access icon free Adaptive image interpolation technique based on cubic trigonometric B-spline representation

A cubic trigonometric B-spline representation with two parameters is constructed in this work. A soft computing technique, genetic algorithm, is used to find the optimal value of the parameters in the description of B-spline so that the sum square error is minimised. The newly constructed B-spline is then utilised to interpolate two-dimensional digital images. The image quality metrics peak signal-to-noise ratio, structure SIMilarity index, multi-scale structure SIMilarity index and feature SIMilarity index are used to investigate the quality of interpolated digital images. Comparison with already existing image interpolation schemes leads to the conclusion that the proposed image interpolation technique is found to be a valuable scheme for the problems related to digital image interpolation.

Inspec keywords: image representation; genetic algorithms; interpolation; splines (mathematics)

Other keywords: feature similarity index; genetic algorithm; cubic trigonometric B-spline representation; image quality metrics peak signal-to-noise ratio; adaptive image interpolation technique; sum square error minimisation; soft computing technique; two-dimensional digital image interpolation; multiscale structure similarity index

Subjects: Interpolation and function approximation (numerical analysis); Computer vision and image processing techniques; Optimisation techniques; Interpolation and function approximation (numerical analysis); Optical, image and video signal processing; Optimisation techniques

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2016.0393
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