A Novel Regularized Image Interpolation Algorithm Based on Genetic Algorithm
Zhijun Liu, Cai Chao, Peng Xiao-ming, Chengping Zhou, Ding Mingyue · 2004
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.