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

194
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...
194
Aliasing01:18

Aliasing

133
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...
133
Upsampling01:22

Upsampling

232
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...
232
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

2.4K
Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
2.4K
Computed Tomography01:10

Computed Tomography

4.5K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
4.5K

You might also read

Related Articles

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

Sort by
Same author

3D-TCM-Driven Bit-Level Image Encryption via S-Box Feedback Algorithm.

Entropy (Basel, Switzerland)·2026
Same author

Image Privacy Protection Communication Scheme by Fibonacci Interleaved Diffusion and Non-Degenerate Discrete Chaos.

Entropy (Basel, Switzerland)·2025
Same author

Secure image communication based on two-layer dynamic feedback encryption and DWT information hiding.

PloS one·2024
Same author

Author Correction: Exploiting high‑quality reconstruction image encryption strategy by optimized orthogonal compressive sensing.

Scientific reports·2024
Same author

Dynamic feedback bit-level image privacy protection based on chaos and information hiding.

Scientific reports·2024
Same author

Chaos-based block permutation and dynamic sequence multiplexing for video encryption.

Scientific reports·2023
Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026
Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026
Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026
Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026
Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026
Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Jun 28, 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

Exploiting high-quality reconstruction image encryption strategy by optimized orthogonal compressive sensing.

Heping Wen1,2, Lincheng Yang1, Chixin Bai1

  • 1Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan, 528402, China.

Scientific Reports
|April 16, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a secure compressive sensing image encryption method using an optimized orthogonal measurement matrix. It enhances image security and reconstruction quality, addressing limitations in current techniques for big data privacy.

Keywords:
Compressive sensingFrequency domain compressionImage encryptionOptimized orthogonal

More Related Videos

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

17.7K
Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases
09:55

Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases

Published on: January 5, 2024

1.2K

Related Experiment Videos

Last Updated: Jun 28, 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
Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

17.7K
Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases
09:55

Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases

Published on: January 5, 2024

1.2K

Area of Science:

  • Information Security
  • Signal Processing
  • Applied Mathematics

Background:

  • Compressive sensing (CS) offers advantages over traditional sampling but faces challenges in joint compression encryption security and image reconstruction quality.
  • Existing methods struggle with robust security and high-fidelity restoration, necessitating improved techniques for sensitive data handling.

Purpose of the Study:

  • To develop a novel compressive sensing image encryption scheme that enhances security and improves the quality of reconstructed images.
  • To address the limitations of existing joint compression encryption methods in terms of security defects and restoration fidelity.

Main Methods:

  • An optimized orthogonal measurement matrix was constructed using DWT, OMP, and a chaotic system, combined with Part Hadamard matrix and Kronecker product.
  • Image sparse representation via DWT and pixel scrambling using Arnold transformation were employed.
  • Bit-level diffusion and permutation using a 2D-LSCM chaotic sequence generated the final ciphertext.

Main Results:

  • The proposed scheme demonstrated strong cryptographic properties, including obfuscation, diffusion, and avalanche effects, with a large key space resistant to brute-force attacks.
  • Experimental results confirmed superior image restoration quality compared to similar compressive sensing algorithms.
  • The method effectively balances high security with excellent decryption reconstruction quality.

Conclusions:

  • The developed compressive sensing image encryption scheme significantly enhances both security and reconstruction quality.
  • This approach offers promising solutions for privacy protection in network big data applications.
  • The optimized orthogonal measurement matrix and chaotic encryption strategy provide a robust framework for secure image transmission.