Konstantinos K. Delibasis

Nicholas Vaiopoulos, Alexander Vavoulas, Harilaos G. Sandalidis et al.

This letter derives closed-form expressions for the probability density function of the distance between two nodes located in heterogeneous concentric geometries, namely a disk or sphere and a surrounding annulus or spherical shell. Two scenarios are considered: (i) both nodes are independently distributed in different regions, disk or sphere and annulus or shell, and (ii) one node is static in the outer region while the other follows the stationary distribution of the random waypoint model in the inner region. The resulting expressions provide a tractable analytical tool for performance evaluation in concentric wireless regions.

Konstantinos K. Delibasis, Iro Oikonomou, Aristides I. Kechriniotis et al.

A number of basic image processing tasks, such as any geometric transformation require interpolation at subpixel image values. In this work we utilize the multidimensional coordinate Hermite spline interpolation defined on non-equal spaced, rectilinear grids and apply it to a very common image processing task, image zooming. Since Hermite interpolation utilizes function values, as well as partial derivative values, it is natural to apply it to image processing tasks as a special case of equi-spaced grid, using numerical approximations of the image partial derivatives at each pixel. Furthermore, the task of image interpolation requires the calculation of image values at positions with nono-zero fractional part. Thus, any spline interpolation can be written as convolution with an appropriate kernel. In this context we generate the Hermite kernels according to the derived $n-$dimensional interpolant of Theorem 2 in [1]. We show that despite the increased complexity of the interpolant, once the kernels are constructed, the Hermite spline interpolation can be applied to images as efficiently as any other less complicated method. Finally, we perform illustrative numerical examples to showcase the applicability and high accuracy of the proposed Hermite kernels for image zooming, compared to other interpolation methods, both traditional convolution-based, as well as employing deep learning, in terms of PSNR, as well as SSIM error metrics. The proposed Hermite spline kernels outperform all other methods in the majority of the test images, in experiments using many cascaded repetitions of the zoom operation. Interesting conclusions can be drawn considering all methods under comparison.