The Iterative Method with Adaptive Thresholding (IMAT) is a fast and efficient algorithm for sparse signal reconstruction. It was originally proposed for sparse signal reconstruction from random/missing samples. It was further modified to IMATI by adding interpolation to each iteration to achieve faster convergence. Another modification of IMAT, namely IMATCS was also proposed for compressed sensing recovery. IMAT proved to outperform Orthogonal Matching Pursuit (OMP) and Iterative Hard Thresholding (IHT) techniques in some applications regarding complexity and reconstruction quality. This algorithm gradually extracts the sparse signal components by iterative thresholding of the estimated signal promoting sparsity.

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last updated 8/9/2016