Jul 2026· International Conference on Signal Processing and Communications· pp. 1-5· 0 citations· 24 references
Abstract
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.
The proposed MSHCDI-Net is a Multi-Scale Hybrid Cross-Domain Interaction Network that effectively integrates CNN and Transformer branches to jointly capture local texture details and global contextual relationships and achieves improved structural consistency and detail restoration in low-light scenes.
Bin Chen, Peitao Li, Chaobing Zheng et al.· PLoS ONE· 1 citation
A Multimodal Intrinsics-Guided Framework that integrates RGB and thermal data to reconstruct well-lit images and demonstrates strong generalization to real-world benchmarks such as LLVIP and V-TIEE, outperforming state-of-the-art methods in most evaluation metrics.
S. Melcarne, J. Dugelay· International Conference on...· 0 citations
The proposed framework provides a robust, scalable solution for real-world illumination enhancement across diverse lighting conditions and consistently outperforms state-of-the-art supervised and unsupervised methods in terms of fidelity, perceptual quality, and generalization.
Yasmin Yasin, Muhammad Usman, Ibrahim Radwan et al.· 0 citations
This work validates the design effectiveness of decoupling global and local representations within a frozen backbone, and establishes a new baseline for parameter-efficient enhancement.
Yan-Peng Cao, Yue Wang, Ming-Hui Liang et al.· Pattern Analysis and Applica...· 0 citations
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.
Yao Xiao, You-Shen Xia, Zhen-Yu Lu et al.· IEEE Transactions on Neural...· 0 citations
Existing methods typically require training a separate model for each dataset, making them difficult to generalize across diverse illumination conditions. To address this limitation, we propose a novel low-light image enhancement method based on a Mixture of Experts (MoE) mechanism with fast adaptation. In our framewor...
Yi Wang, Haonan Su, Zhao-Lin Xiao· IEEE Signal Processing Lette...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
