An adaptive cubic convolution image interpolation approach

Abstract

Key's (bicubic) image interpolation is one of the well-known, state of the art image interpolation algorithms. In this paper, we introduce an adaptive version of Key's interpolation algorithm. The suggested adaptive algorithm is based on minimization of the squared estimation error at each pixel in the interpolated image. Thus, the overall mean square error (MSE) in the entire image is minimized. The suggested algorithm takes into consideration the low resolution (LR) image degradation model. The Key's formula comprises two controling parameters. A study of the effect of optimizing this formula with respect to the separated or combined parameters is presented. The optimum values of the parameters are estimated iteratively at each pixel. The performance of the suggested approach is tested in the presence of noise with different levels and is compared to the traditional warped distance interpolation technique. A comparison of the suggested algorithm performance with other different interpolation techniques used in the commercial ACDSee Software is presented. The computational complexity of the suggested algorithm is also studied in the paper. The obtained results ensure the superiority of the suggested adaptive interpolation algorithm as compared to the traditional algorithms from both of the MSE and edge preservation points of view. As the results imply, the computation time of the suggested algorithm is moderate.