Low-Light Image Enhancement Based on Wavelet Local and Global Feature Fusion Network

Shun Song; Xiangqian Jiang; Dawei Zhao

doi:10.26689/jcer.v9i11.12894

Shun Song Henan Vocational University of Science and Technology, Zhoukou 453003, Henan, China
Xiangqian Jiang Henan Vocational University of Science and Technology, Zhoukou 453003, Henan, China
Dawei Zhao State Grid Zhoukou Power Supply Company, Zhoukou 453003, Henan, China

DOI: https://doi.org/10.26689/jcer.v9i11.12894

Keywords: Image enhancement, Feature fusion, Wavelet transform, Convolutional Neural Network (CNN), Transformer

Abstract

A wavelet-based local and global feature fusion network (LAGN) is proposed for low-light image enhancement, aiming to enhance image details and restore colors in dark areas. This study focuses on addressing three key issues in low-light image enhancement: Enhancing low-light images using LAGN to preserve image details and colors; extracting image edge information via wavelet transform to enhance image details; and extracting local and global features of images through convolutional neural networks and Transformer to improve image contrast. Comparisons with state-of-the-art methods on two datasets verify that LAGN achieves the best performance in terms of details, brightness, and contrast.

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