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Upsampling

Upsampling

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...

Downsampling

Downsampling

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...

Aliasing

Aliasing

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 signal...

Bandpass Sampling

Bandpass Sampling

In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2. The spectrum...

Sampling Theorem

Sampling Theorem

In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.

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Related Experiment Video

Updated: Jun 11, 2026

Blood Flow Imaging with Ultrafast Doppler

Blood Flow Imaging with Ultrafast Doppler

Published on: October 14, 2020

Low-sampling-rate compressed ghost edge imaging via energy-descending ordered speckle patterns.

Yuqiao Zeng, Chengyuan Meng, Sicheng Gu

Journal of the Optical Society of America. A, Optics, Image Science, and Vision

|June 10, 2026

Summary

This summary is machine-generated.

Computational ghost imaging (CGI) for edge detection struggles with a trade-off between sampling cost and edge quality. Our new method, energy-descending ordered compressed ghost edge imaging (EDO-CGEI), improves efficiency and clarity, especially at low sampling rates.

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Live Cell Imaging of F-actin Dynamics via Fluorescent Speckle Microscopy (FSM)

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Published on: August 5, 2009

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Last Updated: Jun 11, 2026

Blood Flow Imaging with Ultrafast Doppler

Blood Flow Imaging with Ultrafast Doppler

Published on: October 14, 2020

Live Cell Imaging of F-actin Dynamics via Fluorescent Speckle Microscopy (FSM)

Live Cell Imaging of F-actin Dynamics via Fluorescent Speckle Microscopy (FSM)

Published on: August 5, 2009

Area of Science:

Computational imaging
Optical sensing
Image processing

Background:

Computational ghost imaging (CGI) is vital for edge detection.
Speckle-shifting ghost imaging (SSGI) faces challenges balancing sampling cost and edge quality.
Existing methods present a significant trade-off between efficiency and image clarity.

Purpose of the Study:

To introduce an adaptive edge detection method for ghost imaging.
To improve the trade-off between sampling cost and edge quality in CGI.
To enhance the efficiency and clarity of edge detection under resource constraints.

Main Methods:

Developed energy-descending ordered compressed ghost edge imaging (EDO-CGEI).
Reordered binary illumination patterns in descending order of energy.
Validated through simulations and experimental implementations.

Main Results:

EDO-CGEI significantly outperforms existing schemes at low sampling rates.
Achieved a 256x256 edge image with a sampling rate as low as 15%.
Demonstrated a superior balance between efficiency and edge clarity.

Conclusions:

EDO-CGEI effectively addresses the sampling cost versus edge quality trade-off in CGI.
The proposed method pushes the boundaries of ghost imaging for resource-constrained edge detection.
EDO-CGEI offers a promising solution for efficient and high-clarity ghost imaging edge detection.