Name
Abstract | ||
|---|---|---|
This paper studies a type of image priors that are constructed implicitly through the alternating direction method of multiplier (ADMM) algorithm, called the algorithm-induced prior. Different from classical image priors which are defined before running the reconstruction algorithm, algorithm-induced priors are defined by the denoising procedure used to replace one of the two modules in the ADMM algorithm. Since such prior is not explicitly defined, analyzing the performance has been difficult in the past. Focusing on the class of symmetric smoothing filters, this paper presents an explicit expression of the prior induced by the ADMM algorithm. The new prior is reminiscent to the conventional graph Laplacian but with stronger reconstruction performance. It can also be shown that the overall reconstruction has an efficient closed-form implementation if the associated symmetric smoothing filter is low rank. The results are validated with experiments on image inpainting. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Computer Vision and Pattern Recognition | Noise reduction,Computer science,Multiplier (economics),Reconstruction algorithm,Artificial intelligence,Image restoration,Laplacian matrix,Pattern recognition,Algorithm,Inpainting,Smoothing,Prior probability,Machine learning |
DocType | Volume | Citations |
Journal | abs/1602.00715 | 5 |
PageRank | References | Authors |
0.43 | 13 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stanley H. Chan | 1 | 403 | 30.95 |