@Article{cmes.2023.028529,
AUTHOR = {Di Wang, Bingcai Wei, Liye Zhang},
TITLE = {Single Image Deraining Using Dual Branch Network Based on Attention Mechanism for IoT},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {137},
YEAR = {2023},
NUMBER = {2},
PAGES = {1989--2000},
URL = {http://www.techscience.com/CMES/v137n2/53349},
ISSN = {1526-1506},
ABSTRACT = {Extracting useful details from images is essential for the Internet of Things project. However, in real life, various
external environments,such as badweather conditions,will cause the occlusion of key target information and image
distortion, resulting in difficulties and obstacles to the extraction of key information, affecting the judgment of the
real situation in the process of the Internet of Things, and causing system decision-making errors and accidents.
In this paper, we mainly solve the problem of rain on the image occlusion, remove the rain grain in the image,
and get a clear image without rain. Therefore, the single image deraining algorithm is studied, and a dual-branch
network structure based on the attention module and convolutional neural network (CNN) module is proposed
to accomplish the task of rain removal. In order to complete the rain removal of a single image with high quality,
we apply the spatial attention module, channel attention module and CNN module to the network structure, and
build the network using the coder-decoder structure. In the experiment, with the structural similarity (SSIM)
and the peak signal-to-noise ratio (PSNR) as evaluation indexes, the training and testing results on the rain
removal dataset show that the proposed structure has a good effect on the single image deraining task.},
DOI = {10.32604/cmes.2023.028529}
}