A noise suppression method and system for hyperspectral medical image acquisition

CN115760773BActive Publication Date: 2026-06-26SHANDONG UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SHANDONG UNIV
Filing Date
2022-11-21
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

In existing technologies, the noise suppression effect of hyperspectral medical images during the acquisition process is not ideal, which affects the image's resolvability and quality, especially the difficulty in distinguishing edge and detail features.

Method used

A pre-trained CNN network is used to screen hyperspectral medical images, and a noise reduction algorithm combining median filtering and wavelet transform is used. The algorithm includes steps such as ambient light noise preprocessing, CNN network screening, median filtering, and wavelet transform decomposition and reconstruction to remove noise interference.

Benefits of technology

It enhances the edge and detail features of hyperspectral medical images, improves imaging quality, and effectively suppresses noise interference.

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Abstract

The application discloses a noise suppression method and system for hyperspectral medical image acquisition, and relates to the technical field of hyperspectral imaging signal processing. An initial hyperspectral medical image is obtained by pretreating a sample to be detected; a pre-trained CNN network is used to screen the initial hyperspectral medical image; a denoising algorithm combining median filtering and wavelet transform is used to perform median filtering on the screened initial hyperspectral medical image, then the filtered image is decomposed through wavelet transform to obtain a wavelet coefficient matrix, a new wavelet coefficient matrix is generated according to the principle of median filtering, image reconstruction is performed through the obtained new coefficient matrix, and finally, a final hyperspectral medical image after noise suppression is obtained according to wavelet threshold denoising. The application can enhance the edge and detail features of the hyperspectral medical image and improve the imaging quality.
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