Learning sparse dictionaries with a popularity-based model

Jianzhou Feng, Li Song, Xiaoming Huo, Xiaokang Yang, Wenjun Zhang
May 2011
Cite DOI

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.

Type
Conference paper
Publication
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
"Encoding" "Dictionaries" "Dictionary learning" "K-SVD" "Image denoising" "Noise reduction" "Matching pursuit algorithms" "Noise level" "OMP" "PK-SVD" "Sparse matrices" "sparse representation"
Li Song
Li Song
Professor, IEEE Senior Member

Professor, Doctoral Supervisor, the Deputy Director of the Institute of Image Communication and Network Engineering of Shanghai Jiao Tong University, the Double-Appointed Professor of the Institute of Artificial Intelligence and the Collaborative Innovation Center of Future Media Network, the Deputy Secretary-General of the China Video User Experience Alliance and head of the standards group.