One of the most common tasks in image processing is to change the resolution of a picture. In this paper we
present a new nonlinear method for interpolating digital images, which is particulary effective in the rendition of
edges in natural and synthetic input. The algorithm is spatial variant and applies the warped distance (WaDi)
concept, generalizing the technique to a two dimensional problem, which requires a non-separable approach.
It consists of three separate stages. First of all the original image is analyzed to detect its local gradient
characteristics; then edge asymmetry is computed at each output pixel position according to the WaDi technique,
and it is compared to a reference sigmoidal edge; the local edge asymmetry straightforwardly determines the
warping factor which is applied to the bi-dimensional space of the image; eventually the actual interpolation is
performed applying a conventional interpolator such as the linear or bicubic ones. The resulting interpolation
method gives an output which does not present the usual blurring typical of images processed with linear
interpolators and at the same time preserves the regularity of resized edges avoiding jagging artifacts. Moreover,
the method adapts for zooming by a rational scaling factor. The paper is organized as follows. In the first
section we introduce the problem of zooming digital images; the second section describes the state of the art; we
continue describing the proposed method; then we propose a possible extension of the method to process color
images; we end showing some examples of images interpolated with our method, and comparing these results
with what can be obtained zooming the same input with other interpolators.
Fixed matrix displays require digital interpolation algorithms to adapt the input spatial format to the output matrix. Interpolation techniques usually employed for this purpose exploit linear kernels designed to preserve the spectral content (anti aliasing), but this generates smooth edges, which result in unpleasant text images where sharpness is essential. By contrast, interpolation kernels designed to preserve sharpness introduce geometrical distortions in the scaled text (e.g. nearest neighbor interpolation). This paper describes an interpolation algorithm which, compared to linear techniques, aims to increase the sharpness of interpolated text while preserving its geometrical regularity. The basic idea is to differentiate the processing for text and non-text pixels. Firstly, a binary text map is built. By using morphological constraints it is possible to form a similar text map in the output domain that preserves the general text regularity. Finally, output text pixel positions are used to control a nonlinear interpolator (based on the Warped Distance approach) that is able to generate both step and gradual luminance profiles, thus enabling the algorithm to locally change its behavior. A general sharpness control is provided as well, which permits to range from a two-level text (maximum sharpness) to a smoother output image (traditional linear interpolation).