Fast Convolutional Sparse Coding

Hilton Bristow, Anders Eriksson, Simon Lucey; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 391-398


Sparse coding has become an increasingly popular method in learning and vision for a variety of classification, reconstruction and coding tasks. The canonical approach intrinsically assumes independence between observations during learning. For many natural signals however, sparse coding is applied to sub-elements ( i.e. patches) of the signal, where such an assumption is invalid. Convolutional sparse coding explicitly models local interactions through the convolution operator, however the resulting optimization problem is considerably more complex than traditional sparse coding. In this paper, we draw upon ideas from signal processing and Augmented Lagrange Methods (ALMs) to produce a fast algorithm with globally optimal subproblems and super-linear convergence.

Related Material

author = {Bristow, Hilton and Eriksson, Anders and Lucey, Simon},
title = {Fast Convolutional Sparse Coding},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}