Arbitrary factor image interpolation by convolution kernel constrained 2-D autoregressive modeling

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.