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相关概念视频

Deconvolution01:20

Deconvolution

186
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
186
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

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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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Light Acquisition02:16

Light Acquisition

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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Phase Contrast and Differential Interference Contrast Microscopy01:26

Phase Contrast and Differential Interference Contrast Microscopy

8.2K
Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...
8.2K
X-ray Imaging01:24

X-ray Imaging

5.6K
German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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相关实验视频

Updated: Jul 16, 2025

Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development
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Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development

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深度多曝光图像融合用于动态场景.

Xiao Tan, Huaian Chen, Rui Zhang

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |September 19, 2023
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了第一个动态场景的多曝光聚变 (MEF) 数据集和一个新的深度动态MEF (DDMEF) 框架. DDMEF有效地从动态场景中重建高质量,无幽灵图像,优于现有方法.

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    Simultaneously Capturing Real-time Images in Two Emission Channels Using a Dual Camera Emission Splitting System: Applications to Cell Adhesion
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    相关实验视频

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    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
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    科学领域:

    • 计算机视觉 计算机视觉
    • 图像处理 图像处理
    • 机器学习 机器学习

    背景情况:

    • 基于学习的多曝光融合 (MEF) 方法在静态场景中表现出色,但在动态场景中扎,产生幽灵文物.
    • 现有的MEF方法缺乏动态场景的数据集和解决方案,阻碍了这一常见场景的进展.

    研究的目的:

    • 在动态场景中解决当前MEF方法的局限性.
    • 在动态场景中引入第一个多曝光聚变的基准数据集.
    • 提出一种新的深度学习框架,用于从动态场景中无幽灵的图像重建.

    主要方法:

    • 开发了一种"静态换动态"的策略,以创建具有参考图像的动态场景的多曝光数据集.
    • 提出了一个深度动态MEF (DDMEF) 框架,利用基于增强前的对齐和特权信息引导的融合.
    • 实施了一种特权蒸方案,具有信息注意力传输损失,用于增强除气.

    主要成果:

    • 提出的"静态换动态"策略成功生成了用于动态场景的新型MEF数据集.
    • DDMEF框架有效地从动态场景的两个不同曝光的图像中重建高质量,无幽灵图像.
    • 实验结果表明,在定性和定量评估方面,DDMEF的表现优于最先进的动态MEF方法.

    结论:

    • 为动态场景创建第一个MEF数据集填补了研究中的一个关键缺口.
    • 拟议的DDMEF框架为动态环境中的无幽灵图像重建提供了一个强大的解决方案.
    • 该研究为推进动态多曝光融合研究提供了宝贵的资源 (数据集和代码).