Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Sep 3, 2025

Quantifying Intermembrane Distances with Serial Image Dilations

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

Multi-Image Encryption Method via Computational Integral Imaging Algorithm.

Xiaowu Li¹, Chuying Yu², Junfeng Guo³

¹The Second Affiliated Hospital of Shantou University Medical College, Shantou 515000, China.

Entropy (Basel, Switzerland)

|July 27, 2022

Summary

This summary is machine-generated.

Related Concept Videos

Imaging Studies III: Computed Tomography

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

Convolution: Math, Graphics, and Discrete Signals

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...

You might also read

Related Articles

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

Sort by

Same author

Multi-Stage Attentive Network for Motion Deblurring via Binary Cross-Entropy Loss.

Entropy (Basel, Switzerland)·2023

Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026

Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026

Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026

Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026

Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026

Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026

See all related articles

This study introduces a novel multi-image encryption method using computational integral imaging and a DNA-chaos algorithm. The technique merges multiple images for efficient, secure encryption, enhancing data protection.

Area of Science:

Computer Science
Cryptography
Image Processing

Background:

Traditional image encryption methods face challenges in efficiency and security.
Multi-image encryption requires robust techniques to manage data complexity.

Purpose of the Study:

To propose an efficient and secure multi-image encryption scheme.
To leverage computational integral imaging and DNA-chaos for enhanced security.

Main Methods:

A multi-image encryption scheme integrating computational integral imaging and a DNA-chaos algorithm.
Merging multiple images into a single image using computational integral imaging.
Utilizing depth distances as encryption keys.
Incorporating chaos algorithm for randomness to mitigate DNA encryption's outline effect.

Keywords:

DNA-chaos algorithm computational integral imaging multi-image encryption

More Related Videos

Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

Published on: December 9, 2013

TACI: An ImageJ Plugin for 3D Calcium Imaging Analysis

TACI: An ImageJ Plugin for 3D Calcium Imaging Analysis

Published on: December 16, 2022

Related Experiment Videos

Last Updated: Sep 3, 2025

Quantifying Intermembrane Distances with Serial Image Dilations

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

Published on: December 9, 2013

TACI: An ImageJ Plugin for 3D Calcium Imaging Analysis

TACI: An ImageJ Plugin for 3D Calcium Imaging Analysis

Published on: December 16, 2022

Main Results:

Achieved high encryption efficiency by merging multiple images.
Demonstrated enhanced security by using depth distances as keys.
The encrypted image entropy value reached 7.6227, significantly reducing image relevance.
Verified high key security and resistance to various attacks through simulations.

Conclusions:

The proposed scheme offers an efficient and secure solution for multi-image encryption.
The integration of computational integral imaging and DNA-chaos significantly boosts encryption security.
The method effectively addresses outline effects and enhances data protection against attacks.