A synthetic rain graph rain removing method based on dictionary training and sparse representation

Abstract

The invention discloses a synthetic rain map rain removal method based on dictionary training and sparse representation, which is applied to rain removal recovery of a synthetic rain map by improvinga method applied to image super-resolution reconstruction. In the training stage, a pure rain template is used for adding rain to a group of rain-free images, a rain-rain-free training set is constructed, and a rain dictionary and a rain-free dictionary are obtained through training; and in the test stage, applying another pure rain template to rain the test rain-free image to obtain a test synthetic rain map, and based on the rain dictionary, performing sparse representation on the test synthetic rain map to obtain a sparse representation coefficient of the test synthetic rain map, obtaininga third different sparse representation coefficient of the pure rain template based on the rain dictionary, and subtracting the two sparse representation coefficients to further remove rain-related components in the representation coefficients, and finally, combining the subtracted sparse representation coefficients with the trained rain-free dictionary to obtain a final rain removal result of thetest synthesis rain map. According to the method, detail information in the image can be well protected while the image is subjected to rain removal.

Description

technical field [0001] The invention belongs to the field of digital image processing, and in particular relates to a rain removal method based on dictionary training and sparse representation for synthesizing rain images. Background technique [0002] In today's information age, outdoor visual acquisition systems are widely used in traffic safety, security monitoring, remote sensing observation and other fields. The collected high-resolution images not only bring people a better visual experience, but also can record more scene information. The acquisition of high-resolution images will not only be limited by the hardware of the imaging device, but also greatly affected by the external acquisition environment. When severe weather such as rain occurs, the observation visibility will be significantly reduced, resulting in the degradation of the image quality collected by the camera or sensor, which will adversely affect the integrity of the image information and subsequent i...

AI Technical Summary

Problems solved by technology

Compared with video image rain removal, single image rain removal is theoretically a pathological problem and is more challenging. More mature technical methods in this regard are rare

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