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相关概念视频

Upsampling01:22

Upsampling

265
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...
265
Downsampling01:20

Downsampling

188
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...
188
Scaling01:26

Scaling

278
In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...
278
Basic Operations on Signals01:22

Basic Operations on Signals

419
Basic signal operations include time reversal, time scaling, time shifting, and amplitude transformations. These operations are fundamental in signal processing and analysis.
Time Reversal mirrors a continuous-time signal about the vertical axis at t=0. This is achieved by substituting t with −t. For example, if a signal x(t) is considered, the time-reversed signal is x(−t). This operation can be graphically represented, showing the mirrored signal.
419
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

241
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
241
Inverse z-Transform by Partial Fraction Expansion01:20

Inverse z-Transform by Partial Fraction Expansion

370
The inverse z-transform is a crucial technique for converting a function from its z-domain representation back to the time domain. One effective method for finding the inverse z-transform is the Partial Fraction Method, which involves decomposing a function into simpler fractions with distinct coefficients. These fractions correspond to known z-transform pairs, facilitating the inverse transformation process.
To begin the process, the poles of the function are identified and the function is...
370

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相关实验视频

Updated: Jul 22, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

442

规模-任意的可逆图像缩小缩小缩小.

Jinbo Xing, Wenbo Hu, Menghan Xia

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |July 24, 2023
    PubMed
    概括
    此摘要是机器生成的。

    我们开发了一个规模任意的可逆图像缩放网络 (AIDN),以实现用于社交媒体的高分辨率图像缩放. 这个网络允许任意的尺度因子,保存细节以供以后从低分辨率图像中恢复.

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    相关实验视频

    Last Updated: Jul 22, 2025

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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    Published on: July 5, 2024

    442
    Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
    10:16

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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:

    • 社交媒体平台经常缩小高分辨率 (HR) 图像,失去视觉细节.
    • 现有的可逆图像缩放方法仅限于固定的整数缩放因子.

    研究的目的:

    • 提出一个有效和通用的规模任意可逆图像缩放网络 (AIDN).
    • 为了使可逆图像缩放随意缩放因子,适合社交媒体分辨率限制.

    主要方法:

    • 开发了规模任意的可逆图像缩放网络 (AIDN).
    • 引入了条件重抽模块 (CRM),通过对尺寸和图像内容进行调节来处理任意的尺度因子.
    • 嵌入人力资源信息无形地嵌入缩小LR图像中进行恢复.

    主要成果:

    • 通过任意的整数和非整数缩放因子,AIDN实现了可逆缩放的最高性能.
    • 该网络展示了对损耗图像压缩的稳定性.
    • 只有从缩小的LR图像中才能恢复HR图像.

    结论:

    • AIDN提供了一个通用解决方案,用于任意尺度的可逆图像缩放.
    • 该方法有效地保留了社交媒体应用程序的视觉细节.
    • 即使在图像压缩后,AIDN也保持了性能,增强了实际实用性.