Marine phytoplankton image edge detection method based on non-local mean denoising mode

By using non-local mean denoising algorithm and Canny edge detection algorithm in phytoplankton image processing, the problems of phytoplankton image noise and edge blur are solved, and clearer and more accurate edge extraction is achieved.

CN119722720AInactive Publication Date: 2025-03-28SHANDONG JIANZHU UNIV
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Patent Information

Application Number
CN202510233240.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-02-28
Publication Date
2025-03-28
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Due to instability of light source, sensor noise and optical error, the phytoplankton images contain strong noise and blurred edges, which affects the accuracy of the subsequent analysis process.

Method used

The non-local mean denoising method is adopted, combined with the Canny edge detection algorithm, the edge details of the image are preserved through the non-local mean denoising algorithm, and the image is edge extracted using the Canny edge detection algorithm.

Benefits of technology

Effectively remove noise, preserve image details and structural information, reduce the impact of noise on edge detection, and significantly improve the accuracy and image quality of edge extraction.

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Abstract

The invention relates to the field of phytoplankton image edge detection, in particular to a marine phytoplankton image edge detection method based on a non-local mean denoising mode. The method comprises the following steps: putting a marine phytoplankton image file needing to be detected into a folder of a program item, automatically reading a code into an image in the folder by a program, and loading a to-be-processed image; edge details related to the marine phytoplankton image are reserved based on a non-local mean denoising algorithm; aiming at the image data after the edge details are reserved; carrying out edge extraction on the image based on a Canny edge detection algorithm; and outputting a marine phytoplankton image with edge details reserved. Through combination of non-local mean denoising and Canny edge detection, noise can be more effectively removed, image details and structure information can be kept, and the influence of noise on edge detection is reduced, so that clearer and more accurate edges are extracted; and in an image with relatively large noise, the edge extraction precision and the image quality can be remarkably improved.
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