ILR-Net: Low-light image enhancement network based on the combination of iterative learning mechanism and Retinex theory.

ABSTRACT: Images captured in nighttime or low-light environments are often affected by external factors such as noise and lighting. Aiming at the existing image enhancement algorithms tend to overly focus on increasing brightness, while neglecting the enhancement of color and detailed features. This paper proposes a low-light image enhancement network based on a combination of iterative learning mechanisms and Retinex theory (defined as ILR-Net) to enhance both detail and color features simultaneously. Specifically, the network continuously learns local and global features of low-light images across different dimensions and receptive fields to achieve a clear and convergent illumination estimation. Meanwhile, the denoising process is applied to the reflection component after Retinex decomposition to

SUBMITTER: Yin M 

PROVIDER: S-EPMC11825054 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature