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Recently there has been increasing attention directed towards the analysis sparsity models. Consequently, there is a quest for learning the operators which would enable analysis sparse representations for signals in hand. Analysis operator learning algorithms such as the Analysis K-SVD have been proposed. Sparsifying transform learning is a paradigm which(More)
Analysis sparsity and the accompanying analysis operator learning problem provide an important framework for signal modeling. Very recently, sparsifying transform learning has been put forward as an effective and new formulation for the analysis operator learning problem. In this study, we develop a new sparsifying transform learning algorithm by using the(More)
The superresolution problem can be formulated as reconstructing a high resolution image from a down-scaled and possibly blurred version. This problem is a highly ill-posed inverse problem. To regularize this ill-posed inverse problem different methods have been used in previous works, where the use of sparse representation has been quite popular recently.(More)
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