DocumentCode :
3063130
Title :
Model-based least squares optimal interpolation
Author :
Gilman, A. ; Bailey, D.G. ; Marsland, S.
Author_Institution :
Sch. of Eng. & Adv. Technol., Massey Univ., Palmerston North, New Zealand
fYear :
2009
fDate :
23-25 Nov. 2009
Firstpage :
124
Lastpage :
129
Abstract :
The traditional approach to image interpolation is by synthesis using basis functions because of its computational simplicity and experience-proven quality of the result. We offer an alternative approach to designing the basis (interpolation kernels), using least-squares optimisation and image models that encompass the prior knowledge. In this paper we consider and derive a finite-support interpolation kernel based on a step-edge model and show that this results in a piece-wise cubic polynomial similar to Keys´ cubic convolution. We offer an experimental comparison of the proposed kernel to a number of common methods and show that it performs similar to, or better than, the existing methods with similar extent of spatial support.
Keywords :
image processing; interpolation; least squares approximations; Keys cubic convolution; finite-support interpolation kernel; image interpolation; least-squares optimisation; model-based least squares optimal interpolation; piece-wise cubic polynomial; step-edge model; Computer vision; Convolution; Design optimization; Image reconstruction; Image sampling; Interpolation; Kernel; Least squares methods; Mean square error methods; Polynomials; interpolation; least-squares optimisation; resampling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image and Vision Computing New Zealand, 2009. IVCNZ '09. 24th International Conference
Conference_Location :
Wellington
ISSN :
2151-2205
Print_ISBN :
978-1-4244-4697-1
Electronic_ISBN :
2151-2205
Type :
conf
DOI :
10.1109/IVCNZ.2009.5378424
Filename :
5378424
Link To Document :
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