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相关概念视频

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

145
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...
145
Upsampling01:22

Upsampling

161
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...
161
Downsampling01:20

Downsampling

109
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...
109
Deconvolution01:20

Deconvolution

116
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...
116
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

178
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
178
Histogram01:05

Histogram

12.5K
The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
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Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
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连续的高光谱压缩重建的混合细粒度隐式表示.

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    这项研究引入了一种使用隐式神经表示 (INR) 的超光谱图像 (HSI) 重建的新方法. 混合细粒度隐性表示 (MGIR) 框架可以在任何分辨率下进行连续的HSI重建,改进编码光圈快照光谱成像 (CASSI) 系统.

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    科学领域:

    • 光学和光子学 在光学和光子学.
    • 计算机视觉 计算机视觉
    • 信号处理 信号处理

    背景情况:

    • 超光谱图像 (HSI) 是至关重要的,但传统上需要很长的采集时间.
    • 编码孔径快照光谱成像 (CASSI) 加快了HSI获取,但面临着重建的挑战.
    • 现有的方法在HSI重建中扎着固定的空间和光谱分辨率限制.

    研究的目的:

    • 开发一种用于连续超光谱图像重建的新方法.
    • 提高CASSI系统的灵活性和适应性.
    • 从压缩数据重建HSI时克服固定的分辨率约束.

    主要方法:

    • 为HSI重建提出了混合细粒度隐式表示 (MGIR) 框架.
    • 引入了一个层次的光谱空间隐性编码器 (HSSIE) 用于多级特征提取.
    • 使用混合细粒度局部特征聚合器 (MGLFA) 和坐标感知解码器进行精确的重建.

    主要成果:

    • 在MGIR框架允许HSI重建在任意空间和光谱分辨率.
    • 拟议的方法在各种压缩比率上实现了最先进的性能.
    • 实验评估验证了模型在连续HSI重建中的有效性.

    结论:

    • 隐式神经表示 (INR) 为灵活的HSI重建提供了一个强大的方法.
    • MGIR框架显著提升了CASSI系统的能力.
    • 这项工作提供了一个可扩展的解决方案,用于从压缩测量的高分辨率HSI重建.