Learning sparse dictionaries with a popularity-based model

Abstract

Sparse signal representation based on overcomplete dictionaries has recently been extensively investigated, rendering the state-of-the-art results in signal, image and video processing. We propose a novel dictionary learning algorithm-the PK-SVD algorithm-which assumes prior probabilities on the dictionary atoms and learns a sparse dictionary under a popularity-based model. The prior distribution brings the flexibility that is desirable in applications. We examine our algorithm in both synthetic tests and image denoising experiments.

Publication
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Jianzhou Feng
Jianzhou Feng
PH.D Student
Li Song
Li Song
Professor, IEEE Senior Member

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