Fast single-image super-resolution with filter selection

This paper presents a new method for estimating a super-resolved version of an observed image by exploiting cross-scale self-similarity. We extend prior work on single-image super-resolution by introducing an adaptive selection of the best fitting upscaling and analysis filters for example learning. This selection is based on local error measurements obtained by using each filter with every image patch, and contrasts with the common approach of a constant metric in both dictionary-based and internal learning super-resolution. The proposed method is suitable for interactive applications, offering low computational load and a parallelizable design that allows straight-forward GPU implementations. Experimental results also show how our method generalizes better to different datasets than dictionary-based super-resolution and comparably to internal learning with adaptive post-processing.