Jointly Optimized Regressors for Image Super-resolution
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Date
2015
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
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 superresolving 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.
Description
@article{10.1111:cgf.12544,
journal = {Computer Graphics Forum},
title = {{Jointly Optimized Regressors for Image Super-resolution}},
author = {Dai, Dengxin and Timofte, Radu and Gool, Luc Van},
year = {2015},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
DOI = {10.1111/cgf.12544}
}