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Accelerating biological imaging of atomic force microscopy by deep-learning

文献类型： 外文期刊

作者： Yu, Haiyue 1 ; Li, Nan 4 ; Yao, Feifan 6 ; Wang, Baichuan 1 ; Song, Hangze 1 ; Wang, Zuobin 1 ;

作者机构： 1.Changchun Univ Sci & Technol, Changchun 130022, Peoples R China

2.Changchun Univ Sci & Technol, Int Res Ctr Nano Handling & Mfg China, Changchun 130022, Peoples R China

3.Changchun Univ Sci & Technol, Zhongshan Inst, Ctr Opto Bionano Measurement & Mfg, Zhongshan 528400, Peoples R China

4.Chinese Acad Agr Sci, Changchun Vet Res Inst, Changchun 130122, Peoples R China

5.Changchun Univ Sci & Technol, Minist Educ, Key Lab Cross Scale Micro & Nano Mfg, Changchun 130022, Peoples R China

6.Jilin Inst Chem Technol, Jilin 132022, Peoples R China

关键词： Atomic force microscopy; Deep-learning; Virus; Particles characterization; Super-resolution reconstruction

期刊名称：BIOMEDICAL SIGNAL PROCESSING AND CONTROL （ 影响因子：4.9； 五年影响因子：5.0 ）

ISSN： 1746-8094

年卷期： 2026 年 111 卷

页码：

收录情况： SCI

摘要： Atomic Force Microscopy is widely used for surface imaging at the nanoscale. However, we need slower scanning speeds to obtain high-quality images, which often introduce noise and sample drift, causing distortion. In addition, the large-scale scanning of high-resolution images by AFM consumes a lot of sample resources and time. Although the traditional bicubic method can achieve the generation of high-resolution images, it cannot restore the details of the image well, and even the obtained image is basically different from the original high-resolution image. Here, in this paper, we propose a fast imaging method that combines the scanning data acquisition and the image super-resolution algorithm of the deep learning model, which can quickly generate high-quality images with little noise interference and small drift. High-resolution scans of viral samples in a clean-room environment were combined with higher-order degradation modeling to generate low-and high-resolution paired images. To address the time and cost limitations of HR scanning, we optimize the Real-Enhanced Super-Resolution Generative Network by incorporating a lightweight Dynamic Local and Global Self-Attention Network. In addition, we replace L1 Loss with Smooth L1 Loss to accelerate the convergence and improve the performance. Validation using novel coronavirus and influenza virus samples shows that our approach reduces imaging time by approximately 20-fold while maintaining high-resolution quality. The resulting super-resolution images preserve fine viral morphology with improved clarity and accuracy. It provides help for the classification and image detection of various viruses in the later research.

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