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Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement
IEEE International Conference on Computer Vision, 2023When enhancing low-light images, many deep learning algorithms are based on the Retinex theory. However, the Retinex model does not consider the corruptions hidden in the dark or introduced by the light-up process.
Yuanhao Cai +5 more
semanticscholar +1 more source
SNR-Aware Low-light Image Enhancement
Computer Vision and Pattern Recognition, 2022This paper presents a new solution for low-light image enhancement by collectively exploiting Signal-to-Noise-Ratio-aware transformers and convolutional models to dynamically enhance pixels with spatial-varying operations.
Xiaogang Xu +3 more
semanticscholar +1 more source
URetinex-Net: Retinex-based Deep Unfolding Network for Low-light Image Enhancement
Computer Vision and Pattern Recognition, 2022Retinex model-based methods have shown to be effective in layer-wise manipulation with well-designed priors for low-light image enhancement. However, the commonly used handcrafted priors and optimization-driven solutions lead to the absence of adaptivity
Wen-Bin Wu +5 more
semanticscholar +1 more source
Underwater Image Enhancement via Minimal Color Loss and Locally Adaptive Contrast Enhancement
IEEE Transactions on Image Processing, 2022Underwater images typically suffer from color deviations and low visibility due to the wavelength-dependent light absorption and scattering. To deal with these degradation issues, we propose an efficient and robust underwater image enhancement method ...
W. Zhang +5 more
semanticscholar +1 more source
Iterative Prompt Learning for Unsupervised Backlit Image Enhancement
IEEE International Conference on Computer Vision, 2023We propose a novel unsupervised backlit image enhancement method, abbreviated as CLIP-LIT, by exploring the potential of Contrastive Language-Image Pre-Training (CLIP) for pixel-level image enhancement. We show that the openworld CLIP prior not only aids
Zhexin Liang +4 more
semanticscholar +1 more source
Implicit Neural Representation for Cooperative Low-light Image Enhancement
IEEE International Conference on Computer Vision, 2023The following three factors restrict the application of existing low-light image enhancement methods: unpredictable brightness degradation and noise, inherent gap between metric-favorable and visual-friendly versions, and the limited paired training data.
Shuzhou Yang +4 more
semanticscholar +1 more source
Underwater Image Enhancement Method via Multi-Interval Subhistogram Perspective Equalization
IEEE Journal of Oceanic Engineering, 2023Due to the selective attenuation of light in water, captured underwater images exhibit poor visibility and cause considerable challenges for vision tasks.
Jingchun Zhou +3 more
semanticscholar +1 more source