Jointly Optimized Regressors for Image Super-resolution
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, wh...
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| Published in | Computer graphics forum Vol. 34; no. 2; pp. 95 - 104 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.05.2015
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| Subjects | |
| Online Access | Get full text |
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| 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. |
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| AbstractList | 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. |
| Author | Timofte, R. Van Gool, L. Dai, D. |
| Author_xml | – sequence: 1 givenname: D. surname: Dai fullname: Dai, D. organization: Computer Vision Lab, ETH Zürich, Switzerland – sequence: 2 givenname: R. surname: Timofte fullname: Timofte, R. organization: Computer Vision Lab, ETH Zürich, Switzerland – sequence: 3 givenname: L. surname: Van Gool fullname: Van Gool, L. organization: Computer Vision Lab, ETH Zürich, Switzerland |
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| Snippet | Learning regressors from low‐resolution patches to high‐resolution patches has shown promising results for image super‐resolution. We observe that some... Learning regressors from low-resolution patches to high-resolution patches has shown promising results for image super-resolution. We observe that some... |
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| SubjectTerms | Analysis Approximation Categories and Subject Descriptors (according to ACM CCS) Computational efficiency Computer graphics Dealing Errors I.3.3 [Computer Graphics]: Image Generation-Display algorithms I.4.3 [Image Processing and Computer Vision]: Enhancement-Sharpening and deblurring Image processing systems Image resolution Labels Learning Optimization Regression analysis Studies Training |
| Title | Jointly Optimized Regressors for Image Super-resolution |
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