Learning phase-invariant dictionaries
Authors:
- 1 May 2013
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 5979-5983
Abstract
In this paper, we present a novel algorithm to learn phase-invariant dictionaries, which can be used to efficiently approximate a variety of signals, such as audio signals or images. Our approach relies on finding a small number of generating atoms that can be used-along with their phase-shifts-to sparsely approximate a given signal. Our method is inspired by the K-SVD algorithm, but imposes an extra constraint that the dictionaries we learn are phase-invariant. We show that the learned dictionaries achieve competitive approximation performance compared to that of state-of-the-art methods for audio signals and images, while substantially reducing the storage requirements and computational complexity.Keywords
This publication has 20 references indexed in Scilit:
- Dictionary learning from sparsely corrupted or compressed signals2012
- Image Super-Resolution Via Sparse RepresentationIEEE Transactions on Image Processing, 2010
- Sparse and Redundant Modeling of Image Content Using an Image-Signature-DictionarySIAM Journal on Imaging Sciences, 2008
- Image Denoising Via Sparse and Redundant Representations Over Learned DictionariesIEEE Transactions on Image Processing, 2006
- $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse RepresentationIEEE Transactions on Signal Processing, 2006
- Compressed sensingIEEE Transactions on Information Theory, 2006
- Stable signal recovery from incomplete and inaccurate measurementsCommunications on Pure and Applied Mathematics, 2006
- Greed is Good: Algorithmic Results for Sparse ApproximationIEEE Transactions on Information Theory, 2004
- Sparse coding with an overcomplete basis set: A strategy employed by V1?Vision Research, 1997
- Emergence of simple-cell receptive field properties by learning a sparse code for natural imagesNature, 1996