Experimental evaluations show the superiority of our method over SOTAs with better perceptual evaluation in most comparisons.\n\n**Q6: The color in the last column of Table 2 is incorrectly marked.**\n\n**A6:** Thanks for your careful reading. We will rectify this mistake in the revised version and double-check the color of Table 2.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "8xsXb3pJRo", "reviewer": "Reviewer_1rUY", "summary": "This paper proposes a Mamba-based framework, namely MambaLLIE, for low-light image enhancement. Specifically, the authors claim that they have two technical contributions:\n\n(i) A global-then-local state space block that integrates a local-enhanced state space module and an implicit Retinex-aware selective kernel module to capture intricate global and local dependencies.\n\n(ii) An implicit Retinex-aware selective kernel mechanism to guide deeper neural representations and segregate them into independent positive and negative illumination components before integrating them.", "questions": "I am curious about the training time comparison between the proposed MambaLLIE and other methods.", "limitations": "Yes, the authors have analyzed the limitations in the main paper.", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "(i) The idea is novel and interesting. Mamba is a scorching research topic in computer vision now and has shown promising performance in many high-level vision tasks like image recognition, object detection, image segmentation, etc. But until now, there have been fewer efforts dedicated to the low-light image enhancement task, a small low-level topic. Thus, it is very exciting to see this wonderful work.\n\n(ii) The presentation is well-dressed. I like the style of the figures in this paper. For example, the teaser figure can clearly show the advantages in larger receptive fields of the proposed MambaLLIE than previous Transformer-based methods and Mamba-based image restoration methods. For another example, the pipeline in Figure 2 can also clearly show the workflow and the details of each submodule of the whole framework.\n\n(iii)  The performance is good and solid. The proposed MambaLLIE significantly outperforms the state-of-the-art methods on six benchmarks including LOL-v2-syn, LOL-v2-real, SMID, SDSD-indoor, and SDSD-outdoor, as reported in Table 1. The visual comparisons in Figure 3 also show the effectiveness of the proposed method. Looks good.", "weaknesses": "(i) The motivation is not clear. To be specific, why use Mamba for low-light image enhancement? The authors claim that the Mamba can help capture long-range dependences. However, Transformer architecture can also model the long-range dependences. Why use Mamba instead of Transformer? Also, why design the space state module like this? The insight of the proposed Mamba should be analyzed in more detail.\n\n(ii) A technique should be explained, i.e., what is the scanning operation in the state space module? The authors mention the scan in Mamba but I could not find any mathematical formula or text description in the paper to explain the computation process of this scan.\n\n(iii) In table 1, although the performance of the proposed MambaLLIE is better than the state-of-the-art Transformer-based method Retinexformer, its computational complexity and memory cost are larger than those of Retinexformer. For example, the FLOPS and the Params are 33% and 38% higher. In addition, what about the comparison of performance on the MIT-Adobe-FiveK dataset (sRGB output mode)?\n\n(iv) In Figure 4, it seems that Retinexformer achieves more visually pleasant results. For example, in the third line of Figure 6, Retinexformer reconstructs the complete edge of the redwood in the left zoomed-in patch but the proposed MambaLLIE fails. Thus, it is better to compare MambaLLIE and the state-of-the-art method on unpaired datasets such as the LIME, NPE, MEF, DICM, and VV datasets.\n\n(v) The source code and pre-trained models have not been submitted. The reproducibility cannot be checked.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Q1: Why use Mamba for low-light image enhancement?**\n\n**A1:** Our **Motivation** aims to take into account both global and local image restore. As we know, the low-light enhancement task faces challenges of global color degeneration and local noise disturbance. Global color degeneration suffer from the decrease of illumination, which tends to introduce a global-aware estimator, while the local degradation may contains the large or small regions. Hence, we intuitively introduce Mamba [8] into low light enhancement task, which excels at capturing the global dependencies of input data to restore. Compared with previous CNN and Transformer-based methods, Mamba-based model has exhibit promising performance with **linear or near-linear scaling complexity** in image super-resolution, image segmentation, etc., but still has the **suboptimal results** in low light enhancement task.\n\n**Q2: Why use Mamba instead of Transformer?**\n\n**A2:** We chose Mamba over Transformers due to its efficient long-range dependency modeling with linear complexity. Transformers can model long-range dependencies, but they typically involve higher computational complexity and memory costs.