A new directional image interpolation based on Laplacian operator

The interpolation task plays a key role in the reconstruction of high-resolution image quality in superresolution algorithms. In fact, the foremost shortcoming encountered in the classical interpolation algorithms, is that they often work poorly when used to eliminate blur and noise in the input image. In this sense, the aim of this work is to develop an interpolation scheme for the purpose of reducing these artifacts in the input image, and consequently preserve the sharpness of the edges. The proposed method is based on the image interpolation, and it is started by the estimation of the edges directions using the Laplacian operator, and then interpolated the missing pixels from the strong edge by using the cubic convolution interpolation. We begin from a gray high-resolution image that is down-sampled by a factor of two, to obtain the low-resolution image, and then reconstructed using the proposed interpolation algorithm. The method is implemented and tested using several gray images and compared to other interpolation methods. Simulation results show the performance of the proposed method over the other methods of image interpolation in both PSNR, and two perceptual quality metrics SSIM, FSIM in addition to visual quality of the reconstructed images results. Key–Words: Image interpolation, Cubic convolution, Laplacian operator, Edge detection