文件名称:baker_simon_1999_3
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Support vector regression has been proposed in a number of image processing tasks including blind
image deconvolution, image denoising and single fr a me 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 fr a me supper-resolution
experiments show that PCA successfully
image deconvolution, image denoising and single fr a me 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 fr a me supper-resolution
experiments show that PCA successfully
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baker_simon_1999_3.pdf