Example-Based Learning for Single-Image Super-Resolution

Abstract. 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.

• Type: Conference
• Year: 2008
• Where: DAGM
• Authors: Kwang In Kim, Younghee Kwon
• Keywords: DAGM 2008, Generic Image Class, Image Processing, Kernel Matching Pursuit, Kernel Ridge Regression