We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Jointly Optimized Regressors for Image Super-resolution.
- Authors
Dai, D.; Timofte, R.; Van Gool, L.
- Abstract
Learning regressors from low-resolution patches to high-resolution patches has shown promising results for image super-resolution. We observe that some regressors are better at dealing with certain cases, and others with different cases. In this paper, we jointly learn a collection of regressors, which collectively yield the smallest super-resolving error for all training data. After training, each training sample is associated with a label to indicate its 'best' regressor, the one yielding the smallest error. During testing, our method bases on the concept of 'adaptive selection' to select the most appropriate regressor for each input patch. We assume that similar patches can be super-resolved by the same regressor and use a fast, approximate kNN approach to transfer the labels of training patches to test patches. The method is conceptually simple and computationally efficient, yet very effective. Experiments on four datasets show that our method outperforms competing methods.
- Subjects
HIGH resolution imaging; NONLINEAR imaging sensors; REGRESSION analysis; MATHEMATICAL optimization; A posteriori error analysis
- Publication
Computer Graphics Forum, 2015, Vol 34, Issue 2, p95
- ISSN
0167-7055
- Publication type
Article
- DOI
10.1111/cgf.12544