计算机科学
块(置换群论)
图层(电子)
特征(语言学)
计算机视觉
GSM演进的增强数据速率
残余物
迭代法
人工智能
图像复原
图像质量
图像(数学)
算法
图像处理
数学
哲学
有机化学
化学
语言学
几何学
作者
Yong Yang,Juwei Guan,Shuying Huang,Weiguo Wan,Yating Xu,Jiaxiang Liu
标识
DOI:10.1109/tmm.2021.3068833
摘要
Methods of rain removal based on deep learning have rapidly developed, and the image quality after rain removal is continuously improving. However, the results of most methods have some common problems, including a loss of details, a blurring of edges, and the existence of artifacts. To remove rain-related information more thoroughly and retain more edge details, this paper proposes an end-to-end rain removal network based on the progressive residual detail supplement (ERRN-PRDS) approach. The entire network structure is designed in an iterative manner to obtain higher-quality rain removal images from coarse to fine. In the network, a diamond residual block is constructed as the main module of iteration to learn the feature information of the background layer. Meanwhile, to keep more texture details in the background layer, a detail supplement mechanism is designed between the iterative layers to transfer more information to the next iterative operation. Experimental results show that this method can remove the rain information more completely and better retain the image edges compared with previous state-of-the-art methods. In addition, because of the sparsity of the detail injection, our network also achieves high-quality results for image denoising tasks.
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