双3次系数的简单计算（英文没验证）.pdfVIP

EN9821 Design Assignment Table of Contents Introduction 2 Image Zooming Methods 2 Nearest Neighbour Interpolation 2 Bilinear Interpolation 3 Cubic Convolution Interpolation 3 Comparison of Image Zooming Methods 5 Computational Complexity/Computation Time 6 Accuracy 6 Matlab’s Implementation of Bicubic Interpolation 6 Conclusion 7 References 8 Appendix I: Interpolation Source Code 9 Bax Smith - 1 - EN9821 Design Assignment Introduction Interpolation is the process of estimating the values of a continuous function from discrete samples. Image processing applications of interpolation include image magnification or reduction, subpixel image registration, to correct spatial distortions, and image decompression, as well as others. Of the many image interpolation techniques available, nearest neighbour, bilinear and cubic convolution are the most common, and will be talked about here. Sinc Interpolation provides a perfect reconstruction of a continuous function, provided that the data was obtained by uniform sampling at or above the Nyquist rate. Sinc Interpolation does not give good results within an image processing environment, since image data is generally acquired at a much lower sampling rate. The mapping between the unknown high-resolution image and the low-resolution image is not invertible, and thus a unique solution to the inverse problem cannot be computed. One of the essential aspects of interpolation is efficiency since the amount of data associated with digital images is large. Image Zooming Methods The general form for an interpolation function is: g(x) = Σ c u( distance ) Equation 1 k k k where g() is the interpolation funct