Image super-resolution via non-local steering kernel regression regularization

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Conference Proceeding
2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings, 2013, pp. 943 - 946
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In this paper, we employ the non-local steering kernel regression to construct an effective regularization term for the single image super-resolution problem. The proposed method seamlessly integrates the properties of local structural regularity and non-local self-similarity existing in natural images, and solves a least squares minimization problem for obtaining the desired high-resolution image. Extensive experimental results on both simulated and real low-resolution images demonstrate that the proposed method can restore compelling results with sharp edges and fine textures. © 2013 IEEE.
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