Example-Based Learning for Single-Image Super-Resolution
نویسندگان
چکیده
This paper proposes a regression-based method for singleimage super-resolution. Kernel ridge regression (KRR) is used to estimate the high-frequency details of the underlying high-resolution image. A sparse solution of KRR is found by combining the ideas of kernel matching pursuit and gradient descent, which allows time-complexity to be kept to a moderate level. To resolve the problem of ringing artifacts occurring due to the regularization effect, the regression results are postprocessed using a prior model of a generic image class. Experimental results demonstrate the effectiveness of the proposed method.
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تاریخ انتشار 2008