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相关概念视频

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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Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution
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深度学习增强的超分辨率成像使用低成本的单光子雪崩二极管.

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    此摘要是机器生成的。

    本研究介绍了一种紧的深度学习模型,用于使用低成本的SPAD阵列进行超分辨率 (SR) 深度成像. 该模型实现了高分辨率的重建,适用于边缘计算和嵌入式应用程序.

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    科学领域:

    • 计算机视觉 计算机视觉
    • 光子学 是一个光子学.
    • 人工智能的人工智能

    背景情况:

    • 低成本的单光子雪崩二极管 (SPAD) 阵列为深度传感提供了潜力.
    • 现有的超分辨率 (SR) 方法通常需要复杂的融合或大量的计算资源.
    • 提高消费级SPAD阵列的性能对于更广泛的采用至关重要.

    研究的目的:

    • 为低成本的SPAD阵列开发一种非融合深度学习 (DL) 基于超分辨率 (SR) 的解决方案.
    • 从低分辨率 (LR) 输入中重建高分辨率 (HR) 深度和强度图像.
    • 优化DL模型以实现高效的硬件部署和实时应用程序.

    主要方法:

    • 一个紧的深度学习 (DL) 模型被设计用于处理低分辨率 (LR,8x8) 深度和强度数据.
    • 该模型同时重建高分辨率 (HR,50x50) 图像.
    • 通过INT8量子化进行模型压缩,以方便硬件部署.
    • 对合成数据集和来自STMicroelectronics VL53L8CX SPAD阵列的真实测量进行了评估.

    主要成果:

    • DL模型在合成数据集上的地面真实图像上表现出高保真性.
    • 从实际的SPAD阵列测量结果的重建中获得了精确的结构细节.
    • INT8量子化只导致了边际的准确性损失,同时允许硬件部署.
    • 原始和量子化模型都在中等级别的GPU上实现了视频速率SR重建.

    结论:

    • 拟议的紧型DL模型有效地提高了用于超分辨率深度成像的低成本SPAD阵列的性能.
    • 该模型的效率和准确性使其适合实时应用.
    • 它的紧尺寸和硬件部署潜力使其适用于边缘计算,移动和嵌入式系统.