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

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