Name
Abstract | ||
|---|---|---|
This paper proposes an extension of total variation (TV) image deconvolution technique that enhances image quality over classical TV while preserving algorithm speed. Enhancement is achieved by altering the regularization term to include directional decompositions before the gradient operator. Such decompositions select areas of the image with characteristics that are more suitable for a certain type of gradient than another. Speed is guaranteed by the use of the augmented Lagrangian approach as basis for the algorithm. Experimental evidence that the proposed approach improves TV deconvolution is provided, as well as an outline for a future work aiming to support and substantiate the proposed method. |
Year | Venue | Keywords |
|---|---|---|
2012 | European Signal Processing Conference | Total variation,augmented Lagrangian,image deconvolution,image restoration,directional decompositions |
Field | DocType | ISSN |
Computer vision,Image gradient,Blind deconvolution,Deconvolution,Image quality,Augmented Lagrangian method,Regularization (mathematics),Operator (computer programming),Artificial intelligence,Image restoration,Mathematics | Conference | 2076-1465 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel R. Pipa | 1 | 21 | 5.41 |
| Stanley H. Chan | 2 | 403 | 30.95 |
| Truong Q. Nguyen | 3 | 1402 | 136.69 |