TL;DR
SF-GAL, a prior-calibrated spatial-frequency Retinex decomposition framework for unsupervised low-light image enhancement, which calibrates a CLAHE-derived structural prior, decomposes low-light features through complementary spatial and wavelet branches, and uses structure-guided frequency modulation to regulate frequency responses before Retinex prediction.
A continuity-guided low-light enhancement network, termed CGLEN, is proposed for unsupervised structure-aware image reconstruction, and indicates that explicitly modeling structural continuity provides an effective strategy for unsupervised low-light image enhancement, particularly in challenging illumination condition...
A Relative Illumination Structure Estimation (RISE) framework is proposed that decouples relative illumination structure from absolute exposure and infers it from reliable bright regions, enabling interpretable and robust enhancement.
A trainable dual-domain illumination-prior module that is jointly optimized with each host backbone and exploits frequency-domain and spatial-domain illumination statistics, which improves PSNR and SSIM over their corresponding baselines.
Low-light image enhancement aims to improve image quality under insufficient illumination while recovering underlying structural and texture information. Existing approaches, ranging from conventional image processing techniques to deep generative models, have shown promising performance in brightness enhancement and d...
SFH-Net is proposed, an horizontal-vertical-intensity (HVI)-guided luminance-chrominance collaborative enhancement framework that achieves a better trade-off among reconstruction accuracy, structural fidelity, and parameter compactness.
SPACE introduces a Depth-Adaptive HVI Transformation to decouple luminance and chrominance under depth guidance, effectively suppressing color-space noise and a Depth-Manifold Modulated Attention mechanism constrains feature interactions within a learned depth manifold, ensuring structural coherence during enhancement.
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