An image rain removing method and system based on a static rain pattern

Abstract

The invention relates to an image rain removing method and system based on a static rain pattern, comprising the following steps of: constructing an image training database; according to the rain-purerain pattern in the image training database, constructing the static rain pattern detection network structure; filtering the image for rain removal to obtain high-frequency information and low-frequency information of the image to be rained; inputting the high frequency information of the image for rain removel into the static rain pattern detection network structure to obtain the high frequencyinformation of the rain pattern; Subtracting the high-frequency information of the rain pattern from the high-frequency information of the rainless image to obtain the high-frequency information of the rainless image; adding the high-frequency information of the rain-free image and the low-frequency information of the image for rain removal to obtain the rain-free image. The invention improves the detection capability of the static rain ripple detection network structure by removing the low-frequency information and other interference network function items. While retaining the main rainpattern feature information, it reduces the static rainpattern detection network parameters and computational load, simplifies the operation, constructs the processing speed is fast, and improves the generalization ability of the depth network model in real-time.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an image deraining method and system based on static rain streaks. 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 based on static rain pattern 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 use the target rain pattern sparse dictionary to Differentiate between rain streaks and background images. 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 per...

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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