Performance Comparison of Different CS based Reconstruction Methods for WSN Application

Compression of the Wireless Sensor Network (WSN) images are always challenging, as we need to maintain the quality of the reconstructed images with restricted resources and the bandwidth of transmission is also less. To meet these diverging requirements Compressive sensing is one of the best among all solutions. In this paper, we have compared the performance of two popular reconstruction methods for compressive sensing namely Orthogonal Matching Pursuits and L1-minimization found in the literature. The performances of these two methods are compared for standard dataset images as well as real wildlife CCTV captured color images. Result analysis shows that OMP method has faster processing with lower stability and L1 method has slower processing with higher stability.