NDLI: Fast single-image super-resolution with filter selection

Content Provider	IEEE Xplore Digital Library
Author	Salvador, J. Perez-Pellitero, E. Kochale, A.
Copyright Year	2013
Abstract	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.
Starting Page	640
Ending Page	644
File Size	2452922
Page Count	5
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738132
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image resolution Image reconstruction Kernel Signal resolution Training Bandwidth Interpolation Parallel algorithms Super-resolution Raised cosine Cross-scale self-similarity
Content Type	Text
Resource Type	Article

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