Learning sparsifying transforms for image processing

Saiprasad Ravishankar, Yoram Bresler

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


The sparsity of signals and images in a certain analytically defined transform domain or dictionary such as discrete cosine transform or wavelets has been exploited in many applications in signal and image processing. Recently, the idea of learning a dictionary for sparse representation of data has become popular. However, while there has been extensive research on learning synthesis dictionaries, the idea of learning analysis sparsifying transforms has received only little attention. We propose a novel problem formulation and an alternating algorithm for learning well-conditioned square sparsifying transforms from data. We show the superiority of our approach for image representation over analytical sparsifying transforms such as the DCT. We also show promise in image denoising. Denoising using the learnt analysis transforms is not only better than by synthesis dictionaries learnt using the K-SVD algorithm but also faster.

Original languageEnglish (US)
Title of host publication2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings
Number of pages4
StatePublished - 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


Other2012 19th IEEE International Conference on Image Processing, ICIP 2012
Country/TerritoryUnited States
CityLake Buena Vista, FL


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

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems


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