Multi-object parallel optical encryption for single-pixel imaging security

Multi-object optical encryption has emerged as a promising field for enhancing encryption performance and reducing communication overhead, which can ensure the security of single-pixel imaging. However, it faces some critical challenges like low efficiency, reliance on complex system design, high hardware costs, and the difficulty of parallel encryption with independent decryption, hindering practical deployment. In this work, an illumination pattern construction algorithm is proposed to generate projected basis patterns for parallel optical encryption, while a decoupling algorithm is developed to demodulate coupled measurements to recover Hadamard spectrum for each object. Independent decryption is then performed using a random-order Hadamard matrix assigned to each object. The proposed method achieves multi-object parallel encryption and independent decryption using a lightweight optical system, with improved efficiency, lower hardware costs, and smaller ciphertext size compared to conventional methods. In addition, it also exhibits robustness against both cropping and noise attacks. These results indicate potential for practical applications and may contribute to future developments in multi-object optical encryption for single-pixel imaging security.

Authors

Institutions

Publication Details

Journal
Optics Express
Published
2025-07-24
DOI
https://doi.org/10.1364/oe.572099
Citations
2
Primary Topic
Random lasers and scattering media
Type
article
Field-Weighted Citation Impact
2.60
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Multi-object parallel optical encryption for single-pixel imaging security

Guilin Wu, Seoktae Kim, Xiaowei Li, Chongyang Zhang et al.
2 citations
Optics Express
Random lasers and scattering media
2.60
article

Multi-object parallel optical encryption for single-pixel imaging security

Guilin Wu, Seoktae Kim, Xiaowei Li, Chongyang Zhang, Hongran Zeng
article en
2 citations

Abstract

Multi-object optical encryption has emerged as a promising field for enhancing encryption performance and reducing communication overhead, which can ensure the security of single-pixel imaging. However, it faces some critical challenges like low efficiency, reliance on complex system design, high hardware costs, and the difficulty of parallel encryption with independent decryption, hindering practical deployment. In this work, an illumination pattern construction algorithm is proposed to generate projected basis patterns for parallel optical encryption, while a decoupling algorithm is developed to demodulate coupled measurements to recover Hadamard spectrum for each object. Independent decryption is then performed using a random-order Hadamard matrix assigned to each object. The proposed method achieves multi-object parallel encryption and independent decryption using a lightweight optical system, with improved efficiency, lower hardware costs, and smaller ciphertext size compared to conventional methods. In addition, it also exhibits robustness against both cropping and noise attacks. These results indicate potential for practical applications and may contribute to future developments in multi-object optical encryption for single-pixel imaging security.

Optics ExpressVol. 33(18)
Pukyong National University (KR)
Openalex Percentile: Top 11%
Random lasers and scattering media
2.60
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.

Multi-object parallel optical encryption for single-pixel imaging security — Guilin Wu, Seoktae Kim, et al. · Optics Express (2025) | TGRS Research Map | TGRS