Single-image rain removing method based on convolutional neural network double-branch attention generation

Abstract

The invention discloses a single-image rain removal method based on convolutional neural network double-branch attention generation. The method comprises the steps of preprocessing an input image; constructing a U-shaped structure network; adding the attention of the weight channel to the first U-shaped network to obtain the added first U-shaped network; adding the spatial attention and the channel attention to a second U-shaped structure network to obtain an added second U-shaped network; adopting the added first U-shaped network to process the obtained processed image a, adopting the added second U-shaped network to process the obtained processed image b, adding the processed image b and the preprocessed image, and obtaining a convolutional neural network model through convolution; training a convolutional neural network model by using the preprocessed image, and constraining by using a loss function to obtain a trained rain removal network model; and putting the to-be-processed image with rain into the trained rain removal network model, and finally outputting the rain-removed image, thereby improving the rain removal performance of the single image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a method for removing rain from a single image based on double-branch attention generation of a convolutional neural network. Background technique [0002] With the rapid development of science and technology, human society is entering the information society, the application of computers is becoming more and more extensive, and image processing technology is becoming more and more important to the development of various fields. Computer vision has become a key technology in the fields of robotics, defense aviation, and machine vision automation. At the same time, it also plays an important role in monitoring images, assisted driving central control systems, traffic gateways and other scenarios. With the rapid development of image processing technology and computer vision technology, more and more computer vision systems are used in many scientific and engineering fields....

AI Technical Summary

Problems solved by technology

Therefore, the attachment of raindrops to the image will obviously affect the visual effect and image quality of the image, which will indirectly affect the performance of the entire computer vision system.

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