A method and system for data augmentation of microscopic images of wool and cashmere

By performing masking, completion, and fusion processing on microscopic images of wool and cashmere, high-quality microscopic images of wool and cashmere fibers were generated, solving the problem of insufficient data, achieving natural texture preservation and data enhancement, and supporting the training of deep neural networks.

CN120953128BActive Publication Date: 2026-07-17INNER MONGOLIA UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
INNER MONGOLIA UNIV OF TECH
Filing Date
2025-07-22
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

The existing technology lacks sufficient data on wool and cashmere microscope images, leading to overfitting in deep neural network training. Furthermore, traditional data augmentation methods have limited applicability to microscope images, especially since electron microscope images are expensive to produce.

Method used

By processing microscopic images of wool and cashmere, fiber masks are obtained, fiber foreground and noisy background images are separated, the noisy background image is completed, the fiber foreground image is simulated to be randomly placed, and then it is fused with the completed background image to generate a new microscopic image.

Benefits of technology

It effectively generates highly natural microscopic images of wool and cashmere fibers, preserving the texture details of the fiber surface, and provides rich image data sources to alleviate overfitting, thus providing high-quality data support for automatic wool and cashmere identification.

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Abstract

This invention discloses a data augmentation method and system for wool and cashmere microscope images. First, the wool and cashmere microscope image is processed to obtain a wool and cashmere fiber mask. Then, based on the fiber mask, the wool and cashmere microscope image is divided into a fiber foreground image and a noisy background image. Next, the noisy background image is completed to obtain a completed noisy background image. Subsequently, the fiber foreground image is simulated with random placement to obtain a random fiber foreground image. Finally, the random fiber foreground image and the completed noisy background image are fused to obtain a new wool and cashmere microscope image. This invention effectively generates highly natural wool and cashmere fiber microscope images while preserving the texture details of the fiber surface and simulating different positions of the fibers in a noisy background, providing a high-quality image source for developers of automatic wool and cashmere recognition programs.
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