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Cite Details

Brendt Wohlberg, "Efficient Convolutional Sparse Coding", in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), (Florence, Italy), doi:10.1109/ICASSP.2014.6854992, pp. 7173--7177, May 2014


When applying sparse representation techniques to images, the standard approach is to independently compute the representations for a set of overlapping image patches. This method performs very well in a variety of applications, but the independent sparse coding of each patch results in a representation that is not optimal for the image as a whole. A recent development is convolutional sparse coding, in which a sparse representation for an entire image is computed by replacing the linear combination of a set of dictionary vectors by the sum of a set of convolutions with dictionary filters. A disadvantage of this formulation is its computational expense, but the development of efficient algorithms has received some attention in the literature, with the current leading method exploiting a Fourier domain approach. The present paper introduces a new way of solving the problem in the Fourier domain, leading to substantially reduced computational cost.

BibTeX Entry

author = {Brendt Wohlberg},
title = {Efficient Convolutional Sparse Coding},
year = {2014},
month = May,
urlpdf = {http://math.lanl.gov/~brendt/Publications/Docs/wohlberg-2014-efficient.pdf},
urlcode = {http://math.lanl.gov/~brendt/Software/SPORCO/},
booktitle = {Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
address = {Florence, Italy},
doi = {10.1109/ICASSP.2014.6854992},
pages = {7173--7177}