\n\n**Q3: Why design the space state module like this?**\n\n**A3**: Most prior VSSMs use the different directional scan in their sequential state, which is necessary for strong performance on visual tasks. However, it has been empirically observed that CNN seem to work fine for 2D dependency of vision data. Ours local-enhanced design essentially introduces the local invariance into state space model, which can integrate the existing directional scan with our local-enhanced term into VSSM. By additional convolving 2D information into directional scan, our method ensures a closer arrangement of relevant local tokens, enhancing the capture of local dependencies. This technique is depicted in Figure 2 (c). Next, we follow the selection mechanism by parameterizing the VSSM parameters based on the input, allows the model to filter out irrelevant information and remember relevant information indefinitely, as pointed in [8]. Besides, our local-enhanced term can be regarded as a local consistency constraint for the state space for vision data, guiding the module to learn neighborhood features and thus enhancing robustness, literatures [47], [C1] likewise added constraints of local term to a state–space model as equation 10 and developed the estimate performance.\n\n**Q4: A technique should be explained, i.e., what is the scanning operation in the state space module?**\n\n**A4:** To allow Mamba to process 2D images, the feature map needs to be flattened before being iterated by the state space. Therefore, the unfolding strategy is particularly important. In this work, we follow [38], which uses scans in four different directions to generate scanned sequences. \n\n**Q5: In table 1, although the performance of the proposed MambaLLIE is better than the state-of-the-art Transformer-based method Retinexformer, its computational complexity and memory cost are larger than those of Retinexformer. For example, the FLOPS and the Params are 33\\% and 38\\% higher.**\n\n**A5:** Reducing the computational complexity and memory cost is not the key part of MambaLLIE. Our MambaLLIE targets addressing the contradiction between global and local context interaction in the VSSM and proposes the flexible and effective IRSK for the LLIE task to achieve a larger receptive field in terms of global and local information. Besides, we significantly reduced the computational complexity and memory cost compared with our competitor MambaIR and kept comparable costs with other competitors. Additionally, we acknowledge that RetinexFormer is an outstanding work, maintaining a good balance in terms of parameter and perfermance. Our method slightly surpasses it in terms of performance.\n\n**Q6: What about the comparison of performance on the MIT-Adobe-FiveK dataset (sRGB output mode)?**\n\n**A6:** Following your valuable suggestion, we conducted the experiments of MambaLLIE and recent SOTA methods on the MIT-Adobe-FiveK dataset in Table R5, which verifies the comparable results of our method with RetinexFormer and surpasses the performance of other SOTA models. Figure R5 further shows the qualitative comparisons of MambaLLIE and RetinexFormer, where ours can remarkably improve the underexposure that appeared in RetinexFormer's result.\n\n**Q7: In Figure 4, it seems that Retinexformer achieves more visually pleasant results. For example, in the third line of Figure 6, Retinexformer reconstructs the complete edge of the redwood in the left zoomed-in patch but the proposed MambaLLIE fails. Thus, it is better to compare MambaLLIE and the state-of-the-art method on unpaired datasets such as the LIME, NPE, MEF, DICM, and VV datasets.**\n\n**A7:** Please refer to Table R4. We compare two non-reference perceptual metrics, MUSIQ and NIMA, on five unpaired datasets, including LIME, VV, NPE, MEF, and DICM. Experimental evaluations show the superiority of our method over SOTAs with better perceptual evaluation in most comparisons.\n\n**Q8: The source code and pre-trained models have not been submitted.**\n\n**A8:** Now, our code and the pretrained model are released on our project page. Please refer to our project page.\n\n**Q9: Training time comparison**\n\n**A9:** For example, training on the LOLV2 real dataset takes approximately 19 hours using PyTorch on a server equipped with 4090 GPUs, while Retinex tasks approximately 22 hours and MambaIR tasks approximately 27 hours . For the corresponding training parameters, please refer to lines 205-208 of the manuscript.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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:
We thank all reviewers and chairs for your time, constructive comments, and recognition of our work. We appreciate the positive comments on our idea, contributions, and state-of-the-art performance, such as *"novel and interesting,"* *"well illustrated,"* *"attracts attention from the community,"* and *"significantly outperforms prior works."* We believe all concerns have been clearly and directly addressed. Here we also want to summarize a few key clarifications concerning the contributions of our work.

