Digestive endoscopy image deblurring enhancement method and system
By employing fuzzy region classification and parallel processing techniques, the problem of distinguishing and processing localized adhesion blur and global motion blur in gastrointestinal endoscopy images was solved, generating high-quality image enhancement effects and improving diagnostic accuracy.
CN121746243APending Publication Date: 2026-03-27THE SECOND AFFILIATED HOSPITAL OF NANJING UNIV OF TRADITIONAL CHINESE MEDICINE (JIANGSU SECOND HOSPITAL OF TRADITIONAL CHINESE MEDICINE JIANGSU TRAINING CENT FOR TRADITIONAL CHINESE MEDICINE MANAGEMENT CADRES)
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Patent Information
- Application Number
- CN202511974391.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-25
- Publication Date
- 2026-03-27
AI Technical Summary
Technical Problem
Existing endoscopy image processing techniques are unable to effectively distinguish and process localized adhesion blur and global motion blur, resulting in limited enhancement effects on composite blurred images and even introducing new distortions.
Method used
A fuzzy region classification network is used to accurately classify images. High-quality enhanced images are generated by parallel processing of attached fuzzy region restoration and motion fuzzy region sharpening, combined with a multi-scale feature fusion network.
Benefits of technology
It achieves accurate identification and differential processing of complex blur, generating high-quality enhanced images with clear details and true colors, thus improving the clinical diagnostic value.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention relates to the technical field of medical image processing, in particular to a digestive endoscopy image deblurring enhancement method and system.The method comprises the steps that firstly, an input digestive endoscopy original image is processed through a blurred region classification network, and a pixel-level blurred classification map capable of distinguishing an adhesion blurred region and a motion blurred region is generated; then, parallel processing is carried out according to the classification graph: for an adhesion fuzzy region, physical model restoration and color correction are carried out by estimating a transmissivity graph and an ambient light value; for a motion blur region, a self-adaptive non-blind deconvolution kernel is constructed to perform deconvolution sharpness. And finally, inputting the two processing results and the original clear area into a multi-scale feature fusion network together, carrying out adaptive feature weighted fusion and image reconstruction, and outputting a globally clear and detail-enhanced final image. According to the method, accurate identification and targeted enhancement of composite blurring are realized, and the visual quality and diagnosis availability of the digestive endoscopy image are effectively improved.
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