Conventional interpolation algorithms of image

such as zero-order or nearest neighbor

bilinear

cubic spline interpolation

can be analyzed in two steps: (i)upsampling by zero filling

and(ii) low-pass filtering. But ideal low-pass filtering of interpolation can not be practically achieved

which results in high-frequency artifacts in the interpolated image. On the other hand

due to the low-pass filters fixed

these algorithms fail to utilize the information of the image itself. In order to improve the quality of the interpolated image and enhance the resolution of it

a novel regularized image interpolation algorithm based on genetic algorithm is proposed in this paper. This genetic algorithm has real-valued coding

the induced mutation operator and the fitness function for evaluation containing the term of some subjective quality measures

so the convergence of the genetic searching in the solution space is very fast. Finally

we analyze how to choose the regularization parameter in the fitness function

and compare the results with that of iterative regularized interpolation algorithm. The experiments demonstrate that the proposed algorithm is practical and applicable.