engleski

Performance of compressive sensing image reconstruction for search and rescue

In the paper, the system combining compressive sensing based image reconstruction and object detection algorithm is introduced. The use of compressive sensing is a promising approach for search and rescue applications since it highly reduces 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 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 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 PSNR.

compressed sensing ; Image reconstruction ; image quality ; Object detection ; search and rescue ; unmanned aerial vehicles

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