Reflectance-Oriented Probabilistic Equalization for Image Enhancement
This work addresses a domain-specific problem in image enhancement for low-light and normal-light images, offering an incremental improvement over existing histogram equalization techniques.
The paper tackles the problem of adaptively enhancing brightness and contrast for both low-light and normal-light images by proposing a novel 2D histogram equalization approach that improves global contrast and reduces noise amplification. It demonstrates superiority over existing methods on over 500 images, effectively enhancing low-light images without over-enhancing normal-light ones.
Despite recent advances in image enhancement, it remains difficult for existing approaches to adaptively improve the brightness and contrast for both low-light and normal-light images. To solve this problem, we propose a novel 2D histogram equalization approach. It assumes intensity occurrence and co-occurrence to be dependent on each other and derives the distribution of intensity occurrence (1D histogram) by marginalizing over the distribution of intensity co-occurrence (2D histogram). This scheme improves global contrast more effectively and reduces noise amplification. The 2D histogram is defined by incorporating the local pixel value differences in image reflectance into the density estimation to alleviate the adverse effects of dark lighting conditions. Over 500 images were used for evaluation, demonstrating the superiority of our approach over existing studies. It can sufficiently improve the brightness of low-light images while avoiding over-enhancement in normal-light images.