# Reflectance-Oriented Probabilistic Equalization for Image Enhancement

arXiv: https://arxiv.org/abs/2209.06406 · HTML: https://mlsanity.com/paper/2209.06406/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 33 references that are in the map.
- First posted on arXiv 14 Sep 2022.

## Builds on (most cited first, 1 of 1)

- [Kindling the Darkness: A Practical Low-light Image Enhancer](https://mlsanity.com/paper/1905.04161/) (2019)

## Built on by (most cited first, 2 of 2)

- [Division Gets Better: Learning Brightness-Aware and Detail-Sensitive Representations for Low-Light Image Enhancement](https://mlsanity.com/paper/2307.09104/) (2023)
- [Dual Degradation-Inspired Deep Unfolding Network for Low-Light Image Enhancement](https://mlsanity.com/paper/2308.02776/) (2023)

## Abstract (arXiv)

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.