Sparse representation based image and video processing have recently drawn much attention. Dictionary#R##N##R##N#learning is an essential task in this framework. Our novel proposition involves direct computation of the#R##N##R##N#dictionary by analyzing the distribution of training data in the metric space. The resulting representation is#R##N##R##N#applied in the domain of grey scale image denoising. Denoising is one of the fundamental problems in image#R##N##R##N#processing. Sparse representation deals efficiently with this problem. In this regard, dictionary learning from#R##N##R##N#noisy images, improves denoising performance. Experimental results indicate that our proposed approach#R##N##R##N#outperforms the ones using K-SVD for additive high-level Gaussian noise while for the medium range of#R##N##R##N#noise level, our results are comparable.
['Rajesh Bhatt', 'Venkatesh K. Subramanian']
Dictionary Learning: From Data to Sparsity Via Clustering