Aug 2026· International Conference on Digital Image Processing· Vol 14351, pp. 1435104 - 1435104-9· 0 citations· 27 references
Engineering
TL;DR
The Edge-guided Hybrid Enhancement Network (EHENet), an efficient network integrating structural priors and spatial-angular extraction, is proposed, which significantly outperforms state-of-the-art methods in both quality and efficiency.
Abstract
Low-light light field (L3F) images suffer from severe structural degradation, including low contrast, blurred edges, and heavy noise, which disrupts angular consistency. Existing single-image enhancement methods fail to exploit the spatial-angular consistency of light field (LF) images, while L3F enhancement methods struggle in low-light scenarios with extremely low contrast, often resulting in over-smoothed edges and the loss of geometric details. To address these issues, we propose the Edge-guided Hybrid Enhancement Network (EHENet), an efficient network integrating structural priors and spatial-angular extraction. We propose a structural prior embedding strategy that employs Scharr operators and Gaussian filtering to explicitly model spatial edges. Furthermore, we design the Global Feature Enhancement (GFE) block to extract spatial-angular correlation. Experiments show EHENet significantly outperforms state-of-the-art methods in both quality and efficiency.
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.
Yue Zhang, Zhi-Liang Wu, Yuxuan Hou et al.· Proceedings of the Thirty-Fi...· 0 citations
The proposed AASFNet achieves competitive or leading performance across multiple key metrics under the evaluated experimental settings, yielding the best or second-best PSNR, SSIM, and NIQE values among the compared methods.
Yang Li, Xian-Guo Li, Dan He et al.· Electronics· 0 citations
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, and uses pairwise downsampling together with the proposed illumination prior to suppress noise in dark regions.
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.
Tian-Qi Jiang· Poster Volume 0007 The 2026...· 0 citations
This manuscript proposes enhancing visibility and reducing noise in lowlight images through disentangled frequency-guided processing and progressive graph convolutional networks (LLI-DFP-PGCN), which attains high PSNR, and high SSIM compared to existing techniques.
K. Vijayakumar, M. Appa, R. Subramanian et al.· International Journal of Wav...· 0 citations
Low-light image enhancement is challenging due to noise and poor visibility. While diffusion models have shown remarkable generative capabilities, applying them directly to this task in the pixel domain remains problematic. They tend to struggle with the trade-off between global illumination correction and local detail...
Ping-Ping Liu, Jia-Hao Liu, S. Pei et al.· Neural Networks· 0 citations
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