Tuning Shape Parameter of Radial Basis Functions in Zooming Images using Genetic Algorithm

Image zooming is one of the current issues of image processing where maintaining the quality and structure of the zoomed image is important. To zoom an image, it is necessary that the extra pixels be placed in the data of the image. Adding the data to the image must be consistent with the texture in the image and not to create artificial blocks. In this study, the required pixels are estimated by using radial basis functions and calculating the shape parameter c with genetic algorithm. Then, all the estimated pixels are revised based on the sub-algorithm of edge correction. The proposed method is a non-linear method that preserves the edges and minimizes the blur and block artifacts of the zoomed image. The proposed method is evaluated on several images to calculate the optimum shape parameter of radial basis functions. Numerical results are presented by using PSNR and SSIM fidelity measures on different images and are compared to some other methods. The average PSNR of the original image and image zooming is 33.16 which shows that image zooming by factor 2 is similar to the original image, emphasizing that the proposed method has an efficient performance.