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Sparse representation is very active area in computer vision and image analysis. It has many applications in de-noising, stereo vision, image painting, image restoration, image de-blurring and many. For sparse modeling, there is need to design an appropriate dictionary. However, there are many dictionaries used for sparse modeling and were reported in literature. In this paper, we implemented the...
Image up-sampling in the discrete cosine transform (DCT) domain is a challenging problem because DCT coefficients are de-correlated, such that it is nontrivial to estimate directly high-frequency DCT coefficients from observed low-frequency DCT coefficients. In the literature, DCT-based up-sampling algorithms usually pad zeros as high-frequency DCT coefficients or estimate such coefficients with limited...
Sparse representation based on over-complete dictionary is a new signal representation theory. Recent activity in this field concentrated mainly on the study of sparse decomposition algorithm and dictionary design algorithm. In this paper, a novel dictionary design algorithm called K-LMS is proposed. It generalized the k-means clustering process, for adapting dictionaries to achieve sparse representation...
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