Feature dimensionality reduction for example-based image super-resolution

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 reduces the feature dimensionality without degrading the performance of SVR when the training images and testing images share similarities (i.e. belong to the same category). In fact, in some cases the performance in terms of Peak Signal-to-Noise Ratio (PSNR), is even better.