Performance of Compressive Sensing Image Reconstruction for Search and Rescue

Abstract

In this letter, a system combining compressive sensing (CS)-based image reconstruction and object detection algorithm is introduced. The use of CS is a promising approach for search-and-rescue applications, since it highly reduces the amount of data that needs to be transmitted. However, the high-quality reconstruction of such images is a challenging task due to the complexity of structures and the number of tiny details, possibly being the objects of interest. Hence, the performance of image reconstruction is evaluated in terms of the missing data amount and the object detection quality. Object detection is performed by applying two-stage data segmentation algorithm based on mean shift clustering. The results quality is measured using structural similarity index and peak signal-to-noise ratio.