Name
Abstract | ||
|---|---|---|
The sparsity of images in a transform domain or dictionary has been widely exploited in image processing. Compared to the synthesis dictionary model, sparse coding in the (single) transform model is cheap. However, natural images typically contain diverse textures that cannot be sparsified well by a single transform. Hence, we propose a union of sparsifying transforms model, which is equivalent to an overcomplete transform model with block cosparsity (OC-TOBOS). Our alternating algorithm for transform learning involves simple closed-form updates. When applied to images, our algorithm learns a collection of well-conditioned transforms, and a good clustering of the patches or textures. Our learnt transforms provide better image representations than learned square transforms. We also show the promising denoising performance and speedups provided by the proposed method compared to synthesis dictionary-based denoising. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICIP.2014.7025161 | Image Processing |
Keywords | Field | DocType |
image denoising,image representation,image texture,pattern clustering,transforms,OC-TOBOS,block cosparsity,closed-form updates,clustering,diverse textures,image processing,image representations,natural images,overcomplete sparsifying transform learning,single transform model,speedups,synthesis dictionary-based denoising,Clustering,Image denoising,Overcomplete representation,Sparse representation,Sparsifying transform learning | Noise reduction,Computer vision,K-SVD,Pattern recognition,Neural coding,Computer science,Sparse approximation,Image processing,Image denoising,Artificial intelligence,Cluster analysis | Conference |
ISSN | Citations | PageRank |
1522-4880 | 5 | 0.44 |
References | Authors | |
15 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bihan Wen | 1 | 225 | 18.64 |
| Saiprasad Ravishankar | 2 | 587 | 36.58 |
| Yoram Bresler | 3 | 1104 | 119.17 |