Organizations: College of Computer Science and Technology, Jilin University, Changchun 130015, P.R. China · Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130015, P.R. China · College of Software, Jilin University, Changchun 130015, P.R. China · College of Communication Engineering, Jilin University, Changchun 130015, P.R. China
Limited illumination often causes severe physical noise and detail degradation in images. Existing Low-Light Image Enhancement (LLIE) methods frequently treat the enhancement process as a blind black-box mapping, overlooking the physical noise transformation during imaging, leading to suboptimal performance. To address this, we propose a novel LLIE approach, conceptually formulated as a physics-based attack and display-adaptive defense paradigm. Specifically, on the attack side, we establish a physics-based Degradation Synthesis (PDS) pipeline. Unlike standard data augmentation, PDS explicitly models Image Signal Processor (ISP) inversion to the RAW domain, injects physically plausible photon and read noise, and re-projects the data to the sRGB domain. This generates high-fidelity training pairs with explicitly parameterized degradation vectors, effectively simulating realistic attacks on clean signals. On the defense side, we construct a dual-layer fortified system. A noise predictor estimates degradation parameters from the input sRGB image. These estimates guide a degradation-aware Mixture of Experts (DA-MoE), which dynamically routes features to experts specialized in handling specific noise intensities. Furthermore, we introduce an Adaptive Metric Defense (AMD) mechanism, dynamically calibrating the feature embedding space based on noise severity, ensuring robust representation learning under severe degradation. Extensive experiments demonstrate that our approach offers significant plug-and-play performance enhancement for existing benchmark LLIE methods, effectively suppressing real-world noise while preserving structural fidelity. The sourced code is available at https://github.com/bywlzts/Attack-defense-llie.
Figures & tables
Fig. 1: Visual comparison of existing LLIE methods against our proposed approach. While current methods demonstrate good brightness recovery, they often suffer from subpar noise suppression, as highlighted by the noisy regions. Our proposed adversarial framework demonstrably provides superior noise reduction while maintaining structural fidelity.
Fig. 2: Comparison of different LLIE training paradigms. (a) Data augmentation-based methods treat LLIE as a passive black-box mapping from augmented low-light inputs. (b) Degradation + contrastive learning methods generate negative samples via handcrafted degradations, but often overlook the physical nature of sensor noise and lack adaptation to degradation severity. (c) Our adversarial framework employs a physics-guided RAW domain attack for plausible noise and explicit priors. A sentinel noise predictor offers explicit noise awareness, while adaptive architectural and metric defenses respond to attack severity for targeted noise suppression and robust enhancement.
Fig. 3: Overview of the proposed physics-based adversarial framework . Attack stream (left, red): PDS analytically inverts clean sRGB images to the linear RAW domain, injects Poisson and Gaussian noise parameters (k,σ) for sensor-level attacks, and re-projects the corrupted signal to sRGB via a forward ISP to generate realistic physics-based degraded samples with ground-truth physical priors ( Patt ). Defense stream (right, green): A sentinel Noise Predictor estimates degradation parameters ( Ppre ) from the low-light input and uses them to gate/modulate DA-MoE blocks and SFT layers, trained with a Dual-Domain Self-Supervised Loss . The DA-MoE is plug-and-play for existing enhancement backbones, and the AMD further adjusts the margin m according to the injected noise parameters ( Patt ) to maintain appropriate feature distances under varying degradation severities.
Fig. 4: Comparison of noise variance against signal intensity. The bottom grayscale strip represents increasing brightness. Naive sRGB-domain injection (orange) shows intensity-invariant variance, inconsistent with real sensors. In contrast, our PDS noise (red), generated via RAW-domain Poisson-Gaussian injection and ISP reprocessing, exhibits clear signal-dependent variance. This underscores the necessity of RAW-domain physics-based noise modeling for realistic low-light degradations.
