Learning doubly sparse transforms for image representation

Saiprasad Ravishankar, Yoram Bresler

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The sparsity of images in a fixed analytic transform domain or dictionary such as DCT or Wavelets has been exploited in many applications in image processing including image compression. Recently, synthesis sparsifying dictionaries that are directly adapted to the data have become popular in image processing. However, the idea of learning sparsifying transforms has received only little attention. We propose a novel problem formulation for learning doubly sparse transforms for signals or image patches. These transforms are a product of a fixed, fast analytic transform such as the DCT, and an adaptive matrix constrained to be sparse. Such transforms can be learnt, stored, and implemented efficiently. We show the superior promise of our approach as compared to analytical sparsifying transforms such as DCT for image representation.

Original languageEnglish (US)
Title of host publication2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings
Pages685-688
Number of pages4
DOIs
StatePublished - Dec 1 2012
Event2012 19th IEEE International Conference on Image Processing, ICIP 2012 - Lake Buena Vista, FL, United States
Duration: Sep 30 2012Oct 3 2012

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2012 19th IEEE International Conference on Image Processing, ICIP 2012
CountryUnited States
CityLake Buena Vista, FL
Period9/30/1210/3/12

Fingerprint

Glossaries
Image processing
Image compression

Keywords

  • Analysis transforms
  • Dictionary learning
  • Image representation
  • Sparse representation
  • Structured transforms

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

Cite this

Ravishankar, S., & Bresler, Y. (2012). Learning doubly sparse transforms for image representation. In 2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings (pp. 685-688). [6466952] (Proceedings - International Conference on Image Processing, ICIP). https://doi.org/10.1109/ICIP.2012.6466952

Learning doubly sparse transforms for image representation. / Ravishankar, Saiprasad; Bresler, Yoram.

2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings. 2012. p. 685-688 6466952 (Proceedings - International Conference on Image Processing, ICIP).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ravishankar, S & Bresler, Y 2012, Learning doubly sparse transforms for image representation. in 2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings., 6466952, Proceedings - International Conference on Image Processing, ICIP, pp. 685-688, 2012 19th IEEE International Conference on Image Processing, ICIP 2012, Lake Buena Vista, FL, United States, 9/30/12. https://doi.org/10.1109/ICIP.2012.6466952
Ravishankar S, Bresler Y. Learning doubly sparse transforms for image representation. In 2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings. 2012. p. 685-688. 6466952. (Proceedings - International Conference on Image Processing, ICIP). https://doi.org/10.1109/ICIP.2012.6466952
Ravishankar, Saiprasad ; Bresler, Yoram. / Learning doubly sparse transforms for image representation. 2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings. 2012. pp. 685-688 (Proceedings - International Conference on Image Processing, ICIP).
@inproceedings{190e717002d24df3888d7b4018c4c829,
title = "Learning doubly sparse transforms for image representation",
abstract = "The sparsity of images in a fixed analytic transform domain or dictionary such as DCT or Wavelets has been exploited in many applications in image processing including image compression. Recently, synthesis sparsifying dictionaries that are directly adapted to the data have become popular in image processing. However, the idea of learning sparsifying transforms has received only little attention. We propose a novel problem formulation for learning doubly sparse transforms for signals or image patches. These transforms are a product of a fixed, fast analytic transform such as the DCT, and an adaptive matrix constrained to be sparse. Such transforms can be learnt, stored, and implemented efficiently. We show the superior promise of our approach as compared to analytical sparsifying transforms such as DCT for image representation.",
keywords = "Analysis transforms, Dictionary learning, Image representation, Sparse representation, Structured transforms",
author = "Saiprasad Ravishankar and Yoram Bresler",
year = "2012",
month = "12",
day = "1",
doi = "10.1109/ICIP.2012.6466952",
language = "English (US)",
isbn = "9781467325332",
series = "Proceedings - International Conference on Image Processing, ICIP",
pages = "685--688",
booktitle = "2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings",

}

TY - GEN

T1 - Learning doubly sparse transforms for image representation

AU - Ravishankar, Saiprasad

AU - Bresler, Yoram

PY - 2012/12/1

Y1 - 2012/12/1

N2 - The sparsity of images in a fixed analytic transform domain or dictionary such as DCT or Wavelets has been exploited in many applications in image processing including image compression. Recently, synthesis sparsifying dictionaries that are directly adapted to the data have become popular in image processing. However, the idea of learning sparsifying transforms has received only little attention. We propose a novel problem formulation for learning doubly sparse transforms for signals or image patches. These transforms are a product of a fixed, fast analytic transform such as the DCT, and an adaptive matrix constrained to be sparse. Such transforms can be learnt, stored, and implemented efficiently. We show the superior promise of our approach as compared to analytical sparsifying transforms such as DCT for image representation.

AB - The sparsity of images in a fixed analytic transform domain or dictionary such as DCT or Wavelets has been exploited in many applications in image processing including image compression. Recently, synthesis sparsifying dictionaries that are directly adapted to the data have become popular in image processing. However, the idea of learning sparsifying transforms has received only little attention. We propose a novel problem formulation for learning doubly sparse transforms for signals or image patches. These transforms are a product of a fixed, fast analytic transform such as the DCT, and an adaptive matrix constrained to be sparse. Such transforms can be learnt, stored, and implemented efficiently. We show the superior promise of our approach as compared to analytical sparsifying transforms such as DCT for image representation.

KW - Analysis transforms

KW - Dictionary learning

KW - Image representation

KW - Sparse representation

KW - Structured transforms

UR - http://www.scopus.com/inward/record.url?scp=84875861443&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84875861443&partnerID=8YFLogxK

U2 - 10.1109/ICIP.2012.6466952

DO - 10.1109/ICIP.2012.6466952

M3 - Conference contribution

AN - SCOPUS:84875861443

SN - 9781467325332

T3 - Proceedings - International Conference on Image Processing, ICIP

SP - 685

EP - 688

BT - 2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings

ER -