Name
Abstract | ||
|---|---|---|
In this paper we formulate a new time dependent convolutional model for super-resolution based on a constrained variational model that uses the total variation of the signal as a regularizing functional. We propose an iterative refinement procedure based on Bregman iteration to improve spatial resolution. The model uses a dataset of low resolution images and incorporates a downsampling operator to relate the high resolution scale to the low resolution one. We present an algorithm for the model and we perform a series of numerical experiments to show evidence of the good behavior of the numerical scheme and quality of the results. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/s10915-008-9214-8 | J. Sci. Comput. |
Keywords | Field | DocType |
high resolution,low resolution,super resolution,spatial resolution,downsampling,total variation | Iterative refinement,Bregman iteration,Mathematical optimization,Variational model,Regularization (mathematics),Operator (computer programming),Upsampling,Superresolution,Image resolution,Mathematics | Journal |
Volume | Issue | ISSN |
37 | 3 | 0885-7474 |
Citations | PageRank | References |
181 | 4.76 | 13 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antonio Marquina | 1 | 431 | 45.30 |
| Stanley Osher | 2 | 7973 | 514.62 |