Edge-and-corner preserving regularization for image interpolation and reconstruction
Image and Vision Computing
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Spatial interpolation is an important technique that is often used to perform an image zoom or to simply recover an original image from its downsampled version. The rapid advancements in hardware, both in acquisition and display devices, has made it possible to process high resolution digital colour images. However, the multichannel nature of colour images demands sophisticated signal processing algorithms that take into account the existing interchannel correlations when performing image expansion. Many conventional linear approaches exist. Nevertheless, these produce artifacts in the form of blockiness, jagged lines, and blurring in the interpolated image. In addition to this, these methods perform independently in each colour plane, thereby neglecting the colour component correlation. A set of nonlinear vector FIR-median hybrid (VFMH) filters are applied to the interpolation problem. These schemes are based on the class of vector order statistic filters which have desirable properties, such as the preservation of edges and image details, and the preservation of interchannel correlations. Colour images are interpolated from their downsampled versions and all of the techniques are compared, both, quantitatively as well as qualitatively. Experimental results indicate that VFMH filters produce better quantitative, and visually pleasing results than linear techniques