Kriging-Weighted Laplacian Kernels for Grayscale Image Sharpening

Sharpening filters are used to highlight fine image details, including object edges. However, sharpening very specific different types of images as they may create undesired edge effects, over-highlight or emphasize noise. Laplacian, Laplacian Gaussian, high-boost, unsharp masking filters, and their extended algorithms among most widely spatial filters. This paper introduces a method that integrates anisotropic averaging with the kernels for grayscale sharpening. The proposed methodology is based on concept kriging computation in geostatistics determining optimal interpolation weights domain. convolution then carried out Experimental results suggest certain advantages linear model over masking, diffusion methods terms balance sharpness natural visualization. Another advantage it does not require any input statistical parameters.