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This article is cited in 1 scientific paper (total in 1 paper)
IMAGE PROCESSING, PATTERN RECOGNITION
Super-resolution microscopy based on wide spectrum denoising and compressed sensing
T. Cheng, H. Jin Guangxi University of Science and Technology, 545006, Liuzhou, P. R. China, Chengzhong District, Avenue Donghuan 268
Abstract:
WSD can effectively remove random noise of a raw image from very low density to ultra-high density fluorescent molecular distribution scenarios. The size of the raw image that WSD can denoise is subject to the used measurement matrix. A large raw image must be divided into blocks so that WSD denoises each block separately. Based on traditional single-molecule localization and super-resolution reconstruction scenarios, wide spectrum denoising (WSD) for blocks of different sizes was studied. The denoising ability is related to block sizes. The general trend is when the block gets larger, the denoising effect gets worse. When the block size is equal to 10, the denoising effect is the best. Using compressed sensing, only 20 raw images are needed for reconstruction. The temporal resolution is less than half a second. The spatial resolution is also greatly improved.
Keywords:
fluorescence microscopy, super-resolution, noise, diffraction theory, compressed sensing
Received: 06.06.2022 Accepted: 01.12.2022
Citation:
T. Cheng, H. Jin, “Super-resolution microscopy based on wide spectrum denoising and compressed sensing”, Computer Optics, 47:3 (2023), 426–432
Linking options:
https://www.mathnet.ru/eng/co1142 https://www.mathnet.ru/eng/co/v47/i3/p426
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