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相关概念视频

Downsampling01:20

Downsampling

121
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...
121
Upsampling01:22

Upsampling

188
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...
188
Aliasing01:18

Aliasing

107
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
107
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

157
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...
157
Fast Fourier Transform01:10

Fast Fourier Transform

252
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
252
¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

993
The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...
993

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Updated: May 24, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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利用频率分析来进行图像拒绝网络修剪.

Dongdong Ren, Wenbin Li, Jing Huo

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |March 3, 2025
    PubMed
    概括
    此摘要是机器生成的。

    网络修剪,一种模型压缩技术,对图像无声化无效. 一种名为高频组件修剪 (HFCP) 的新方法,通过专注于高频组件以提高性能,专门针对网络无噪声.

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    科学领域:

    • 计算机视觉 计算机视觉
    • 深度学习 (Deep Learning) 是一种深度学习.
    • 图像处理 图像处理

    背景情况:

    • 网络修剪是一种用于模型压缩的常见技术,可以降低存储和计算成本.
    • 现有的修剪方法主要设计用于高水平视觉任务,不适合用于像图像消噪等低水平任务.
    • 基于标准的裁剪标准在图像无色化方面失败,原因是特征细分度和网络目标不同.

    研究的目的:

    • 开发一种新的修剪方法,专门用于图像破坏网络.
    • 在低视力任务中解决现有修剪技术的局限性.
    • 为了提高修剪图像无色化模型的性能和可解释性.

    主要方法:

    • 提出了一种新的过器评估方法:高频组件修剪 (HFCP).
    • 通过分析网络中的高频组件,HFCP评估过器的重要性.
    • 在四个主流的图像拒绝网络中验证了HFCP.

    主要成果:

    • HFCP是第一个专门为图像无噪设计的修剪方法.
    • 该方法很简单,适用于各种噪音类型,并增强高频信息内容.
    • 通过HFCP,修剪后的模型能够以可靠和可解释的方式将信号与噪声区分开来.

    结论:

    • 高频组件修剪 (HFCP) 是一种有效和专业的技术,用于图像无效的网络压缩.
    • 在低水平视觉任务中,HFCP克服了传统修剪方法的局限性.
    • 这种新的方法为无声化模型提供了更好的性能和可解释性.