Name
Title | ||
|---|---|---|
Ingredient separation of natural images: a multiple transform domain method based on sparse coding strategy |
Abstract | ||
|---|---|---|
'Sparse coding' is a ubiquitous strategy employed in the sensory information process system of mammals. Such strategy aims to find a representation of data in which the components of the representation are only rarely significantly active. This paper presents a multiple transform domain image model and demonstrates that it may be used to separate natural images into different ingredients based on the sparse coding strategy. In such model an overcomplete dictionary is constructed by combining different type of complete or over-complete systems that can respectively deal with different image ingredients. Based on a sparse prior restriction, decomposition coefficients are inferred by maximizing a posterior distribution. The resulting coefficients belonging to different systems correspond to different image ingredients. The proposed multiple transform domain image model provides a flexible framework for image ingredient separation which allows one to extract image structure of special interest. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74260-9_67 | ICIAR |
Keywords | Field | DocType |
different image ingredient,image structure,domain image model,different system,sparse coding strategy,sparse coding,image ingredient separation,domain method,different ingredient,natural image,different type,posterior distribution,information processing | Computer vision,Pattern recognition,K-SVD,Neural coding,Computer science,Ingredient,Sparse approximation,Posterior probability,Artificial intelligence,Prior probability,Image structure | Conference |
Volume | ISSN | ISBN |
4633 | 0302-9743 | 3-540-74258-1 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
1 | ||