Description: 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
To Search:
File list (Check if you may need any files):
859398.pdf
Download