Video image rain removing method based on convolutional neural network

Abstract

The invention belongs to the field of image processing and computer vision, and provides a video image rain removing method in order to improve the visual effect of video images. The video image rainremoving method based on a convolutional neural network includes the following steps: selecting several consecutive frames of images; extracting the brightness component of each frame and the corresponding high-frequency component of the image; inputting the high-frequency component images to a constructed and trained convolutional neural network to get high-frequency non-rain component images processed by the convolutional neural network; and finally, synthesizing the non-rain component images and the retained low-frequency components to get rain-removed video images, wherein the specific relation of the convolutional neural network is D(P)=||hp(I)-J||<2><2>, hp(.) represents the convolutional neural network, P represents network parameters, I represents the original rain images, J represents the rain-free images, and the convolutional neural network is trained to minimize the value of D(P) so as to get an optimal parameter value P* and get rain-removed images J-^=hp*(I). The method is mainly applied to the occasion of image processing.

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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