Microscopic Images Restoration of Chinese Herbal Medicine Using the TV Model

Liu, Qing; Zhang, Lijun; Yang, Xiaoping; Liu, Xiping

doi:10.2174/1874444301507010266

Cited by 2 publications

(1 citation statement)

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“…In the field of Chinese herbal medicine identification in China, there have been some preliminary developments in the method of using computer to process Chinese herbal medicine data. Hou Qing [3] et al processed the image with an improved median filter algorithm and then performed edge extraction to better preserve the privet. Details and edge features of leaf cell images; Sun Huiting [4] et al extracted shape features and texture features through Fourier algorithm and grayscale co-occurrence matrix algorithm, and used DBT-SVM to classify the above features, and solved the problem of Cortex Moutan.…”

Section: Introductionmentioning

confidence: 99%

Research on image classification of Chinese medicinal materials powder based on texture extraction and improved lightweight network

Wang

Tao

et al. 2023

Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022)

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In recent years, a large number of mixed products, counterfeit products and adulterated products of traditional Chinese medicine powder have often appeared on the market. And because the traditional image processing algorithm cannot meet the application requirements for such complex background, large amount of data, and multi-category problem processing accuracy and speed. In view of the above problems, this paper proposes an improved method of deep convolutional neural network based on texture extraction and improved attention mechanism model. First, the MobileNetV3 network is unable to fully extract the texture information of fiber powder, and an inception-like structure is added to significantly improve the final recognition accuracy; secondly, the use of hole convolution minimizes the impact of the inception-like structure on the overall parameter quantity. Influence: finally, an improved attention mechanism module is added to the network, which significantly suppresses the noise texture in the background image. The fiber characteristics of 32 different kinds of Chinese herbal medicine powder images are used for experimental comparison. The added inception-like structure and the improved attention mechanism module increase the accuracy rate by 4.1% to 92.82%. The experiments show that the improvement method proposed in this paper is better than other classification algorithms.

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Section: Introductionmentioning

confidence: 99%