Paper Info
Title
On Double-Resolution Imaging and Discrete Tomography.
Abstract
Superresolution imaging aims at improving the resolution of an image by enhancing it with other images or data that might have been acquired using different imaging techniques or modalities. In this paper we consider the task of doubling, in each dimension, the resolution of grayscale images of binary objects by fusion with double-resolution tomographic data that have been acquired from two viewing angles. We show that this task is polynomial-time solvable if the gray levels have been reliably determined. The problem becomes NP-hard if the gray levels of some pixels come with an error of +/- 1 or larger. The NP-hardness persists for any larger resolution enhancement factor. This means that noise does not only affect the quality of a reconstructed image but, less expectedly, also the algorithmic tractability of the inverse problem itself.
Year
DOI
Venue
2018
10.1137/17M1115629
SIAM JOURNAL ON DISCRETE MATHEMATICS
Keywords
Field
DocType
discrete mathematics,combinatorics,discrete tomography,superresolution,polynomial-time,algorithms,computational complexity
Computer vision,Discrete mathematics,Discrete tomography,Pixel,Inverse problem,Artificial intelligence,Superresolution,Mathematics,Grayscale,Computational complexity theory,Binary number
Journal
Volume
Issue
ISSN
32
2
0895-4801
Citations 
PageRank 
References 
0
0.34
12
Authors
2
Name
Order
Citations
PageRank
Andreas Alpers1475.47
Peter Gritzmann241246.93