A Novel Sparse Representation Method for Image Restoration Applications
Journal Title: International Journal of Computer Science Engineering and Technology (IJCSET) - Year 2012, Vol 2, Issue 9
Abstract
Sparse representation has been widely used in various image restoration applications. The quality of image restoration mainly depends on whether the used sparse domain can represent well the underlying image. Since the contents representing the underlying image can vary significantly across different images or different patches in an image, we propose to learn various sets of bases from a pre-collected dataset of example image patches and for processing a particular given patch, a suitable set of base is selected adaptively as local sparse domain. Here we introduce two adaptive regularization terms into the sparse representation framework. One is a set of auto regressive (AR) models are learned from the pre-collected dataset of example image patches and the best fitted AR model is adaptively selected for regularization. Second is image non-local self similarity regularization to regularize the image local structures. To make the sparse coding more accurate, a centralized sparsity constraint is introduced by exploiting the nonlocal image statistics. The local sparsity and the nonlocal sparsity constraints are unified into a variational framework for optimization. Extensive experiments on the proposed method of CSR method achieves convincing improvement over previous state-of-the-art methods in terms of PSNR and SSIM values.[/b]
Authors and Affiliations
K. S. K. L. Priyanka , U. V. Ratna Kumari
Keywords
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