Multi-Modular Network-Based Retinex Fusion Approach for Low-Light Image Enhancement

Current low-light image enhancement techniques prioritize increasing image luminance but fail to address issues including loss of intricate distortion of colors and image details. In order to address these issues that has been overlooked by all parties, this paper suggests a multi-module optimization network for enhancing low-light images by integrating deep learning with Retinex theory. First, we create a decomposition network to separate the lighting components and reflections from the low-light image. We incorporated an enhanced global spatial attention (GSA) module into the decomposition network to boost its flexibility and adaptability. This module enhances the extraction of comprehensive information from the image and safeguards against information loss. To increase the illumination component's luminosity, we subsequently constructed an enhancement network. The Multiscale Guidance Block (MSGB) has been integrated into the improvement network, together with multilayer extended convolution to expand the sensing field and enhance the network's capability for feature extraction. Our proposed method out-performs existing ways in both objective measures and personal evaluations, emphasizing the virtues of the procedure outlined in this paper.