Method
Venue
LOL-Blur
LOL-v1
PSNR ↑
SSIM ↑
LPIPS ↓
FID ↓
PSNR ↑
SSIM ↑
LPIPS ↓
FID ↓
LEDNet [ 15 ]
ECCV 22
25.79
0.8500
0.1670
17.89
23.32
0.9029
0.0972
40.38
LEDNet+
Ours
26.40 (+0.61)
0.8664 (+0.0164)
0.1282 (-0.0388)
13.95 (-3.94)
23.37 (+0.05)
0.9014 (-0.0015)
0.0868 (-0.0104)
34.75 (-5.63)
Restormer [ 13 ]
CVPR 22
25.21
0.8367
0.2190
25.19
23.31
0.8865
0.1298
62.70
Restormer+
Ours
25.83 (+0.62)
0.8655 (+0.0288)
0.1214 (-0.0976)
13.81 (-11.38)
23.45 (+0.14)
0.9033 (+0.0168)
0.0874 (-0.0424)
35.32 (-27.38)
FourLLIE [ 11 ]
MM 23
22.48
0.7289
0.2596
26.85
20.06
0.8401
0.2409
88.17
TABLE I: Results on LOL-Blur and LOL-v1 Datasets. Our plug-in almost improves PSNR/SSIM and reduces LPIPS/FID across all baselines, with particularly large gains on LPIPS and FID.
Fig. 5: Visual comparison on the LOL-Blur dataset. The first two columns are the input and ground truth. For each method, we show the baseline result and its enhanced variant, where “+” indicates integrating our proposed adversarial framework. Zoom in for better inspection of noise suppression.
Fig. 6: Visual comparisons with state-of-the-art methods on LOL-v1 dataset. Zoom in for better detail recovery.
Method
Real-LOL-Blur
NIQE ↓
BRISQUE ↓
PI ↓
LEDNet [ 15 ]
5.169
42.801
5.030
LEDNet+
4.506 (-0.663)
35.609 (-7.192)
4.339 (-0.691)
Restormer [ 13 ]
5.457
36.682
5.488
Restormer+
4.154 (-1.303)
25.911 (-10.771)
3.874 (-1.614)
FourLLIE [ 11 ]
5.983
43.892
6.214
TABLE II: Results on the Real-LOL-Blur dataset using models pretrained on LOL-Blur.
Method
Real-LOL-Blur
NIQE ↓
BRISQUE ↓
PI ↓
LEDNet [ 15 ]
5.322
31.849
5.535
LEDNet+
5.280 (-0.042)
30.419 (-1.430)
5.486 (-0.049)
Restormer [ 13 ]
5.287
34.544
5.461
Restormer+
4.925 (-0.362)
32.913 (-1.631)
4.898 (-0.563)
FourLLIE [ 11 ]
5.796
41.620
5.791
TABLE III: Results on the Real-LOL-Blur dataset using models pretrained on LOL-v1.
Method
GoPro
SID
PSNR ↑
SSIM ↑
PSNR ↑
SSIM ↑
Retinexformer [ 10 ]
29.14
0.8808
25.74
0.6951
Retinexformer+
29.10 (-0.04)
0.8839 (+0.0031)
26.80 (+1.06)
0.7012 (+0.0061)
Wave-Mamba [ 38 ]
26.94
0.8273
23.57
0.6740
Wave-Mamba+
27.03 (+0.09)
0.8296 (+0.0023)
23.98 (+0.41)
0.6846 (+0.0106)
CWNet [ 12 ]
30.71
0.9104
25.82
0.7024
TABLE IV: Quantitative results on the GoPro and SID datasets.
Settings
PSNR ↑
SSIM ↑
LPIPS ↓
FID ↓
FourLLIE (Baseline)
20.06
0.8401
0.2409
88.17
w/o Noise Predictor
20.83
0.8675
0.2277
84.95
w/o Gaussian-noise Expert
20.79
0.8723
0.2173
83.91
w/o Poisson-noise Expert
21.12
0.8746
0.1857
79.74
w/o Gate
20.89
0.8788
0.1962
80.80
w/o SFT layer
21.25
0.8801
0.1745
78.62
TABLE V: Ablation study on modules.
Variant
PSNR ↑
SSIM ↑
LPIPS ↓
FID ↓
FourLLIE (Baseline)
20.06
0.8401
0.2409
88.17
w/o Lmetric
20.24
0.8459
0.2225
83.46
w/o Lconsist
21.05
0.8774
0.1857
79.74
w/o Lnpnormal
21.25
0.8749
0.1851
78.67
w/o Lnplow
20.87
0.8698
0.1873
79.91
FourLLIE+ (Ours)
21.34
0.8815
0.1524
76.87
TABLE VI: Ablation of loss terms.
Weight
PSNR ↑
SSIM ↑
LPIPS ↓
FID ↓
Baseline
20.06
0.8401
0.2409
88.17
λcon=0.3
20.46
0.8752
0.2085
81.03
λcon=0.7
21.37
0.8803
0.1840
77.56
λmet=0.001
20.91
0.8560
0.1985
79.04
λmet=0.05
21.32
0.8801
0.1842
78.58
λmet=0.1
20.74
0.8785
0.1969
80.87
TABLE VII: Sensitivity to loss weights on LOL-v1.
