Image Restoration Using IIST Algorithm

The problem of restoration of digital images plays a central role in multitude important applications. A particularly challenging instance of this problem occurs in the case when the degradation phenomenon is modelled by ill-conditional operator. In such situation, the presence of noise makes it impossible to recover a valuable approximation of the image of interest without using some priori information called as simply priors is essential for image restoration, rendering it stable and robust to noise. Particularly, if the original image is known to be a piecewise smooth function, one of the standard priors used Rudin-Osher-Fatemi model which results in total variation (TV) based image restoration. We proposed an algorithm for unconstrained optimization problem where the objective function includes a data fidelity term and a nonsmooth regulaizer.Total Variation method is employed to find solution of the problem based on the improved iterative shrinkage thresholding algorithm (IITSA). IISTA is performed through a recursive application of two simple procedures linear filtering and soft thresholding. An experimental result shows that better performance of the algorithm when compared with the existing methods.