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

Retinex-based low-light image enhancement methods are widely used due to their excellent performance. However, most of them are time-consuming for large-size images. To solve this issue, 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. First, we establish the relationship among the histograms of the illumination, reflectance, and low-light image based on the traditional Retinex model and an independence assumption, thereby approximating Retinex decomposition in the histogram domain. Second, based on prior information and the histogram-based Retinex model, we construct a novel two-level optimization model. By solving this optimization model, we derive the iterative formulas for the illumination and reflectance histograms, respectively. Finally, we enhance the low-light image by matching its histogram to the histogram estimated by HistRetinex. Experimental results demonstrate that the estimation errors between the estimated histogram and the low-light image histogram are within an acceptable range. Moreover, HistRetinex outperforms existing unsupervised enhancement methods in both visibility and performance metrics, while requiring only 1.85 seconds to process a $1000\times 664$ image, saving at least 6.68 seconds compared with the fastest competing traditional method. To further support efficient implementation, we use the MATLAB MEX tool to accelerate the implementation of HistRetinex, and the accelerated version runs at 28 FPS for $600\times 400$ images. The source code and experimental results are available at https://github.com/jingtianzhao/HistRetinex.