This paper proposes a framework for single-image super-resolution and JPEG artifact removal. The underlying idea is to learn a map from input low-quality images (suitably preprocessed low-resolution or JPEG encoded images) to target high-quality images based on example pairs of input and output images. To
retain the complexity of the resulting learning problem at a moderate level, a patch-based approach is taken such that kernel ridge regression (KRR) scans the input image with a small window (patch) and produces a patchvalued output for each output pixel location. These constitute a set of candidate images each of which reflects different local information. An image output is then obtained as a convex combination of candidates for each pixel based on estimated confidences of candidates. To reduce the time complexity of training and testing for
KRR, a sparse solution is found by combining the ideas of kernel matching pursuit and gradient descent. As a
regularized solution, KRR leads to a better generalization than simply storing the examples as it has been done
in existing example-based super-resolution algorithms and results in much less noisy images. However, this may
introduce blurring and ringing artifacts around major edges as sharp changes are penalized severely. A prior model
of a generic image class which takes into account the discontinuity property of images is adopted to resolve this
problem. Comparison with existing super-resolution and JPEG artifact removal methods shows the effectiveness
of the proposed method. Furthermore, the proposed method is generic in that it has the potential to be applied to
many other image enhancement applications.