Paper Info
Title
Least-squares Optimal Interpolation for Fast Image Super-resolution
Abstract
Image super-resolution is generally regarded as consisting of three steps – image registration, fusion, and deblurring. This paper presents a novel technique for resampling a non-uniformly sampled image onto a uniform grid that can be used for fusion of translated input images. The proposed method can be very fast, as it can be implemented as a finite impulse response filter of low order (10th order results in good performance). The technique is based on optimising the resampling filter coefficients using a simple image model in a least squares fashion. The method is tested experimentally on a range of images and shown to have similar results to that of a least-squares optimal filter. Further experimental comparisons are made against a number of methods commonly used in image super-resolution that show that the proposed method is superior to these.
Year
DOI
Venue
2010
10.1109/DELTA.2010.59
Ho Chi Minh City
Keywords
Field
DocType
novel technique,image super-resolution,image registration,least-squares optimal interpolation,fast image,finite impulse response filter,input image,least-squares optimal filter,low order,resampling filter,simple image model,super resolution,image reconstruction,finite impulse response,polynomials,pixel,resampling,image resolution,gray scale,image fusion,least squares,interpolation,least square,mathematical model
Least squares,Computer vision,Image fusion,Deblurring,Computer science,Artificial intelligence,Pixel,Finite impulse response,Image resolution,Image registration,Filter design
Conference
ISBN
Citations 
PageRank 
978-1-4244-6026-7
2
0.38
References 
Authors
7
3
Name
Order
Citations
PageRank
Andrew Gilman173.54
Donald Bailey21010.04
Stephen Marsland3146.33