## The novelty of GLSSB

We argue that the GLSSB is novel in terms of _*the new exploration of the vision state space model*_ and _*technical improvements for the Retinex-aware low light enhancement task*_.

Our **Motivation** aims to take into account both global and local image restore. As we know, the low-light enhancement task faces challenges of global color degeneration and local noise disturbance. Global color degeneration suffer from the decrease of illumination, which tends to introduce a global-aware estimator, while the local degradation may contains the large or small regions. Hence, we intuitively introduce Mamba [8] into low light enhancement task, which excels at capturing the global dependencies of input data to restore. Compared with previous CNN and Transformer-based methods, Mamba-based model has exhibit promising performance with **linear or near-linear scaling complexity** in image super-resolution, image segmentation, etc., but still has the **suboptimal results** in low light enhancement task. We find that the existing state space model is limited in modeling local dependencies, leading to a failure in restoring details. Besides, end-to-end models may exhibit suboptimal performance in low-light enhancement tasks because they do not consider illumination priors. 

Based on the two aforementioned assumptions, we propose the local-enhanced state space module (LESSM) and implicit retinex-aware selective kernel module (IRSK), which is the modeling of local and global degradation coupled in our framework for low light enhancement task. Therefore, we believe our novelty and contribution is on the par with the SOTA methods.

## The difference between LESSM and VSSM of MambaIR

Our **Main Contribution** lies in enhancing the local 2D dependencies for vision state space modle (VSSM). Specifically, we aim to formulate a simple yet efficient local-enhanced state space module (LESSM) to aggregate both local and global information by introducing additional local-enhanced term into the scan directions for sequence modeling, compared to existing state space methods, our approach requires only the _*small additional number of parameters*_ of VSSM to achieve significant performance improvements. 

The **Key insight** between LESSM and vanilla VSSM and its variants: most prior VSSMs use the different directional scan in their sequential state, which is necessary for strong performance on visual tasks. However, it has been empirically observed that CNN seem to work fine for 2D dependency of vision data. Ours local-enhanced design essentially introduces the local invariance into state space model, which can integrate the existing directional scan with our local-enhanced term into VSSM. By additional convolving 2D  information into directional scan, our method ensures _*a closer arrangement of relevant local tokens*_, enhancing the capture of local dependencies. This technique is depicted in Figure 2 (c). Next, we follow the selection mechanism by parameterizing the VSSM parameters based on the input, allows the model to filter out irrelevant information and remember relevant information indefinitely, as pointed in [8]. Besides, our local-enhanced term can be regarded as a local consistency constraint for the state space for vision data, guiding the module to learn neighborhood features and thus enhancing robustness, literatures [47], [C1] likewise added constraints of local term to a state–space model as equation 10 and developed the estimate performance.

As shown in Figure R1 and Table R1, we demonstrate the effectiveness of the proposed LESSM in MambaIR on image restoration tasks, indicating that our approach also has a positive impact on other low-level vision tasks. While the differences between LESSM and the vanilla VSSM may appear relatively small in description, each has its key motivations and shows significant improvements on common benchmarks over the previous models. We believe that simplicity combined with effectiveness is one of the most favored traits in the field of computer vision.

## Summary
As commented in **Reviewers DUEm and 1rUY**, *Considering emerging attention on Mamba for low-level vision tasks, the proposed work is timely and can thus attract attention to the community. Until now, there have been fewer efforts of Mamba dedicated to the low-light image enhancement task.* We posit that our contributions can provide a fresh perspectives on VSSM for low-level vision tasks. Our method achieves the SOTA results on low-light image enhancement, even image super-resolution, across various datasets and evaluation methods. Finally, we are willing to supplement the newly added experiments and analysis in the final manuscript/supplementary material. Also, our code and pre-trained model will be released on our project page.

[C1] Pfeffermann, Danny, and Richard Tiller. Small-area estimation with state–space models subject to benchmark constraints. Journal of the American Statistical Association 101.476,1387-1397, 2006.

Meta-review: The submission received scores of 4, 4, 6, 8. Reviewers recognize that the paper provides some relevant and well-constructed contributions, but there is disagreement about the level of novelty. In particular, reviewers ahrr (elaborated on in the AC-reviewer discussion) and DUEm considered the changes to be mainly incremental compared to MambaIR, while 1bAi considered that the modification follows the general trend of other transformer-based approaches in incprporating local dependencies. In terms of experiments and results, ahrr argued that the improvements are on par with (marginally better) other transformer-based methods, although comparisons were thorough and SOTA had been achieved. Reviewer 1rUY, who gave the score of 8, considered the application of Mamba to the LLE problem to be novel, with other important merits. The authors went into detail in making clarifications in the rebuttal phase.

After taking into account all the material and arguments presented, the AC considers the overall merit of the paper to be sufficient for acceptance. Although the critical comments of the reviewers are valid, and individually the novel components are broadly straightforward, as a whole there is sufficient non-trivial contribution in terms of an extension of Mamba to LLE, modifications that improve the VSSM receptive field (with visualization), and reasonable effectiveness in extending SOTA results.
Decision: Accept (poster)