Denoising Low-dose Images Using Deep Learning of Time Series Images
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929298324062208 |
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| author | Shao, Yang Yaguchi, Toshie Tanigaki, Toshiaki |
| author_facet | Shao, Yang Yaguchi, Toshie Tanigaki, Toshiaki |
| contents | Digital image devices have been widely applied in many fields, including scientific imaging, recognition of individuals, and remote sensing. As the application of these imaging technologies to autonomous driving and measurement, image noise generated when observation cannot be performed with a sufficient dose has become a major problem. Machine learning denoise technology is expected to be the solver of this problem, but there are the following problems. Here we report, artifacts generated by machine learning denoise in ultra-low dose observation using an in-situ observation video of an electron microscope as an example. And as a method to solve this problem, we propose a method to decompose a time series image into a 2D image of the spatial axis and time to perform machine learning denoise. Our method opens new avenues accurate and stable reconstruction of continuous high-resolution images from low-dose imaging in science, industry, and life. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_00510 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Denoising Low-dose Images Using Deep Learning of Time Series Images Shao, Yang Yaguchi, Toshie Tanigaki, Toshiaki Computer Vision and Pattern Recognition Image and Video Processing Digital image devices have been widely applied in many fields, including scientific imaging, recognition of individuals, and remote sensing. As the application of these imaging technologies to autonomous driving and measurement, image noise generated when observation cannot be performed with a sufficient dose has become a major problem. Machine learning denoise technology is expected to be the solver of this problem, but there are the following problems. Here we report, artifacts generated by machine learning denoise in ultra-low dose observation using an in-situ observation video of an electron microscope as an example. And as a method to solve this problem, we propose a method to decompose a time series image into a 2D image of the spatial axis and time to perform machine learning denoise. Our method opens new avenues accurate and stable reconstruction of continuous high-resolution images from low-dose imaging in science, industry, and life. |
| title | Denoising Low-dose Images Using Deep Learning of Time Series Images |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2404.00510 |