Aug 2026· IEEE Transactions on Neural Networks and Learning Systems· Vol PP· 0 citations
Medicine
TL;DR
This work proposes a model-driven deep neural network to effectively handle the joint degradation of low light and blur and designs an illumination enhancement module (IEM) and a reflectance refinement module (RRM) to improve brightness, restore fine details, and suppress noise.
Abstract
Images captured at night are often degraded by both low-light conditions and motion blur, primarily caused by insufficient illumination and relative motion during exposure. Existing methods typically address only a single type of degradation or rely heavily on data-driven deep neural networks, which limits interpretability and generalization capability. To overcome these limitations, we propose a model-driven deep neural network to effectively handle the joint degradation of low light and blur. By integrating image restoration and Retinex-based modeling, we formulate a unified optimization framework and employ a proximal gradient descent (PGD)-based deep unfolding strategy to recover reflectance and illumination components from low-light blurry images. Furthermore, we design an illumination enhancement module (IEM) and a reflectance refinement module (RRM) to improve brightness, restore fine details, and suppress noise. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed method outperforms state-of-the-art approaches. Code is available at https://github.com/cassiopeia-yxx/MDNet.
Low-light image enhancement is crucial in situations where visible sensors might suffer from severe noise and information loss ( e.g., nighttime surveillance). Recent approaches investigate auxiliary modalities invariant to illumination to improve the performance, such as thermal infrared imaging. We propose a Multimodal Intrinsics-Guided Framework that integrates RGB and thermal data to reconstruct well-lit images. Our method utilizes a two-stage pipeline: first, we employ an intrinsic decomposition strategy to separate re-flectance and shading components through knowledge distillation, where a teacher network guides a student model in re-constructing consistent intrinsic components; then, a refine-ment stage restores fine structures and visual details. We train the proposed model on synthetic data from HDRT dataset and demonstrate strong generalization to real-world benchmarks such as LLVIP and V-TIEE, outperforming state-of-the-art methods in most evaluation metrics. Code is available at : https://github.com/simonemelc/TIRGlow
S. Melcarne, J. Dugelay· International Conference on...· 0 citations
This work introduces See in the Degraded Extremely Dark (SIDED), a new dataset that applies controlled motion degradation to extremely low-light RAW pairs while retaining their original sensor noise and introduces a physics-guided refinement model to strengthen illumination--reflectance consistency, pixel fidelity, and color preservation without incurring additional inference cost.
Zepu Wang, Jin Liang, Weijie Xiao et al.· 0 citations
Results validate the effectiveness and robustness of the proposed illumination-aware modeling strategy for low-light image enhancement, IA2former, which effectively captures long-range dependencies, improves detail restoration, and preserves spatial structures under challenging illumination conditions.
Tianqi Jiang· Poster Volume 0007 The 2026...· 0 citations
Existing zero-shot low-light image enhancement methods often underutilize image priors, leading to noise amplification and color distortion. To address these issues, we propose a zero-shot framework for low-light image enhancement. The framework first performs illumination-aware self-supervised denoising to generate a cleaner reference image, which is then used to guide diffusion-based enhancement with a pre-trained backbone. Specifically, the denoising module uses pairwise downsampling together with the proposed illumination prior to suppress noise in dark regions. We then guide the reverse sampling of the pre-trained diffusion model with a refinement strategy operating in both the frequency and spatial domains, so that illumination enhancement and local detail refinement can be jointly achieved during sampling. At each step, Fourier-based reconstruction contributes to illumination enhancement while preserving structural information, and illumination-guided spatial adjustment further refines local brightness. Experiments on multiple benchmark datasets show that the proposed method improves illumination while preserving structural details.
Image enhancement is a widely researched area in the domain of computer vision, particularly image processing. Among the subdomains, low-light image enhancement (LLIE) receives considerable attention due to problems and challenges imposed by poor lighting conditions. As such, low-light images suffer from poor visibility, distorted colors, and loss of details, which limits their usability in many applications. The traditional methods have struggled to preserve such details and make the images susceptible to over-enhancement. Whereas, the learning-based techniques rely heavily on paired datasets for training. Therefore, we propose a Bidirectional Conv-GRU integrated GAN framework. Involving bidirectional Conv-GRU modules in our use-case enables the model to capture both local textures and long-range feature dependencies. Also, the use of unpaired datasets allows it to learn flexible and realistic mappings without the strict need for aligned image pairs. The results demonstrate the potency of our proposed work as compared to state-of-the-art methods.
Palak Deb Patra, Santosh Kumar Panda, Manoj Kumar Bishwal et al.· International Conference on...· 0 citations
Most existing low-light image enhancement methods mainly rely on feature modeling in a single domain, making it difficult to simultaneously achieve global illumination correction, local detail restoration, and noise suppression. To overcome this limitation, we propose a low-light image enhancement network(MPNet) that leverages multi-domain prior attention, and introduce MPGSA (Multi-domain Prior Guided Self-Attention) as its core module. Specifically, MPGSA incorporates priors from the spatial, Fourier, and wavelet domains into the attention mechanism. Through complementary multi-domain modeling, it improves global illumination restoration, local texture reconstruction, and noise suppression. Extensive experimental results demonstrate that the proposed method not only achieves outstanding visual quality and competitive quantitative metrics on multiple public datasets, but also performs excellently on downstream tasks, showcasing strong generalization ability and broad application potential.
Shifan Yang, Qizhao Lin, Weilin Wu et al.· IEEE Signal Processing Lette...· 0 citations
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