Paper Info
Title
Arbitrary factor image interpolation by convolution kernel constrained 2-D autoregressive modeling
Abstract
Among existing interpolation methods, convolution-based methods are able to perform arbitrary factor interpolation but the results are usually blurry or jaggy, adaptive interpolation methods usually can reduce the blurry and jaggy artifacts but cannot handle arbitrary factor interpolation. In this paper we propose an arbitrary factor adaptive interpolation algorithm by combining 2-D piecewise autoregressive (PAR) modeling and convolution kernel constraint. PAR model ensures local geometries are well preserved thus the resultant image is not blurry or jaggy. Convolution kernel constraint ensures the recovered high resolution image consistent with the low resolution image, and also provides the flexibility to handle arbitrary interpolation factor. Experiment results show that our algorithm achieves state-of-the-art performance for any interpolation factor.
Year
DOI
Venue
2013
10.1109/ICIP.2013.6738206
ICIP
Keywords
Field
DocType
par modeling,interpolation,arbitrary factor,image resolution,arbitrary interpolation factor,arbitrary factor interpolation,convolution kernel constrained 2d autoregressive modeling,convolution,2d piecewise autoregressive modeling,adaptive interpolation methods,autoregressive processes,high resolution image recovery,low resolution image,convolution kernel constraint,arbitrary factor image interpolation,autoregressive model,arbitrary factor adaptive interpolation algorithm,local geometries
Nearest-neighbor interpolation,Computer vision,Spline interpolation,Computer science,Interpolation,Bicubic interpolation,Stairstep interpolation,Artificial intelligence,Linear interpolation,Trilinear interpolation,Bilinear interpolation
Conference
ISSN
Citations 
PageRank 
1522-4880
2
0.39
References 
Authors
6
6
Name
Order
Citations
PageRank
Ketan Tang110612.98
Oscar C. Au21592176.54
Yuanfang Guo39518.21
Jiahao Pang414912.42
Jiali Li5499.29
Lu Fang634355.27