Learning sparsifying transforms for image processing

Abstract

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… (More)
DOI: 10.1109/ICIP.2012.6466951
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@article{Ravishankar2012LearningST, title={Learning sparsifying transforms for image processing}, author={Saiprasad Ravishankar and Yoram Bresler}, journal={2012 19th IEEE International Conference on Image Processing}, year={2012}, pages={681-684} }