Fig. 7: Efficiency comparison. We report Params, FLOPs, and average inference time per image for FourLLIE and CWNet.
Diffusion models excel at image restoration via probabilistic modeling of forward noise addition and reverse denoising, and their ability to handle complex noise while preserving fine details makes them well-suited for Low-Light Image Enhancement (LLIE). Mainstream diffusion based LLIE methods either adopt a two-stage pipeline or an auxiliary correction network to refine U-Net outputs, which severs the intrinsic link between enhancement and denoising and leads to suboptimal performance owing to inconsistent optimization objectives. To address these issues, we propose the Signal Attenuation Diffusion Model (SADM), a novel diffusion process that integrates the signal attenuation mechanism into the diffusion pipeline, enabling simultaneous brightness adjustment and noise suppression in a single stage. Specifically, the signal attenuation coefficient simulates the inherent signal attenuation of low-light degradation in the forward noise addition process, encoding the physical priors of low-light degradation to explicitly guide reverse denoising toward the concurrent optimization of brightness recovery and noise suppression, thereby eliminating the need for extra correction modules or staged training relied on by existing methods. We validate that our design maintains consistency with Denoising Diffusion Implicit Models(DDIM) via multi-scale pyramid sampling, balancing interpretability, restoration quality, and computational efficiency.
Ying Liu, Junchao Zhang, Caiyun Wu
School of Aeronautics and Astronautics, Central South University, Changsha 410083, China · Hunan Provincial Key Laboratory of Optic-Electronic Intelligent Measurement and Control, Changsha 410083, China
Low-Light Image Enhancement (LLIE) has long been a challenging problem in low-level vision, as insufficient illumination often leads to low contrast, detail loss, and noise. Recent studies show that deep learning-based Retinex theory can effectively decouple illumination and reflectance. However, existing methods frequently suffer from over-enhancement or color distortion, and often assume uniform noise or ideal lighting. To address these limitations, we propose InterLight, a novel framework that systematically excavates and operationalizes intrinsic illumination priors for LLIE.Our core insight is that robust enhancement requires not just estimating illumination, but constructing an illumination-aware pipeline. We first inject sensor-level illumination-response priors via physics-guided augmentation, then represent the degradation through adaptive prompts conditioned on the scene's latent illumination state. This explicit representation directly guides a luminance-gated intrinsic memory mechanism to selectively compensate for information loss, prioritizing reconstruction in dark regions while preserving fidelity in bright ones. Finally, the entire process is regularized by a self-supervised consistency objective that distills illumination-invariant features. By deeply exploiting intrinsic illumination priors, our method achieves clearer textures and more visually coherent enhancement results. Extensive experiments across multiple benchmarks demonstrate the effectiveness of our approach. Code is available at: https://github.com/House-yuyu/InterLight.
Ziqi Wang, Xu Zhang, Laibin Chang +3
National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University · Department of Computer Science, University of Macau · Department of Computer Vision, Mohamed bin Zayed University of Artificial Intelligence +1
Self-supervised low-light image enhancement (LLIE) is highly appealing as it eliminates the reliance on external paired data. However, the lack of external references causes networks to struggle with decoupling entangled illumination, delicate textures, and amplified noise. To resolve this challenge, we propose an Internally Referenced LLIE framework that extracts reliable physical and structural references from the degraded input image itself. First, we introduce a local exposure-simulated scheme to extract a low-frequency pseudo ground-truth. This serves as an internal physical reference to guide global illumination estimation and correct color casts. Second, we propose a dual-domain preservation strategy with spatial and spectral constraints to construct internal structural references. Specifically, an Illumination-Aligned Perceptual loss preserves global structures under illumination shifts, while a Shift-Invariant Spectral Correlation loss captures fine-grained local structures and suppresses high-frequency noise. Finally, we propose a Gain-Adaptive Feature Modulation (GAFM) mechanism to address highly spatially-variant residual noise. By transforming the self-estimated illumination map into an internal spatial gain prior, GAFM dynamically guides a blind-spot network for spatially-aware denoising. Extensive experiments demonstrate that our method achieves state-of-the-art performance, delivering superior noise suppression and textural fidelity. Code will be publicly released at https://visonj.github.io/IRLE/.
Peiyuan He, Hainuo Wang, Hengxing Liu +2
College of Intelligence and Computing, Tianjin University, Tianjin 300350, China