Retinex-based low-light image enhancement methods are widely used due to their excellent performance. However, most of them are time-consuming for large-sized images. This paper extends the Retinex model from the spatial domain to the histogram domain, and proposes a novel histogram-based Retinex model for fast low-light image enhancement, named HistRetinex. Firstly, we define the histogram location matrix and the histogram count matrix, which establish the relationship among histograms of the illumination, reflectance and the low-light image. Secondly, based on the prior information and the histogram-based Retinex model, we construct a novel two-level optimization model. Through solving the optimization model, we give the iterative formulas of the illumination histogram and the reflectance histogram, respectively. Finally, we enhance the low-light image through matching its histogram with the one provided by HistRetinex. Experimental results demonstrate that the HistRetinex outperforms existing enhancement methods in both visibility and performance metrics, while executing 1.86 seconds on 1000*664 resolution images, achieving a minimum time saving of 6.67 seconds.
@article{arxiv.2510.21100,
title = {HistRetinex: Optimizing Retinex model in Histogram Domain for Efficient Low-Light Image Enhancement},
author = {Jingtian Zhao and Xueli Xie and Jianxiang Xi and Xiaogang Yang and Haoxuan Sun},
journal= {arXiv preprint arXiv:2510.21100},
year = {2025}
}
Comments
Currently, this manuscript has been rejected by TIP and is undergoing revisions. The reviewers noted that the paper contains some innovative aspects, but identified issues in the experimental and algorithmic sections