Abstract
We propose a learning-based, single-image super-resolution
reconstruction technique using the contourlet transform, which is
capable of capturing the smoothness along contours making use of
directional decompositions. The contourlet coefficients at finer
scales of the unknown high-resolution image are learned locally
from a set of high-resolution training images, the inverse
contourlet transform of which recovers the super-resolved image.
In effect, we learn the high-resolution representation of an
oriented edge primitive from the training data. Our experiments
show that the proposed approach outperforms standard interpolation
techniques as well as a standard (Cartesian) wavelet-based
learning both visually and in terms of the PSNR values, especially
for images with arbitrarily oriented edges.