Image rain removal method based on space-frequency double-domain perception

By using space-frequency dual-domain perception technology in the image rain removal method and using the feature information of the spatial domain and the frequency domain to fusion, the problem of difficulty in maintaining image details in complex rainy scenes in the prior art is solved, and better rain removal effect and image quality are achieved.

CN119784637APending Publication Date: 2025-04-08EAST CHINA UNIV OF SCI & TECH
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Patent Information

Application Number
CN202411867860.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-18
Publication Date
2025-04-08

AI Technical Summary

Technical Problem

The existing image rain removal method is difficult to maintain the clear texture and details of the image when dealing with complex rainy scenes, resulting in unsatisfactory rain removal effect.

Method used

The image rain removal method based on space frequency dual-domain perception is adopted, and information fusion is carried out by simultaneously utilizing the characteristic information of the spatial domain and the frequency domain. The spatial domain is used to capture the global semantic structure of the image, while the frequency domain can effectively suppress frequency noise caused by raindrops and rain patterns.

Benefits of technology

While removing raindrops and rain patterns, maintaining the clear texture and details of the image, significantly improving the rain removal effect and improving the quality and structural integrity of the image.

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Abstract

The invention relates to the technical field of image processing, and provides an image rain removal method based on space-frequency double-domain perception, which comprises the following steps of: 1, constructing an image rain removal network based on space-frequency double-domain perception; 2, training the image rain removal network model in the step 1 until the model converges; and step 3, for any acquired rainy day scene image, reasoning by using the rain removal network trained in the step 2 to obtain a rain-free background image. Compared with the prior art, the rain-removed image generated by the method has fewer rain stripe residues and clearer texture features, and the problems of rain stripe residues, blurred background and the like in an existing image rain-removing method can be effectively solved.
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