Dictionary learning: From data to sparsity via clustering

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