NDLI: Fast and accurate image upscaling with super-resolution forests

Content Provider	IEEE Xplore Digital Library
Author	Schulter, S. Leistner, C. Bischof, H.
Copyright Year	2015
Description	Author affiliation: Microsoft Photogrammetry, Austria (Leistner, C.) \|\| Inst. for Comput. Graphics & Vision, Graz Univ. of Technol., Graz, Austria (Schulter, S.; Bischof, H.)
Abstract	The aim of single image super-resolution is to reconstruct a high-resolution image from a single low-resolution input. Although the task is ill-posed it can be seen as finding a non-linear mapping from a low to high-dimensional space. Recent methods that rely on both neighborhood embedding and sparse-coding have led to tremendous quality improvements. Yet, many of the previous approaches are hard to apply in practice because they are either too slow or demand tedious parameter tweaks. In this paper, we propose to directly map from low to high-resolution patches using random forests. We show the close relation of previous work on single image super-resolution to locally linear regression and demonstrate how random forests nicely fit into this framework. During training the trees, we optimize a novel and effective regularized objective that not only operates on the output space but also on the input space, which especially suits the regression task. During inference, our method comprises the same well-known computational efficiency that has made random forests popular for many computer vision problems. In the experimental part, we demonstrate on standard benchmarks for single image super-resolution that our approach yields highly accurate state-of-the-art results, while being fast in both training and evaluation.
Starting Page	3791
Ending Page	3799
File Size	687397
Page Count	9
File Format	PDF
ISSN	10636919
e-ISBN	9781467369640
DOI	10.1109/CVPR.2015.7299003
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image resolution Dictionaries Training Vegetation Encoding Yttrium Computer vision
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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