Name
Title | ||
|---|---|---|
Single Noisy Image Super Resolution by Minimizing Nuclear Norm in Virtual Sparse Domain. |
Abstract | ||
|---|---|---|
Super-resolving a noisy image is a challenging problem, and needs special care as compared to the conventional super resolution approaches, when the power of noise is unknown. In this scenario, we propose an approach to super-resolve single noisy image by minimizing nuclear norm in a virtual sparse domain that tunes with the power of noise via parameter learning. The approach minimizes nuclear norm to explore the inherent low-rank structure of visual data, and is further augmented with coarse-to-fine information by adaptively re-aligning the data along the principal components of a dictionary in virtual sparse domain. The experimental results demonstrate the robustness of our approach across different powers of noise. |
Year | Venue | Field |
|---|---|---|
2017 | NCVPRIPG | Computer vision,Computer science,Parameter learning,Matrix norm,Robustness (computer science),Artificial intelligence,Superresolution,Principal component analysis |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
22 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Srimanta Mandal | 1 | 50 | 5.42 |
| A. N. Rajagopalan | 2 | 1106 | 92.02 |