American Journal of Engineering and Applied Sciences

Digital High-Pass Filters with Milder High-Pass Effect on Digital Images

Issa A. Al-Shakhrah

DOI : 10.3844/ajeassp.2015.360.370

American Journal of Engineering and Applied Sciences

Volume 8, Issue 3

Pages 360-370


Image filtering consists of modifying the original image by logically “reimaging” it with a mathematical imaging device in which spatial response can be controlled by the user. Image filtering is performed by a mathematical operation called convolution, which is simply the successive replacement of each point in the original image by a new value produced by a weighted combination of the original point and its surrounding neighbour points. Filtering generally requires definition of a filtering kernel or small matrix; often a few filtering kernels are predefined in imaging computer systems. The filtering kernel is generally square with a matrix size of 3×3 pixels, 5×5 pixels or 7×7 pixels. We consider the use of two-dimensional, second-order derivatives for image enhancement. The approach basically consists of defining a discrete formulation of the second-order derivative and then constructing a filter mask based on that formulation. Ten spatial high-pass filters (masks) are developed, then implemented and tested in our laboratory by using programs that were written in Borland c++ and visual Fortran. The results of the application of the developped Laplacian and Laplacian high-pass digital filters (masks) on digital images (either edge detection, sharpening of high frequency regions (fine details) accentuation), comparing between the effect of different dimensions filters 3×3 and 5×5 and milder high pass effect are presented and demonstrated. As the size of the filter (mask) gets larger and/or the weight of the center pixel of the kernel gets higher, the sharpenning effect becomes more and more. Second-order derivatives have a strong response to fine detail, such as thin lines and isolated points.


© 2015 Issa A. Al-Shakhrah. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.