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相关概念视频

Upsampling01:22

Upsampling

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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X-ray Imaging01:24

X-ray Imaging

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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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Deconvolution01:20

Deconvolution

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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...
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Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Downsampling01:20

Downsampling

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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
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Convolution Properties I01:20

Convolution Properties I

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Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
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Measuring Spatially- and Directionally-varying Light Scattering from Biological Material
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打破界限:统一成像和压缩,实现HDR图像压缩

Xuelin Shen, Linfeng Pan, Zhangkai Ni

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |March 3, 2025
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    概括

    本研究引入了一种用于高动态范围 (HDR) 图像压缩的新方法,统一成像和压缩 (HDR-UIC) 以提高质量. HDR-UIC方法提高了压缩性能,而不会牺牲感知质量.

    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:

    • 高动态范围 (HDR) 图像压缩面临挑战,因为与低动态范围 (LDR) 图像相比,数据分布复杂.
    • 现有的HDR压缩方法经常使用预处理步骤,降低感知质量.

    研究的目的:

    • 提出一个新的高动态范围 (HDR) 图像压缩范式,统一成像和压缩 (HDR-UIC).
    • 为了实现从图像捕获到传输的端到端培训和优化,克服当前HDR压缩技术的局限性.

    主要方法:

    • 开发了一个基于混合注意力 (MAT) 的骨干,用于合并LDR功能并生成紧的表示.
    • 引入了一个参考引导的错位感知功能增强 (RME) 模块,以减少幽灵文物.
    • 实现了一个外观冗余删除 (ARR) 模块,以优化编码资源分配.

    主要成果:

    • 拟议的HDR-UIC方法显著提高了压缩性能.
    • 与现有的最先进的HDR压缩方案相比,证明了更好的效果.
    • 保持高的感知质量,没有额外的信息损失.

    结论:

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  • 对于具有挑战性的HDR图像压缩任务,HDR-UIC范式提供了一个有效的解决方案.
  • 图像和压缩过程的无集成可以提高性能.
  • 这些新型模块有效地解决了HDR压缩中的错位和冗余问题.