Support vector regression has been proposed in a number of image processing tasks including blind

image deconvolution, image denoising and single frame super-resolution. As for other machine learning

methods, the training is slow. In this paper, we attempt to address this issue by reducing the feature

dimensionality through Principal Component Analysis (PCA). Our single frame supper-resolution

experiments show that PCA successfully