期刊文献+

融合残差及通道注意力机制的单幅图像去雨方法 被引量:6

Single Image Rain Removal Method by Fusing Residual and Channel Attention Mechanism
下载PDF
导出
摘要 为了去除雨天图像上附着的雨滴并恢复图像的清晰度,提出一种基于深度学习思想结合图像增强技术融合残差及通道注意力机制来实现的单幅图像去雨方法。首先,利用导向滤波将有雨图像分解为平滑基本层和高频细节层;其次,提出自适应Gamma校正算法增强平滑基本层以提高对比度;然后,构建融合残差块和通道注意力机制的深度神经网络实现高频细节层去雨;最后,将去雨后的高频细节层与增强后的平滑基本层融合实现单幅图像去雨功能。实验结果表明:与具有代表性的单幅图像去雨方法相比,所提方法效果较好并可保留更多的图像细节信息。 In order to remove the raindrops and restore the image sharpness,a single image rain removal method based on depth learning and image enhancement technology combined with residual and channel attention mechanism is proposed. Firstly,the rainy image is decomposed into the smooth base layer and the high-frequency detail layer by using the guided filter. Secondly,an adaptive gamma correction algorithm is proposed to enhance the smooth base layer to improve contrast. Thirdly,the deep neural network with residual block and the channel attention mechanism is constructed to remove rain in the high-frequency detail layer. Finally,the high-frequency detail layer after rain removal is combined with the enhanced smooth base layer to realize the single image rain removal. The experimental results show that compared with the representative single image rain removal method,the proposed method works well and can retain more image detail information.
作者 张世辉 闫晓蕊 桑榆 ZHANG Shi-hui;YAN Xiao-rui;SANG Yu(School of Information Science and Engineering,Yanshan University,Qinhuangdao,Hebei 066004,China;The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province,Qinhuangdao,Hebei 066004,China)
出处 《计量学报》 CSCD 北大核心 2021年第1期20-28,共9页 Acta Metrologica Sinica
基金 国家自然科学基金(61379065) 河北省自然科学基金(F2019203285)。
关键词 计量学 单幅图像去雨 图像处理 压缩和激励残差网络 注意力机制 深度学习 GAMMA校正 metrology single image rain removal image processing squeeze and excitation residual network attention mechanism deep learning Gamma correction
  • 引文网络
  • 相关文献

参考文献2

二级参考文献19

共引文献17

同被引文献33

引证文献6

二级引证文献3

;
使用帮助 返回顶部