Design of Robust Sensing Matrix for UAV Images Encryption and Compression

The sparse representation error (SRE) exists when the images are represented sparsely. The SRE is particularly large in unmanned aerial vehicles (UAV) images due to the disturbance of the harsh environment or the instability of its flight, which will bring more noise. In the compressed sensing (CS)...

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Bibliographic Details
Published inApplied sciences Vol. 13; no. 3; p. 1575
Main Authors Jiang, Qianru, Bai, Huang, He, Xiongxiong
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.02.2023
Subjects
Accuracy
Algorithms
compressed sensing
Compression
Data compression
Data encryption
Design
Dictionaries
encryption
Image compression
lower SRE
Representations
sensing matrix design
Sparsity
UAV images
Unmanned aerial vehicles
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Summary:The sparse representation error (SRE) exists when the images are represented sparsely. The SRE is particularly large in unmanned aerial vehicles (UAV) images due to the disturbance of the harsh environment or the instability of its flight, which will bring more noise. In the compressed sensing (CS) system, the projected SRE in the compressed measurement will bring a significant challenge to the recovery accuracy of the images. In this work, a new SRE structure is proposed. Following the new structure, a lower sparse representation error (LSRE) is achieved by eliminating groups of sparse representation. With the proposed LSRE modeling, a robust sensing matrix is designed to compress and encrypt the UAV images. Experiments for UAV images are carried out to compare the recovery performance of the proposed algorithm with the existing related algorithms. The results of the proposed algorithm reveal superior recovery accuracy. The new CS framework with the proposed sensing matrix to address the scenario of UAV images with large SRE is dominant.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13031575