Image rain removing method based on multi-scale progressive fusion

Abstract

The invention discloses an image rain removal method based on multi-scale progressive fusion. The method comprises pyramid decomposition of a rain image, relevance learning of rain stripes, and progressive fusion and reconstruction of multi-scale features. In the pyramid decomposition process of the rain image, Gaussian sampling operators of different scales are utilized to perform sampling decomposition on the original rain image; in the correlation learning process of the rain stripes, learning global texture feature correlation is learned by using a non-local network; in the progressive fusion and reconstruction process of the multi-scale features; the multi-scale pyramid network is used for processing the features of the corresponding scales, and meanwhile, the multi-scale rain stripeinformation is gradually fused to assist the feature expression of the highest pyramid layer, so that the multi-scale fusion of the rain stripe information is realized, the residual rain image is reconstructed, and the residual image is subtracted from the rain image to obtain the rain-free image. According to the method, tThe correlation between the rain stripes in the same-scale image and the rain stripes in different-scale images is effectively utilized, the rain stripes are more accurately modeled, and a better rain removal effect is achieved.

Description

technical field [0001] The invention belongs to the technical field of digital images, and relates to an image deraining method, in particular to an image deraining method based on multi-scale progressive fusion. Background technique [0002] The image or video data obtained in rainy days suffers from severe degradation, which greatly affects the quality and readability of the image or video content, thereby interfering with the accuracy of downstream high-level computer vision tasks. Therefore, single image rain streak removal is a fundamental process to improve the visual experience of images, and it is also an important preprocessing step for many computer vision tasks (such as segmentation, detection, tracking, recognition, classification, etc.). [0003] Since deep learning, especially convolutional neural network (CNN) has achieved good results in the field of image processing, image rain removal methods based on deep learning have been proposed and developed rapidly i...

AI Technical Summary

Problems solved by technology

Due to insufficient development and utilization of multi-scale rain streak information, existing deraining methods cannot produce ideal inpainting results for complex rainfall scenes

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