Dictionary construction in sparse methods for image restoration [electronic resource].
- Washington, D.C. : United States. Dept. of Energy, 2010. and Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
- Additional Creators:
- Los Alamos National Laboratory, United States. Department of Energy, and United States. Department of Energy. Office of Scientific and Technical Information
- Restrictions on Access:
- Free-to-read Unrestricted online access
- Sparsity-based methods have achieved very good performance in a wide variety of image restoration problems, including denoising, inpainting, super-resolution, and source separation. These methods are based on the assumption that the image to be reconstructed may be represented as a superposition of a few known components, and the appropriate linear combination of components is estimated by solving an optimization such as Basis Pursuit De-Noising (BPDN). Considering that the K-SVD constructs a dictionary which has been optimised for mean performance over a training set, it is not too surprising that better performance can be achieved by selecting a custom dictionary for each individual block to be reconstructed. The nearest neighbor dictionary construction can be understood geometrically as a method for estimating the local projection into the manifold of image blocks, whereas the K-SVD dictionary makes more sense within a source-coding framework (it is presented as a generalization of the k-means algorithm for constructing a VQ codebook), is therefore, it could be argued, less appropriate in principle, for reconstruction problems. One can, of course, motivate the use of the K-SVD in reconstruction application on practical grounds, avoiding the computational expense of constructing a different dictionary for each block to be denoised. Since the performance of the nearest neighbor dictionary decreases when the dictionary becomes sufficiently large, this method is also superior to the approach of utilizing the entire training set as a dictionary (and this can also be understood within the image block manifold model). In practical terms, the tradeoff is between the computational cost of a nearest neighbor search (which can be achieved very efficiently), or of increased cost at the sparse optimization.
- Published through SciTech Connect., 01/01/2010., "la-ur-10-00042", " la-ur-10-42", FT, and Wohlberg, Brendt.
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