Key points are not available for this paper at this time.
Image interpolation is the most basic requirement for many image processing task such as computer graphics, gaming, medical image processing, virtualization, camera surveillance and quality control. Image interpolation is a technique used in resizing images. To resize an image, every pixel in the new image must be mapped back to a location in the old image in order to calculate a value of new pixel. There are many algorithms available for determining new value of the pixel, most of which involve some form of interpolation among the nearest pixels in the old image. In this paper, we used Nearest-neighbor, Bilinear, Bicubic, Bicubic B-spline, Catmull-Rom, Mitchell-Netravali and Lanzcos of order three algorithms for image interpolation. Each algorithms generates varies artifact such as aliasing, blurring and moir. This paper presents, quality and computational time consideration of images while using these interpolation algorithms.
Parsania et al. (Sat,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: