This is the official implementation of the paper "Color Shift Estimation-and-Correction for Image Enhancement". The code is implemented in PyTorch.
Abstract: Images captured under sub-optimal illumination conditions may contain both over- and under-exposures. Current approaches mainly focus on adjusting image brightness, which may exacerbate color tone distortion in under-exposed areas and fail to restore accurate colors in over-exposed regions. We observe that over- and over-exposed regions display opposite color tone distribution shifts, which may not be easily normalized in joint modeling as they usually do not have "normal-exposed" regions/pixels as reference. In this paper, we propose a novel method to enhance images with both over- and under-exposures by learning to estimate and correct such color shifts. Specifically, we first derive the color feature maps of the brightened and darkened versions of the input image via a UNet-based network, followed by a pseudo-normal feature generator to produce pseudo-normal color feature maps. We then propose a novel COlor Shift Estimation (COSE) module to estimate the color shifts between the derived brightened (or darkened) color feature maps and the pseudo-normal color feature maps. The COSE module corrects the estimated color shifts of the over- and under-exposed regions separately. We further propose a novel COlor MOdulation (COMO) module to modulate the separately corrected colors in the over- and under-exposed regions to produce the enhanced image. Comprehensive experiments show that our method outperforms existing approaches.
- [2024/04/18] Update Google Drive link for the paper and README.
If you find our work helpful, feel free to cite our paper as:
@inproceedings{li_2024_cvpr_color,
title = {Color Shift Estimation-and-Correction for Image Enhancement},
author = {Yiyu Li and Ke Xu and Gerhard Petrus Hancke and Rynson W.H. Lau},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2024}
}