1. A method for parallel adaptive signal reconstruction from a multitude of measurements of an input signal, the method comprising the steps of: (a) establishing sparse transforms Q and W to provide a sparse representation of the input signal, wherein the sparse representation of the input signal includes active components of the sparse representation; (b) establishing a sparsity measure based on a number of active components of the sparse representation; (c) using the sparsity measure to establish a sparsity constraint determining whether a sparse code is sparse enough by the sparsity measure; (d) establishing a cost function containing the sparsity measure and a correlation quality of reconstruction of the input signal; (e) receiving an input signal in the form of n dimensional sampled values; (f) calculating a first sparse code by a selection based method (SM), wherein the first sparse code is calculated using non-zero sampled values of the input signal; (g) sparsification of said first sparse code; (h) calculating using one or more hardware processor a usage rate of the active components of said sparse code; (i) calculating using the one or more hardware processor similarity and dissimilarity measures between the active components of said sparse code; (j) determining a learning rate for the active components based on the usage rate and similarity and dissimilarity measures of the active components of said sparse code; (k) updating said transforms Q and W with said learning rate to decrease said cost function; (l) tuning of said transforms Q and W so that the low usage rate components of said sparse code become more frequent; and (m) removing values below a predetermined threshold from said transforms Q and W and normalization of said transform Q.