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Efficient learning of sparse image decompositions
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We investigate a number of approaches for the sparse coding of images where the basis set is learned. These learning techniques are efficient and can be applied to a range of sparsity types in a convolutional setting. The performance of different forms of sparsity are compared on low-level tasks such as denoising and in-painting.