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Single-image super-resolution via local learning
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DOI：10.1007/s13042-011-0011-6.png)
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Nearest neighbor-based algorithms are popular in example-based super-resolution from a single image. The core idea behind such algorithms is that similar images are close in the sense of distance measurement. However, it is well known in the field of machine learning and statistical learning theory that the generalization of the nearest neighbor-based estimation is poor, when complex or high dimensional data are considered. To improve the power of the nearest neighbor-based algorithms in single-image based super-resolution, a local learning method is proposed in this paper. Similar to the nearest neighbor-based algorithms, a local training set is generated according to the similarity between the training samples and a given test sample. For super-resolving the given test sample, a local regression function is learned on the local training set. The generalization of nearest neighbor-based algorithms can be enhanced by the process of local regression. Based on such an idea, we propose a novel local-learning-based algorithm, where kernel ridge regression algorithm is used in local regression for its well generalization. Some experimental results verify the effectiveness and efficiency of the local learning algorithm in single-image based super-resolution.
Keyword:
Super-resolution
Local learning
Generalization
Reproducing kernel
Kernel ridge regression
Similarity
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