Name
Abstract | ||
|---|---|---|
This paper presents a promising super-resolution (SR) approach using maximum a posteriori (MAP) estimation. We consider the high resolution (HR) estimation as a Markov Random Field (MRF), using a transformed gradient field prior to repair the image fuzzy problem caused by MRF. An improved Normalized Convolution method is proposed to obtain a first good estimation. We build a reasonable energy function and minimize the posterior energy by gradient descent algorithm. Experimental results on realistic image sequence and comparisons with several other SR techniques show that our approach gives the best results both qualitative and quantitative. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.7148/2013-0838 | PROCEEDINGS 27TH EUROPEAN CONFERENCE ON MODELLING AND SIMULATION ECMS 2013 |
Keywords | Field | DocType |
Super-Resolution, Markov Random Field, Transformed Gradient Field, Maximum A Posteriori, Normalized Convolution | Gradient descent,Normalization (statistics),Vector field,Markov random field,Convolution,Fuzzy logic,Algorithm,Maximum a posteriori estimation,Superresolution,Mathematics | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin-Long Lu | 1 | 0 | 0.34 |
| Sheng-Yong Chen | 2 | 1077 | 114.06 |
| Xin Wang | 3 | 114 | 10.47 |
| Sheng Liu | 4 | 5 | 8.58 |
| C Yan | 5 | 41 | 6.90 |
| Xianping Huan | 6 | 0 | 0.34 |