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Sparse representation-based single-image rain elimination method

A single image, sparse representation technology, applied in the field of computer vision, can solve the problems of insufficient edge information, insufficient dictionary clustering effect, image loss of detailed information, etc., to achieve good detailed information and avoid large learning residuals in the dictionary Effect

Inactive Publication Date: 2017-03-15
TIANJIN UNIV
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Problems solved by technology

This will cause the effect of dictionary clustering to be not good enough, and some atoms that should have been geometric dictionaries are misclassified into rain dictionaries, resulting in the loss of some detailed information in the final image, and the edge information is not obvious enough

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Embodiment Construction

[0035] Below is the concrete technical scheme that this patent proposes.

[0036] It can be seen from the previous method that the performance of deraining mainly depends on the learning and clustering of the dictionary. Therefore, this patent starts from the residual error of the dictionary learning and the characteristics of the rain line, and improves these two parts to improve the deraining performance of a single image. The effect of rain.

[0037] The traditional single image rain removal method mainly includes image decomposition, sparse representation, and dictionary learning. The following three technologies are briefly introduced.

[0038] 1. Image decomposition

[0039] The theoretical basis of image decomposition is morphological component analysis, which uses the morphological diversity of different features in the data, decomposes it, and combines each morphological component with the atoms in the dictionary. Assuming an image I with N pixels, it consists of K ...

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Abstract

The present invention relates to a sparse representation-based single-image rain elimination method. The method includes the following steps that: the low-frequency part and high-frequency part of an original image are obtained; non-overlapping partitioning is performed on the high-frequency part; online dictionary learning is carried out, so that a corresponding dictionary DHF composed of a plurality of sub-blocks is obtained; each of the sub-blocks of the DHF are divided into two categories by using an affinity propagation clustering method, wherein one category represents the geometric component information of the high-frequency part, and the other category represents the rain component information in the high-frequency part; the color distribution and edge direction of each sub-block are extracted, and each of the sub-blocks of the formed rain dictionary of the high-frequency part are classified again; a rain component in the high-frequency part is restored; the geometric component of the high-frequency part is obtained; and a rain-eliminated image can be obtained. The method of the invention has a better rain elimination effect.

Description

technical field [0001] The invention belongs to the field of computer vision and relates to a method for removing rain from a single image. 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 methods and single image-based rain removal methods. A...

Claims

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T2207/20028G06T2207/20221G06T5/77G06T5/73
Inventor 周俊庞彦伟
Owner TIANJIN UNIV
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