A depth iterative illumination estimation network for low-light image enhancement based on retinex theory

Existing low-light image enhancement techniques face challenges in achieving high visual quality and computational efficiency, as well as in effectively removing noise and adjusting illumination in extremely dark scenes. To address these problems, in this paper, we propose an illumination enhancement network based on Retinex theory for fast and accurate brightening of images in low-illumination scenes. Two learning-based networks are carefully constructed: decomposition network and enhancement network. The decomposition network is responsible for decomposing the low-light input image into the initial reflectance and illumination map. The enhanced network includes two sub-modules: the illumination enhancement module and the reflection denoising module, which are used for efficient brightness enhancement and accurate reflectance. Specially, we have established a cascaded iterative lighting learning process and utilized weight sharing to conduct accurate illumination estimation. Additionally, unsupervised training losses are defined to improve the generalization ability of the model. The proposed illumination enhancement framework enables noise suppression and detail preservation of the final decomposition results. To establish the efficacy and superiority of the model, on the widely applicable LOL dataset, our approach achieves a significant 9.16% increase in PSNR compared to the classical Retinex-Net, and a remarkable enhancement of 19.26% compared to the latest SCI method.

Automated summary

In this paper, we propose an illumination enhancement network based on Retinex theory to brighten low-light images in a fast and accurate manner. The network consists of two parts: a decomposition network for separating the input image into reflectance and illumination, and an enhancement network for brightness enhancement and reflection denoising. The proposed framework utilizes cascaded iterative lighting learning and unsupervised training losses to improve illumination estimation and generalization ability. Experimental results demonstrate the effectiveness and superiority of the proposed method compared to classical and state-of-the-art methods.