Updated September 2016
Y. Romano, J. Isidoro, and P. Milanfar,
"RAISR: Rapid and Accurate Image Super-Resolution
With sufficient training data (corresponding pairs of low and high resolution images) we can learn sets of filters (i.e. a mapping) that when applied to given image that is not in the training set, will produce a higher resolution version of it, where the learning is low complexity. In our approach, the run-time is more than one to two orders of magnitude faster than the best competing methods currently available, while producing results comparable or better than state-of-the-art.
M. Elad and P. Milanfar,
"Style Transfer via Texture Synthesis
We propose a novel style-transfer algorithm that extends the texture-synthesis work of Kwatra et. al. (2005), while aiming to get stylized images that get closer in quality to ones produced by Convolutional Neural Networks. The results obtained are visually pleasing and diverse, shown to be competitive with the recent CNN style-transfer algorithms. The proposed algorithm is fast and flexible, being able to process any pair of content + style images .