A post-processing method for multi-level iterative collaborative representation of rain-removed images
By employing a post-processing method based on multi-level iterative collaborative representation, combined with Hessian-Affine and SIFT algorithms to remove mismatches, and utilizing a deraining network to optimize the image, the problem of blurred details in derained images in existing technologies is solved, achieving more efficient and accurate image restoration.
CN117218008BActive Publication Date: 2026-03-06BEIJING CREATUNION INFORMATION TECH CO LTD
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Patent Information
- Application Number
- CN202310323984.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-03-29
- Publication Date
- 2026-03-06
- Estimated Expiration
- 2043-03-29
AI Technical Summary
Technical Problem
Existing methods for de-raining images tend to blur image details during processing, and have low processing efficiency and accuracy, failing to effectively recover blurred details in the image.
Method used
A post-processing method using multi-level iterative collaborative representation is adopted. Key points are extracted by Hessian-Affine feature point detection and SIFT local feature descriptor. A graph feature matrix is constructed to remove mismatches. A rain removal network is used for multi-level iterative optimization to finally obtain a clear image.
Benefits of technology
It improves the accuracy and efficiency of image processing, enabling faster and more precise removal of rainwater interference and restoration of image detail information.
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Abstract
This invention discloses a post-processing method for multi-level iterative collaborative representation of derained images, comprising the following steps: obtaining an initial image set, which includes several sets of derained images and corresponding rainless images, wherein the derained images are images obtained after a deraining preprocessing algorithm on rainy images, and the rainless images are images collected for the corresponding scene that do not contain rain streaks; constructing an algorithm matrix to remove mismatches; obtaining the images after removing mismatches, and using them to construct deraining network data, and obtaining the final derained image based on it. This invention combines the Hessian-Affine and SIFT algorithms, focusing on the strong correlation between feature point neighborhood information and feature points, resulting in higher feature matching accuracy during feature matching, and providing more refined assistance for image post-processing.
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