HistRetinex: Optimizing Retinex model in Histogram Domain for Efficient Low-Light Image Enhancement

TL;DR

HistRetinex introduces a fast histogram-based Retinex model for low-light image enhancement, significantly reducing processing time while improving visibility and performance compared to existing methods.

Contribution

The paper extends the Retinex model to the histogram domain and develops a novel optimization approach for efficient low-light image enhancement.

Findings

Outperforms existing methods in visibility and metrics

Processes 1000*664 images in 1.86 seconds

Achieves at least 6.67 seconds time saving

Abstract

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.