De-Noising of Sparse Signals Using Mixture Model Shrinkage Function

In this work a new thresholding function referred to as ’mixture model shrinkage’ (MMS) based on the minimization of convex cost is proposed. Normally, functions underestimate larger signal amplitudes during de-noising process. The proposed more flexible shrinkage it solves underestimation problem greater extent and thus efficiently de-noises without affecting amplitudes. Expectation (EM) algorithm used find parameters along with majorization-minimization (MM) that minimize monotonic function. then applied for group sparse signals Shepp Logan phantom images. Our experimental study shows MMS outclasses current overlapping results suffering from underestimation. Furthermore, has smallest Root Mean Square Error (RMSE) signals.