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相关概念视频

Vision01:24

Vision

53.1K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
53.1K
Visual System01:26

Visual System

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Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
566
Channel Rhodopsins01:11

Channel Rhodopsins

2.5K
Most organisms use photoreceptors to sense and respond to light. Examples of photoreceptors include bacteriorhodopsins and bacteriophytochromes in some bacteria, phytochromes in plants, and rhodopsins in the photoreceptor cells of the vertebral retina. The light-sensitive property of these receptors is because of the bound chromophores, such as bilin in the phytochromes and retinal in the rhodopsins.
Rhodopsins belong to the family of cell surface proteins called G-protein coupled receptors,...
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相关实验视频

Updated: Jun 22, 2025

Lensless Fluorescent Microscopy on a Chip
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Lensless Fluorescent Microscopy on a Chip

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学习内核调制的神经表示,以实现高效的光场压缩.

Jinglei Shi, Yihong Xu, Christine Guillemot

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |July 1, 2024
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    概括
    此摘要是机器生成的。

    这项研究引入了用于光场压缩的紧神经网络,显著超过现有方法. 该技术有效地重建3D场景数据,实现高质量的光场染和视图合成.

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    相关实验视频

    Last Updated: Jun 22, 2025

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    Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture
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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:

    • 光场捕获3D场景信息,提供沉浸式感知,但产生大量数据.
    • 有效的压缩方法对于实际的光场应用至关重要.

    研究的目的:

    • 为有效的光场压缩设计一个紧的神经网络表示.
    • 为了实现高质量的光场重建,同时最大限度地减少数据大小.

    主要方法:

    • 一个使用描述和调制内核的新型神经网络架构.
    • 包括调节器分配,内核张量分解,非均量子化和编码在内的技术.
    • 使用随机初始化的噪声作为输入来重建目标亚光圈图像 (SAI) 的监督训练.

    主要成果:

    • 拟议的方法在光场压缩方面明显优于最先进的 (SOTA) 方法.
    • 实现了高质量的解码光场,增强了紧性.
    • 证明了学习模块的成功转移学习,用于视图合成.

    结论:

    • 紧的神经网络表示对于光场压缩非常有效.
    • 该方法为高效的3D场景数据处理和视图合成提供了一个有前途的解决方案.
    • 适应新光场的学习组件的潜力,以产生新的视图.