Open Access

ARTICLE

Single Image Deraining Using Dual Branch Network Based on Attention Mechanism for IoT

Di Wang, Bingcai Wei, Liye Zhang^*

School of Computer Science and Technology, Shandong University of Technology, Zibo, 255000, China

* Corresponding Author: Liye Zhang. Email:

(This article belongs to the Special Issue: Federated Learning Algorithms, Approaches, and Systems for Internet of Things)

Computer Modeling in Engineering & Sciences 2023, 137(2), 1989-2000. https://doi.org/10.32604/cmes.2023.028529

Received 23 December 2022; Accepted 13 February 2023; Issue published 26 June 2023

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.

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Keywords

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