Author(s)

Peter Amoako-Yirenkyi^1,2, Justice Kwame Appati¹, Isaac Kwame Dontwi^1,2

¹Department of Mathematics, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
²National Institute of Mathematical Sciences, Kumasi, Ghana.

We present the analysis of three independent and most widely used image smoothing techniques on a new fractional based convolution edge detector originally constructed by same authors for image edge analysis. The implementation was done using only Gaussian function as its smoothing function based on predefined assumptions and therefore did not scale well for some types of edges and noise. The experiments conducted on this mask using known images with realistic geometry suggested the need for image smoothing adaptation to obtain a more optimal performance. In this paper, we use the structural similarity index measure and show that the adaptation technique for choosing smoothing function has significant advantages over a single function implementation. The new adaptive fractional based convolution mask can smoothly find edges of various types in detail quite significantly. The method can now trap both local discontinuities in intensity and its derivatives as well as locating Dirac edges.

Cubic B-Spline, Edge Detection, Fractional Edge, Gaussian Filter, Image Smoothing, Median Filter, Structural Similarity Index Measure

Amoako-Yirenkyi, P. , Appati, J. and Dontwi, I. (2016) Performance Analysis of Image Smoothing Techniques on a New Fractional Convolution Mask for Image Edge Detection. Open Journal of Applied Sciences, 6, 478-488. doi: 10.4236/ojapps.2016.67048.