现代信息科技2024，Vol.8Issue(21)：50-56,7.DOI:10.19850/j.cnki.2096-4706.2024.21.011
基于深度卷积神经网络的低照度图像增强方法
Low Illumination Image Enhancement Method Based on Deep Convolutional Neural Networks
摘要
Abstract
The image details and textures under low illumination conditions are difficult to distinguish,resulting in serious information loss.The traditional enhancement method requires a lot of manual adjustment for parameters,low efficiency and no prominent details after enhancement.To solve this problem,a low illumination image enhancement model based on Convolutional Neural Networks(CNN)is proposed.It automatically learns the decomposition and enhancement of low illumination image through a data-driven network structure,and updates model parameters by end-to-end training.This model includes modules of decomposition network,illumination adjustment network and noise reduction,and Convolutional Block Attention Module(CBAM)is added to the decomposition network and the illumination adjustment network,to capture important information in the image more comprehensively.This model firstly decomposes the image into the illumination component and the reflection component by the decomposition network,and then inputs the illumination adjustment network and noise reduction module respectively for processing,and finally reconstructs to obtain the enhanced image.The experimental results demonstrate that compared to other common enhancement algorithms,this method effectively improves the contrast and texture details of low illumination image,providing clearer and more reliable image quality.关键词
低照度图像/图像增强/卷积神经网络/CBAM注意力机制Key words
low illumination image/image enhancement/CNN/CBAM分类
信息技术与安全科学引用本文复制引用
徐俊,戎舒畅,李墨,刘煊,刘昭含,吴镇..基于深度卷积神经网络的低照度图像增强方法[J].现代信息科技,2024,8(21):50-56,7.基金项目
中国矿业大学(北京)大学生创新训练项目(202304070,202414021) （北京）
