Image De-Blurring Based on Constraint Conditional Model

Abstract

Image capturing is more vulnerable to the various physical limitations such as defocus, low lighting and camera shaking; this makes the image blurry and noisy. Moreover De-blurring is the process to recover the original image from the given degraded image. De-blurring technique uses the estimated blur kernel for achieving the optimal restored image with the sharp features, however the accuracy has been one of the major concern , hence in this paper we use Constrained Conditional model (CCM) for restoring the image. Moreover, here two different methods are integrated i.e. conditional model and convergence operator, these two combined learns the model and efficiently and provides the better results. In order to evaluate the proposed model, Levin dataset is used by considering the two eminent model metric i.e. PSNR and SSIM and CCM based model outperforms the other state-of-art technique.