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Super-resolution Fluorescence Microscopy

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...

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Updated: Jun 25, 2025

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

基于深度学习的单个像素成像增强了单个值分解.

Youquan Deng¹, Rongbin She^1,2, Wenquan Liu^1,2

¹CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.

Sensors (Basel, Switzerland)

|May 25, 2024

概括

此摘要是机器生成的。

我们开发了一种新的单像素成像方法,使用深度学习和单一值分解. 这种技术可以提高图像质量和防噪性能,即使数据有限,也可用于更广泛的应用.

关键词:

深度学习网络是一个深度学习网络.一个像素的成像.单一价值分解分解的方法

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Published on: August 17, 2011

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Last Updated: Jun 25, 2025

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

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Published on: April 28, 2022

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

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科学领域:

计算成像技术的成像
机器学习应用机器学习应用
光学传感传感器是什么?

背景情况:

单像素成像 (SPI) 传统上依赖于预定义的模式,如哈达马德序列.
深度学习方法,如深度卷积自动编码器,在SPI中显示出了希望.
现有的SPI方法在重建质量方面面临挑战,特别是在较低的采样比率下.

研究的目的:

提出并展示一种新的单像素成像方法.
提高SPI系统中的图像重建质量和稳定性.
探索该方法在可见光谱之外的应用中的潜力.

主要方法:

开发一种单像素成像方法,将深度学习与奇数值分解 (SVD) 整合在一起.
深度学习增强SVD (DL-SVD) 方法的理论框架和实验实施.
与传统的哈达马德模式和深度卷积自编码器 (DCAE) 网络方法进行比较.

主要成果:

DL-SVD方法实现了优越的图像重建质量,特别是在较低的采样比率 (降至3.12%) 时.
拟议的方法需要更少的测量或更短的采集时间,以获得可比的图像质量.
证明了增强的抗噪声性能和改善了对未经训练的目标的概括性.

结论:

深度学习增强的SVD方法比传统的SPI技术具有显著的优势.
该方法在具有挑战性的条件下 (低采样,噪音) 提供了强大的,高质量的图像重建.
在单像素成像中广泛应用的潜力,包括非可见光谱范围.