Sparse point spread function-based multi-image optical encryption

Multi-image optical encryption (MOE) has demonstrated promising potential in image data protection owing to its parallel processing capability and abundant degrees of freedom. However, existing methods suffer from either low compression ratios or stringent experimental conditions, such as accurate calibration of phase modulation, precise manufacturing of encryption elements, and no ambient light interference. This work introduces a lensless sparse point spread function-based multi-image optical encryption (sPSF-MOE) technique that addresses these challenges and enhances performance. In the encryption process, each plaintext image is encoded using a sparsely distributed PSF with specifically designed geometric shapes through spatial phase engineering. The resulting ciphertexts are superimposed to produce a compressed ciphertext. During decryption, an iterative algorithm recovers encrypted images with improved reconstruction quality. We show that sPSF-MOE ensures high fidelity for binary (gray-scale) images at a compression ratio of 12 (6) and resists autocorrelation-based attacks. Integrating principal component analysis (PCA) into decryption preserves image high fidelity under ambient light interference. sPSF-MOE reduces the bandwidth requirement for data transmission while ensuring data integrity. Optical encryption is used to protect image data but often struggles with low compression or strict experimental setups. The authors introduce a lensless method that securely encrypts multiple images with high compression and robustness, using smart point spread patterns and compressed sensing algorithms.

Authors

Institutions

Publication Details

Journal
Communications Physics
Published
2025-04-29
DOI
https://doi.org/10.1038/s42005-025-02105-1
Citations
5
Primary Topic
Chaos-based Image/Signal Encryption
Type
article
Field-Weighted Citation Impact
6.38

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Sparse point spread function-based multi-image optical encryption

Dalong Qi, Yunhua Yao, Yuecheng Shen, Shian Zhang et al.
5 citations
Communications Physics
Chaos-based Image/Signal Encryption
6.38
article

Sparse point spread function-based multi-image optical encryption

Dalong Qi, Yunhua Yao, Yuecheng Shen, Shian Zhang, Lianzhong Deng, Cheng Zhou, Zhenrong Sun, Hongmei Ma, Zhen Pan, Ning Xu, Long Cheng, Wenzhang Lin
article en
5 citations

Abstract

Multi-image optical encryption (MOE) has demonstrated promising potential in image data protection owing to its parallel processing capability and abundant degrees of freedom. However, existing methods suffer from either low compression ratios or stringent experimental conditions, such as accurate calibration of phase modulation, precise manufacturing of encryption elements, and no ambient light interference. This work introduces a lensless sparse point spread function-based multi-image optical encryption (sPSF-MOE) technique that addresses these challenges and enhances performance. In the encryption process, each plaintext image is encoded using a sparsely distributed PSF with specifically designed geometric shapes through spatial phase engineering. The resulting ciphertexts are superimposed to produce a compressed ciphertext. During decryption, an iterative algorithm recovers encrypted images with improved reconstruction quality. We show that sPSF-MOE ensures high fidelity for binary (gray-scale) images at a compression ratio of 12 (6) and resists autocorrelation-based attacks. Integrating principal component analysis (PCA) into decryption preserves image high fidelity under ambient light interference. sPSF-MOE reduces the bandwidth requirement for data transmission while ensuring data integrity. Optical encryption is used to protect image data but often struggles with low compression or strict experimental setups. The authors introduce a lensless method that securely encrypts multiple images with high compression and robustness, using smart point spread patterns and compressed sensing algorithms.

Communications PhysicsVol. 8(1)
Shanxi University (CN), Shandong Normal University (CN), East China Normal University (CN)
National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 4%
Chaos-based Image/Signal Encryption
6.38
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.