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通过波形金字塔递归神经网络分析超分辨率的医学图像,波形能量局限于波形能量

Yue Yu1, Kun She2, Kaibo Shi3

  • 1School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu, 610106, Sichuan, China; School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan, China.

Neural networks : the official journal of the International Neural Network Society
|June 21, 2024
PubMed
概括

一个新的波形金字塔递归神经网络 (WPRNN) 通过传输多层波形系数和使用波形能量 (WEE) 来实现信号能量约束,提高图像超分辨率,以更少的参数实现竞争性性能.

关键词:
医学图像 医学图像 医学图像多个分辨率分析分析.超级分辨率的超级分辨率波段能量的波段能量.波形金字塔网络的波形金字塔网络.

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Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been developed.