Design of Unsharp Masking Filter Kernel and Gain using Particle Swarm Optimization

Design of Unsharp Masking Filter Kernel and Gain using Particle Swarm Optimization

The unsharp masking filter is an efficient and
effective algorithm frequently applied in image contrast enhancement
applications. The principle is based on sharpening object
edges by appending a scaled high-pass version of the image
to the original. The quality of the processed image is largely
dependent on the characteristics of the high-pass signal and
the scaling factor. Thus, optimal choices of the high-pass kernel
and scaling are needed. In this work, a symmetrical kernel is
employed and optimized to extract the edge together with an
optimal scale factor to enhance a color image. In particular,
the particle swarm optimization algorithm is used to obtain
the proper filter kernel settings and the gain with regard to
maximizing the information content and minimizing the number
of over-ranged pixels. The proposed method is tested with 200
real-world images and the filter performance is assessed by
referring to measures in colorfulness, average saturation and
entropy.