Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

319
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...
319
Aliasing01:18

Aliasing

210
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...
210
Upsampling01:22

Upsampling

303
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...
303

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Hybrid zeolitic imidazolate frameworks with catalytically active TO4 building blocks.

Angewandte Chemie (International ed. in English)·2010
Same author

Whiter matter abnormalities in medication-naive subjects with a single short-duration episode of major depressive disorder.

Psychiatry research·2010
Same author

A new comorbidity index: the health-related quality of life comorbidity index.

Journal of clinical epidemiology·2010
Same author

S-adenosylmethionine inhibits the growth of cancer cells by reversing the hypomethylation status of c-myc and H-ras in human gastric cancer and colon cancer.

International journal of biological sciences·2010
Same author

Nano-sized SnSbAgx alloy anodes prepared by reductive co-precipitation method used as lithium-ion battery materials.

Journal of nanoscience and nanotechnology·2010
Same author

Complementary diffusion tensor imaging study of the corpus callosum in patients with first-episode and chronic schizophrenia.

Journal of psychiatry & neuroscience : JPN·2010

Related Experiment Video

Updated: Sep 4, 2025

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.5K

Adaptive compressed sensing algorithm for terahertz spectral image reconstruction based on residual learning.

Yuying Jiang1, Guangming Li2, Hongyi Ge2

  • 1Key Laboratory of Grain Informatcon Processing and Control (Henan University of Technology), Ministry of Education, Zhengzhou 450001, China; School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, China; Henan Provincial Key Laboratory of Grain Photoelectric Detection and Control, Zhengzhou, 450001, China.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|July 19, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive compressed sensing reconstruction algorithm (ATResCS) for terahertz spectral imaging. ATResCS accelerates grain detection by reducing data requirements and processing time, enabling faster, high-quality imaging.

Keywords:
Compressed sensingResidual learningTHz spectral imageTerahertz spectrum

More Related Videos

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

606
Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

17.8K

Related Experiment Videos

Last Updated: Sep 4, 2025

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.5K
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

606
Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

17.8K

Area of Science:

  • Non-destructive testing
  • Terahertz spectroscopy
  • Image processing

Background:

  • Terahertz time-domain spectroscopy (THz-TDS) is valuable for rapid, non-destructive grain detection due to its properties.
  • Current THz imaging faces challenges with long acquisition times and large data volumes.
  • Efficient reconstruction algorithms are needed to overcome these limitations.

Purpose of the Study:

  • To develop an adaptive compressed sensing reconstruction algorithm for THz spectral images.
  • To improve imaging speed and reduce data processing requirements in THz imaging.
  • To enable real-time, high-quality reconstruction of THz spectral images.

Main Methods:

  • An adaptive compressed sensing reconstruction algorithm based on residual learning (ATResCS) was developed.
  • The algorithm utilizes a convolutional neural network to compress data samples and reduce time complexity.
  • The ATResCS algorithm was validated using THz-TDS system data.

Main Results:

  • ATResCS significantly reduces data requirements and improves imaging speed.
  • The algorithm demonstrates superior performance in peak signal-to-noise ratio (PSNR) and structural similarity compared to conventional methods.
  • At low sampling rates (0.1), ATResCS effectively retains spectral image information, outperforming DR2-Net by 0.96-1.015 dB in PSNR.

Conclusions:

  • ATResCS enables high-quality and fast reconstruction of terahertz spectral images.
  • The algorithm exhibits enhanced reconfiguration capabilities and lower computational complexity.
  • This advancement facilitates real-time reconstruction, addressing key limitations in THz imaging systems.