Fast and Flexible Convolutional Sparse Coding

Felix Heide, Wolfgang Heidrich, Gordon Wetzstein; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 5135-5143

Abstract


Convolutional sparse coding (CSC) has become an increasingly important tool in machine learning and computer vision. Image features can be learned and subsequently used for classification and reconstruction tasks. As opposed to patch-based methods, convolutional sparse coding operates on whole images, thereby seamlessly capturing the correlation between local neighborhoods. In this paper, we propose a new approach to solving CSC problems and show that our method converges significantly faster and also finds better solutions than the state of the art. In addition, the proposed method is the first efficient approach to allow for proper boundary conditions to be imposed and it also supports feature learning from incomplete data as well as general reconstruction problems.

Related Material


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[bibtex]
@InProceedings{Heide_2015_CVPR,
author = {Heide, Felix and Heidrich, Wolfgang and Wetzstein, Gordon},
title = {Fast and Flexible Convolutional Sparse Coding},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}