Video image rain removal method based on convolutional neural network

Abstract

The invention belongs to the field of image processing and computer vision technology. In order to improve the video image visual effect of the video image deraining method, the present invention, the video image deraining method based on the convolutional neural network, first select several frames of continuous images, and extract each frame of image The brightness component of the image and the corresponding high-frequency component of the image, and then input the high-frequency component image into the constructed and trained convolutional neural network, and then obtain the high-frequency non-rain component image processed by the convolutional neural network, and finally the non-rain component The image and the retained low-frequency components are integrated to obtain the video image after rain removal, and the specific relationship of the convolutional neural network is: where, h P ( ) represents the convolutional neural network, P represents the network parameters, I represents the original rainy image, and J represents the rainless image. By training the convolutional neural network, the value of D(P) is minimized and the optimal parameter value is obtained. P * , and then obtain the rain-removed image. The present invention is mainly applied to image processing occasions.

Description

technical field [0001] The invention belongs to the technical fields of image processing and computer vision, and in particular relates to a method for removing rain from a video image based on a convolutional neural network. Background technique [0002] With the rapid development of computer science and technology and the maturity of image processing technology, the computer vision system can obtain a large amount of rich and high-resolution image information in time due to its accuracy, speed, reliability, and intuitive, real-time, and comprehensive reflection of the monitored object. Advantages, especially in some occasions that are not easy for humans to directly observe, it can solve the problem of difficult observation, and is widely used in various fields. However, outdoor video images acquired under rainy weather conditions will be adversely affected by the weather environment. Rain lines will blur the captured outdoor video images, causing the image to lose origin...

AI Technical Summary

Problems solved by technology

The existing rain line features can accurately detect the position of the rain line to a certain extent, but due to the complexity and diversity of the video image scene, the effect of the video image rain removal algorithm is still not ideal under many conditions

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