基于广谱去噪和压缩感知的超分辨率显微技术

IF 1.1 Q4 OPTICS
T. Cheng, H. Jin
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引用次数: 0

摘要

WSD可以有效去除极低密度到超高密度荧光分子分布场景下原始图像的随机噪声。WSD能够去噪的原始图像的大小取决于所使用的测量矩阵。一幅大的原始图像必须分成若干块,以便水务署分别对每个块进行去噪。基于传统的单分子定位和超分辨率重建场景,研究了不同大小块的宽谱去噪方法。去噪能力与块大小有关。总体趋势是块越大,去噪效果越差。当块大小为10时,去噪效果最好。使用压缩感知,只需要20张原始图像进行重建。时间分辨率小于半秒。空间分辨率也大大提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Super-resolution microscopy based on wide spectrum denoising and compressed sensing
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.
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来源期刊
Computer Optics
Computer Optics OPTICS-
CiteScore
4.20
自引率
10.00%
发文量
73
审稿时长
9 weeks
期刊介绍: The journal is intended for researchers and specialists active in the following research areas: Diffractive Optics; Information Optical Technology; Nanophotonics and Optics of Nanostructures; Image Analysis & Understanding; Information Coding & Security; Earth Remote Sensing Technologies; Hyperspectral Data Analysis; Numerical Methods for Optics and Image Processing; Intelligent Video Analysis. The journal "Computer Optics" has been published since 1987. Published 6 issues per year.
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