A quantum reversible color-to-grayscale conversion scheme via image encryption based on true random numbers and two-dimensional quantum walks

IF 3.4 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Signal Processing Pub Date : 2025-02-19 DOI:10.1016/j.sigpro.2025.109949
Nianqiao Li, Zhenjun Tang
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引用次数: 0

Abstract

Reversible Color-to-Grayscale Conversion (RCGC) is a method for converting color images to grayscale while retaining sufficient information to reconstruct the original color image when needed. This study proposes a Quantum RCGC (QRCGC) scheme that integrates quantum encryption techniques. The scheme first uses a finite number of true random numbers as seeds, which are then extended using a two-dimensional quantum walks system to generate sufficiently large random matrices for performing bitwise XOR operations with the original image. Subsequently, a quantum confusion technique is proposed, combining quantum block Arnold scrambling, cyclic shifts, and subsequence exchanges, which enhances the complexity of the relationship between the keys and the ciphertext in parallel. Additionally, a quantum diffusion technique is designed, efficiently generating hash values via a two-dimensional quantum walks system to verify image integrity. These hash values are used as content-based key inputs in a chaotic system to generate quantum secure matrices for diffusing the image information. Finally, a quantum bidirectional conversion operation is designed to achieve lossless reversible conversion between color and grayscale images. Experimental results show that the QRCGC scheme demonstrates significant advantages in terms of security, efficiency, and information retention.
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来源期刊
Signal Processing
Signal Processing 工程技术-工程:电子与电气
CiteScore
9.20
自引率
9.10%
发文量
309
审稿时长
41 days
期刊介绍: Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work, tutorial and review articles, and accounts of practical developments. It is intended for a rapid dissemination of knowledge and experience to engineers and scientists working in the research, development or practical application of signal processing. Subject areas covered by the journal include: Signal Theory; Stochastic Processes; Detection and Estimation; Spectral Analysis; Filtering; Signal Processing Systems; Software Developments; Image Processing; Pattern Recognition; Optical Signal Processing; Digital Signal Processing; Multi-dimensional Signal Processing; Communication Signal Processing; Biomedical Signal Processing; Geophysical and Astrophysical Signal Processing; Earth Resources Signal Processing; Acoustic and Vibration Signal Processing; Data Processing; Remote Sensing; Signal Processing Technology; Radar Signal Processing; Sonar Signal Processing; Industrial Applications; New Applications.
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