RetinexGuI: Retinex-Guided Iterative Illumination Estimation Method for Low Light Images

TL;DR

RetinexGuI is a new low-light image enhancement method that efficiently separates and iteratively refines illumination, offering high performance with low computational complexity and potential for real-time applications.

Contribution

It introduces a Retinex-guided iterative framework with linear complexity, improving low-light image enhancement and enabling integration with deep learning.

Findings

Effective enhancement across multiple datasets

Low computational complexity of O(N)

Potential for real-time processing

Abstract

In recent years, there has been a growing interest in low-light image enhancement (LLIE) due to its importance for critical downstream tasks. Current Retinex-based methods and learning-based approaches have shown significant LLIE performance. However, computational complexity and dependencies on large training datasets often limit their applicability in real-time applications. We introduce RetinexGuI, a novel and effective Retinex-guided LLIE framework to overcome these limitations. The proposed method first separates the input image into illumination and reflection layers, and iteratively refines the illumination while keeping the reflectance component unchanged. With its simplified formulation and computational complexity of $\mathcal{O}(N)$, our RetinexGuI demonstrates impressive enhancement performance across three public datasets, indicating strong potential for large-scale applications. Furthermore, it opens promising directions for theoretical analysis and integration with deep learning approaches. The source code will be made publicly available at https://github.com/etuspars/RetinexGuI once the paper is accepted.