JCERJournal of Contemporary Educational Research2208-84662208-8474Bio-Byword Scientific Publishing Pty. Ltd.10.26689/jcer.v9i11.12894ArticleLow-Light Image Enhancement Based on Wavelet Local and Global Feature Fusion Networkhttps://artdesignp.com/journal/JCER/9/11/10.26689/jcer.v9i11.12894SongShun,JiangXiangqian,ZhaoDawei20259112025-12-08A 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.

Pizer SM, Amburn EP, Austin JD, et al., 1987, Adaptive Histogram Equalization and Its Variations. Computer Vision, Graphics, and Image Processing, 39(3): 355–368.

Narendra PM, Fitch RC, 1978, Real-Time Adaptive Contrast Enhancement For Imaging Sensors, Proceedings of SPIE - The International Society for Optical Engineering, 137.

Reza AM, 2004, Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement. Journal of VLSI Signal Processing Systems for Signal, Image and Video Technology, 38: 35–44.

Kim YT, 1997, Contrast Enhancement Using Brightness Preserving Bi-Histogram Equalization. IEEE Transactions on Consumer Electronics, 43(1): 1–8.

Fu X, Zeng D, Huang Y, et al., 2016, A Fusion-Based Enhancing Method for Weakly Illuminated Images. Signal Processing, 129: 82–96.

Wang S, Zheng J, Hu HM, et al., 2013, Naturalness Preserved Enhancement Algorithm for Non-Uniform Illumination Images. IEEE Transactions on Image Processing, 22(9): 3538–3548.

Fu X, Zeng D, Huang Y, et al., 2016, A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.

Guo X, Li Y, Ling H, 2016, LIME: Low-Light Image Enhancement via Illumination Map Estimation. IEEE Transactions on Image Processing, 26(2): 982–993.

Lore KG, Akintayo A, Sarkar S, 2017, LLNet: A Deep Autoencoder Approach to Natural Low-Light Image Enhancement. Pattern Recognition, 61: 650–662.

Zhang Y, Zhang J, Guo X, 2019, Kindling the Darkness: A Practical Low-Light Image Enhancer, Proceedings of the 27th ACM International Conference on Multimedia, 1632–1640.

Li J, Li J, Fang F, et al., 2020, Luminance-Aware Pyramid Network for Low-Light Image Enhancement. IEEE Transactions on Multimedia, 23: 3153–3165.

Li C, Guo C, Chen CL, 2021, Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation. IEEE Transactions on Software Engineering, (99).