Image Enhancement and Reduction of Computational Complexity Using Various Interpolation Techniques
Main Article Content
This paper contains Image interpolation techniques which are frequently requisite from clinical image to satellite image. Since various ideal interpolation functions are defined in spatial domain with unconstrained functions, interpolation kernels of finite size should introduce necessarily. B - Spline Interpolation Methods and Quadratic B - spline functions are such kernels and truncate ideal interpolator and produces phase distortion in the output and re-sampled images. This decreases the computing time and increases the quality of re-sampled images. Further these re-sampled images are enhanced by cubic, sinc, linear, B-spline, ideal, adaptive and quadratic interpolation methods. Cubic Spline interpolations with minimal distortion of the sub-sampled images are enhanced and reduced the computational complexity. The quadratic spline interpolator resulting from a quadratic B-spline weighted function is decomposed by manipulating the weighted sum of quadratic B-spline weight functions. Subsequently by using spatial analysis, the images are assessed its chromatic eminence value, error identification and reduction of error, reduction of computational complexity, and run time measurement.