@article {huang2025detailpreserving,
	title = {Detail-Preserving Diffusion Models for Low-Light Image Enhancement},
	journal = {IEEE Transactions on Circuits and Systems for Video Technology},
	volume = {35},
	number = {4},
	year = {2025},
	month = {apr},
	pages = {3396{\textendash}3409},
	abstract = {Existing diffusion models for low-light image enhancement typically incrementally remove noise introduced during the forward diffusion process using a denoising loss, with the process being conditioned on input low-light images. While these models demonstrate remarkable abilities in generating realistic high-frequency details, they often struggle to restore fine details that are faithful to the input. To address this, we present a novel detail-preserving diffusion model for realistic and faithful low-light image enhancement. Our approach integrates a size-agnostic diffusion process with a reverse process reconstruction loss, significantly enhancing the fidelity of enhanced images to their low-light counterparts and enabling more accurate recovery of fine details. To ensure the preservation of region- and content-aware details, we employ an efficient noise estimation network with a simplified channel-spatial attention mechanism. Additionally, we propose a multiscale ensemble scheme to maintain detail fidelity across diverse illumination regions. Comprehensive experiments on eight benchmark datasets demonstrate that our method achieves state-of-the-art results compared to over twenty existing methods in terms of both perceptual quality (LPIPS) and distortion metrics (PSNR and SSIM). The code is available at: https://github.com/CSYanH/DePDiff.},
	issn = {1558-2205},
	author = {Huang, Yan and Liao, Xiaoshan and Liang, Jinxiu and Shi, Boxin and Yong Xu and Le Callet, Patrick}
}
