  Single image super resolution (SISR) is an ill-posed problem aiming at
estimating a plausible high resolution (HR) image from a single low resolution
(LR) image. Current state-of-the-art SISR methods are patch-based. They use
either external data or internal self-similarity to learn a prior for a HR
image. External data based methods utilize large number of patches from the
training data, while self-similarity based approaches leverage one or more
similar patches from the input image. In this paper we propose a
self-similarity based approach that is able to use large groups of similar
patches extracted from the input image to solve the SISR problem. We introduce
a novel prior leading to collaborative filtering of patch groups in 1D
similarity domain and couple it with an iterative back-projection framework.
The performance of the proposed algorithm is evaluated on a number of SISR
benchmark datasets. Without using any external data, the proposed approach
outperforms the current non-CNN based methods on the tested datasets for
various scaling factors. On certain datasets, the gain is over 1 dB, when
compared to the recent method A+. For high sampling rate (x4) the proposed
method performs similarly to very recent state-of-the-art deep convolutional
network based approaches.
