Image rain removal method and system

Abstract

The invention relates to an image rain removal method and system. The method comprises the steps: S1, constructing an image training database, wherein the image training database comprises a pluralityof no rain-rain-only rain grain image pairs; S2, according to the no rain-rain-only rain grain image pairs in the image training database, constructing a twin convolution network structure for rain removal; S3, performing filtering on an image to be subjected to rain removal to obtain high frequency information and low frequency information of the image to be subjected to rain removal; and S4, inputting the high frequency information of the image to be subjected to rain removal into the twin convolution network structure for rain removal to obtain a high frequency information of a corresponding no-rain image, and adding the low frequency information of a rain image into the obtained high frequency information of a no-rain image to obtain the corresponding no-rain image. According to the method, the twin convolution network structure is constructed by the no rain-rain-only rain grain image pairs, operation is simplified, the construction processing speed is high, the instantaneity is good, and the clear no-rain image can be obtained by constructing the twin convolution network structure with high robustness.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a method and system for removing rain from an image. Background technique [0002] With the rapid development of modern information technology, people hope to obtain clearer images, and for this purpose, it is usually necessary to remove the rain streaks in the image. [0003] The traditional image deraining method mainly adopts the method based on sparse dictionary learning. The core of this method is to learn a target rain pattern sparse dictionary from the synthesized rain pattern library, and distinguish the rain pattern from the background through the target rain pattern sparse dictionary. image. However, this method needs to continuously introduce new target features to increase the discrimination of dictionary classification, which increases the complexity of the algorithm, takes a long time to calculate, and has low real-time performance. Contents of the invention [0...

AI Technical Summary

Problems solved by technology

However, this method needs to continuously introduce new target features to increase the discrimination of dictionary classification, which increases the complexity of the algorithm, takes a long time to calculate, and has low real-time performance.

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