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Two dimensional (2D) sparse representation provides promising performance in image denoising by cooperatively exploiting horizontal and vertical features inherent in images by two dictionaries. In this paper, we first propose integrating the 2D sparse model with clustering and nonlocal regularization into a unified variational framework, defined as 2D nonlocal sparse representation (2DNSR), for optimization...
Image super-resolution based on sparse model with patch clustering and nonlocal similarity provides promising performance. However, the traditional one dimensional (1D) sparse model enforces a 1D dictionary for every cluster of patches to capture complex structures and different features in images. The total dictionary will take expensive memory, which can be alleviated at cost of representation power...
Image super-resolution with sparsity prior provides promising performance. However, traditional sparse-based super resolution methods transform a two dimensional (2D) image into a one dimensional (1D) vector, which ignores the intrinsic 2D structure as well as spatial correlation inherent in images. In this paper, we propose the first image super-resolution method which reconstructs a high resolution...
Dictionary learning for sparse representation has been an active topic in the field of image processing. Most existing dictionary learning schemes focus on the representation ability of the learned dictionary. However, according to the theory of compressive sensing, the mutual incoherence of the dictionary is of crucial role in the sparse coding. Thus incoherent dictionary is desirable to improve...
Sparse representation has been proved to be very efficient in machine learning and image processing. Traditional image sparse representation formulates an image into a one dimensional (1D) vector which is then represented by a sparse linear combination of the basis atoms from a dictionary. This 1D representation ignores the local spatial correlation inside one image. In this paper, we propose a two...
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