BIAWDiff: Enhancing Low-Light Images with Bio-Inspired Attention and Wavelet Diffusion
Zeyu Li, Sheng Yang, Hanxiang Yang, Xiongxin Tang, Fengge Wu, Fanjiang Xu
Abstract
Low-light image enhancement aims to improve visual quality under challenging lighting conditions while preserving details and color fidelity. Existing traditional algorithms and deep learning approaches, often struggle with balancing brightness enhancement and detail preservation, leading to issues such as overexposure, artifacts, and loss of high-frequency details. To address these challenges, we propose a novel method, Bio-Inspired Attention and Wavelet Diffusion (BIAWDiff), that integrates Retinex theory with bio-inspired attention and wavelet-based diffusion models to enhance low-light Images. BIAWDiff consists of three key modules: the Initial Light Restoration (ILR) module for brightness enhancement and noise reduction, the Rod Cell-Inspired Attention Refinement (RCAR) module for luminance refinement, and the Detail Refinement (DR) module for restoring high-frequency details. Experimental results demonstrate that BIAWDiff outperforms existing techniques, achieving superior results in brightness enhancement, noise reduction, and detail preservation, with an average PSNR increase of 5.1% and SSIM improvement of 3.2% on paired datasets, and a reduction in NIQE and BRISQUE by 7.4% and 8.6% on unpaired datasets.
BibTeX
@inproceedings{icassp2025_biawdiffenhancin,
title = {BIAWDiff: Enhancing Low-Light Images with Bio-Inspired Attention and Wavelet Diffusion},
author = {Zeyu Li and Sheng Yang and Hanxiang Yang and Xiongxin Tang and Fengge Wu and Fanjiang Xu},
booktitle = {ICASSP 2025},
year = {2025}
}