计算机科学
人工智能
计算机视觉
显微镜
过程(计算)
深度学习
光学(聚焦)
网格
图像质量
图像处理
图像(数学)
光学
物理
数学
几何学
操作系统
作者
Shu Wang,Xiaoxiang Liu,Yueying Li,Xinquan Sun,Qi Li,Yinhua She,Yixuan Xu,Xingxin Huang,Ruolan Lin,Deyong Kang,Xingfu Wang,Haohua Tu,Wenxi Liu,Feng Huang,Jianxin Chen
标识
DOI:10.1038/s41467-023-41165-1
摘要
Stitched fluorescence microscope images inevitably exist in various types of stripes or artifacts caused by uncertain factors such as optical devices or specimens, which severely affects the image quality and downstream quantitative analysis. Here, we present a deep learning-based Stripe Self-Correction method, so-called SSCOR. Specifically, we propose a proximity sampling scheme and adversarial reciprocal self-training paradigm that enable SSCOR to utilize stripe-free patches sampled from the stitched microscope image itself to correct their adjacent stripe patches. Comparing to off-the-shelf approaches, SSCOR can not only adaptively correct non-uniform, oblique, and grid stripes, but also remove scanning, bubble, and out-of-focus artifacts, achieving the state-of-the-art performance across different imaging conditions and modalities. Moreover, SSCOR does not require any physical parameter estimation, patch-wise manual annotation, or raw stitched information in the correction process. This provides an intelligent prior-free image restoration solution for microscopists or even microscope companies, thus ensuring more precise biomedical applications for researchers.
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