In this paper we present an innovativeinterpolator which performs high quality N \Theta interpolation on both synthetic and real world images. Its structure, which is based on a rational operator, provides edge sensitive data interpolation, so that sharp and artifact free images are obtained. 1

. The Absolute Minimal Lipschitz Extension model (AMLE) is singled out and studied in more detail. We show experiments suggesting a possible application, the restoration of images with poor dynamic range. We also analyse the problem of unsmooth interpolation and show how it permits a subsidiary variational

Common image interpolation methods assume that the underlying signal is continuous and may require that it possess one or more continuous derivatives. These assumptions are not generally true of natural images, most of which have instantaneous luminance transitions at the boundaries between objects

We discuss possible algorithms for interpolating data given in a set of curves and/or points in the plane. We propose a set of basic assumptions to be satisfied by the interpolation algorithms which lead to a set of models in terms of possibly degenerate elliptic partial differential equations

Abstract- image interpolation is very interesting areaof research in image processing which consists of various processing tasks such as compression, restoration, denoising, enhancement. Reconstruction quality of any image interpolation algorithm depends on it’s ability to adapt changing pixel

Abstract — Image interpolation algorithms try to fit a function to a matrix of samples in a “natural-looking ” way. This paper presents edge inference, an algorthm that does this by mixing neural network regression with standard image interpolation techniques. Results on gray level images

Abstract—This chapter presents a survey of interpolation and resampling techniques in the context of exact, separable interpolation of regularly sampled data. In this context, the traditional view of interpolation is to represent an arbitrary continuous function as a discrete sum of weighted

this concept to its logical extreme by using interpolated images to portray three-dimensional scenes. The special-effects technique of morphing, which combines interpolation of texture maps and their shape, is applied to computing arbitrary intermediate frames from an array of prestored images. If the images

We discuss the problem of interpolating visually acceptable images at a higher resolution. We first present the interpolation problem and why linear interpolation filters are inadequate for image data. To represent the major mathematical approaches to image processing, we discuss and evaluate five

, a texture enhancement method is incorporated as a post-process of PDE-based interpolation methods. The post-process turns out to make the resulting algorithm an interpolator. It has been numerically verified that the resulting algorithm, called the PDE-based image interpolator (PII), restores sharp