Design of Robust Sensing Matrix for UAV Images Encryption and Compression

Abstract

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.

Securing High-Resolution Images From Unmanned Aerial Vehicles With DNA Encoding and Bit-Plane Extraction Method

“…U NMANNED aerial vehicles (UAVs), also known as Drones, are becoming more popular in a variety of industries for tasks such as security and surveillance. The drone sector is particularly thriving in sectors such as the military, and oil industries, where drones are used to inspect hazardous activities from the air and provide images for decision-making [1]. The importance of image encryption for drones is crucial due to their use in sensitive areas.…”