New learning based super resolution using contourlet transform
new learning based super-resolution reconstruction using contourlet transforms is proposed. contourlet transform provides high degree of directionality. It captures geometrical smoothness along multiple directions and learns the edges present in an image normal to the contour. For learning purpose, training set of low resolution (LR) and high resolution (HR) images, all captured using the same camera, are used. Here two and three level contourlet decomposition for LR images (test image and training image dataset) and HR training images respectively. The comparison of contourlet coeffcients of LR test image from the LR training set using minimum absolute difference (MAD) criterion to obtain the best match contourlet coeffcient. The finer details of test image are learned from the high resolution contourlet coefficients of the training data set. The inverse contourlet transform gives super resolved image corresponding to the test image.
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