Centralized Sparse Representation Non-locally For Image Restoration

Sparse representation based image restoration is an interesting and challenging field of research in image processing and it is used in many real life applications. In this paper represent the non-locally centralized sparse representation model for image restoration. Sparse coding noise is used to define the sparse code of the degraded image and unknown original image; it is also used to increase the performance of sparsity based image restoration sparse representation model shown promising results in various image restoration applications. In this paper sparse coding noise is introduced and the goal of image restoration changes the sparse coding noise. Standard sparse representation model used to solve the image restoration problem. Image restoration intends to recover high resolution image from low resolution image. Non-local means approach to image de-noising, where the prominence of self similarities is used as a prior on natural images. Sparse representation model sufficient for reconstruction of the original image.