De-raining method of single image based on sparse and low-rank matrix decomposition

Abstract

The invention provides a de-raining method of a single image based on sparse and low-rank matrix decomposition. The de-raining method is characterized in that the method comprises following steps: step 1, inputting a rain-containing image, decomposing the rain-containing image into 8*8 image blocks, taking the center of the image blocks as the origin, forming an input matrix by 8 image blocks with 4 of pixel deviation, and performing sparse and low-rank matrix decomposition on the input matrix, wherein a low-rank matrix is regarded as a low-frequency component, and a sparse matrix is regarded as a high-frequency component; step 2, dividing the high-frequency component into a plurality of mutually overlapped high-frequency sub-blocks, obtaining a dictionary by learning through a dictionary learning method, and dividing the dictionary into a rain portion dictionary and a geometric portion dictionary according to HOG characteristics; and step 3, dividing a high-frequency image into a plurality image sub-blocks which are not mutually overlapped after obtaining the rain portion dictionary and the geometric portion dictionary, adding geometric portions in a geometric component and a rain component, and forming a de-rained output image through merging, wherein each image sub-block is represented by the geometric component and the rain component.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a method for removing rain from a single image based on sparse and low-rank matrix decomposition. Background technique [0002] In recent years, with the rapid development of computer science and technology, outdoor vision systems have been widely used in traffic monitoring, driving assistance systems and other fields. However, bad weather, such as rain, snow, fog, etc., will reduce the contrast of the captured image, blur the image, and lose detailed information, which seriously affects the performance of the outdoor vision system. Among them, rainy day is a common bad weather in life, and it has important practical significance and wide application value to perform clear processing such as deraining on the image captured in rainy day. [0003] According to different methods of researching rain removal, these methods can be divided into two directions: video-based rain removal me...

AI Technical Summary

Problems solved by technology

However, the above-mentioned methods have some common shortcomings: first, the final deraining effect of a single image is heavily dependent on dictionary classification; second, the above-mentioned methods must be filtered once, and the size of the filter parameters directly affects the final reconstruction effect, while the traditional method is often based on experience, assigning fixed values ​​​​to the parameters, which cannot be adjusted according to the specific conditions of the image.

[0007] like figure 1 As shown, the existing single image rain removal method using sparse representation, the first step of single image rain removal is mainly carried out by filtering, which has problems such as poor adaptability

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