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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. Experimental results have shown that image qualities are improved as compared to results from conventional kernels.