@inproceedings{
wang2024illumination,
title={Illumination Distribution Prior for Low-light Image Enhancement},
author={Chao Wang and Yang Zhou and Liangtian He and Lin Fenglai and Hongming Chen and Liang-Jian Deng},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=4UDvROcNJu}
}
In this paper, we propose a simple but effective illumination distribution prior (IDP) for images to illuminate the darkness. The illumination distribution prior is the product of a statistical approach to low-light images. It is based on a key factor - the mean value and standard deviation of images are positively correlated with the illumination. Using IDP in combination with the dual-domain feature fusion network (DFFN), we can obtain images that are more consistent with the ground truth distribution. DFFN inserts the discrete wavelet transform (DWT) into the transformer architecture, aiming to recover the detailed texture of the image through local high-frequency information and global spatial information. We have conducted extensive experiments on five widely used low-light image enhancement datasets and the experimental results show the superior performance of our proposed network (IDP-Net) compared to other state-of-the-art methods.
Illumination Distribution Prior for Low-light Image Enhancement
Chao Wang Yang Zhou Liangtian He Lin Fenglai Hongming Chen Liang-Jian Deng