亮度信噪比引导Transformer的低照度图像增强  

Low-Light Image Enhancement Using Brightness and Signal-to-Noise Ratio Guided Transformer

作　　者：杜晓刚 路文杰 雷涛[1,2] 王营博 DU Xiaogang;LU Wenjie;LEI Tao;WANG Yingbo(Shaanxi Joint Laboratory of Artificial Intelligence,Shaanxi University of Science and Technology,Xi’an 710021,China;School of Electronic Information and Artificial Intelligence,Shaanxi University of Science and Technology,Xi’an 710021,China)

机构地区：[1]陕西科技大学人工智能联合实验室,西安710021 [2]陕西科技大学电子信息与人工智能学院,西安710021

出　　处：《计算机工程与应用》2025年第6期263-272,共10页Computer Engineering and Applications

基　　金：国家自然科学基金(61861024,62271296,62201334);陕西省教育厅科研计划项目(23JP022,23JP014);陕西省重点研发计划(2021ZDLGY08-07)。

摘　　要：一些低照度图像增强方法生成的增强图像存在亮度不均、去噪效果较差和缺少细节的问题。为了解决上述问题,提出了基于亮度和信噪比引导Transformer的低照度图像增强网络BSGFormer。该网络具有三个优势:设计了亮度信噪比生成子网络,旨在提取全局光照信息和定位信息量缺失的暗区域;通过亮度和信噪比特征引导Transformer,仅对信息量缺失的暗区提取长距离特征以减少计算量,同时引导后续特征融合模块,借助亮区信息来丰富暗区细节并实现信息共享;设计了交叉融合注意力模块并将其引入到编解码器之间,改善网络对图像细节信息的保留能力。在四个公开数据集上进行实验表明,与主流方法相比,BSGFormer在主观视觉和客观评价两方面均得到了更好的增强效果。The enhanced images generated by some existing low-light image enhancement methods have problems such as uneven brightness,poor denoising effect,and lack of detailed information.To solve these issues,this paper proposes a low-light image enhancement network based on brightness and signal-to-noise ratio guided Transformer.This network has the following advantages:a brightness and signal-to-noise ratio generation sub-network is designed to extract global illumination information and locate dark areas with missing information.The Transformer is guided by brightness and signal-to-noise ratio feature maps to extract long-distance features only from dark areas with missing information to reduce the calculation complexity.Meanwhile,the subsequent feature fusion module is guided to enrich the details of dark areas with the help of bright area information and achieve information sharing.A cross-fusion attention module is designed and introduced between the encoder and decoder,thereby the ability of network is improved to retain image details.Experimental results on four public datasets show that BSGFormer can achieve better enhancement effects than the popular methods in both subjective vision and objective evaluation.

关 键 词：低照度图像 图像增强 TRANSFORMER 卷积残差 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]