Browsing by Author "Lu, Min"

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    Combining Transformer and CNN for Super-Resolution of Animal Fiber Microscopy Images
    (The Eurographics Association, 2023) Li, Jiagen; Ji, Yatu; Lu, Min; Wang, Li; Dai, Lingjie; Xu, Xuanxuan; Wu, Nier; Liu, Na; Chaine, Raphaƫlle; Deng, Zhigang; Kim, Min H.
    The images of cashmere and wool fibers used for scientific research in the textile field are mostly acquired manually under an optical microscope. However, due to the interference of microscope quality, shooting environment, focal length selection, acquisition techniques and other factors, the quality of the obtained photographs tends to have a low resolution, and it is difficult to display the fine fiber texture structure and scale details. To address the above problems, a lightweight super-resolution reconstruction algorithm with multi-scale hierarchical screening is proposed. Specifically, firstly, a hybrid module incorporating SwinTransformer and enhanced channel attention is proposed to extract the global features and obtain the important localization among them, in addition, a multi-scale hierarchical screening filtering module is proposed based on the residual model, which amplifies the feature information focusing on high-frequency regions by splitting the channels to allow the model to adaptively weight the features according to the feature weights and amplifies the feature information focusing on high-frequency regions. Finally, the global average pooling attention module integrates and weights the high-frequency features again to enhance details such as edges and textures. A large number of experiments show that compared with other state-of-the-art algorithms, the proposed method significantly improves the image quality on the fiber dataset, and at the same time proves the effectiveness of the proposed method at all scales in five public datasets, occupies less memory parameters than SwinIR, and not only improves the PSNR and SSIM, but also reduces the parameters compared with the light-weight ESRT.
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    A Simple Stochastic Regularization Technique for Avoiding Overfitting in Low Resource Image Classification
    (The Eurographics Association, 2023) Ji, Ya Tu; Wang, Bai Lun; Ren, Qing Dao Er Ji; Shi, Bao; Wu, Nier E.; Lu, Min; Liu, Na; Zhuang, Xu Fei; Xu, Xuan Xuan; Wang, Li; Dai, Ling Jie; Yao, Miao Miao; Li, Xiao Mei; Chaine, Raphaƫlle; Deng, Zhigang; Kim, Min H.
    Drop type technique, as a method that can effectively regulate the co-adaptations and prediction ability of neural network units, is widely used in model parameter optimization to reduce overfitting problems. However, low resource image classification faces serious overfitting problems, and the data sparsity problem weakens or even disappears the effectiveness of most regularization methods. This paper is inspired by the value iteration strategy and attempts a Drop type method based on Metcalfe's law, named Metcalfe-Drop. The experimental results indicate that using Metcalfe-Drop technique as a basis to determine parameter sharing is more effective than randomly controlling neurons according to a certain probability. Our code is available at https://gitee.com/giteetu/metcalfe-drop.git.