Adaptive interpolation of images with application to interlaced-to-progressive conversion

We propose a novel method for image interpolation which adapts to the local characteristics of the image in order to facilitate perfectly smooth edges. Features are classified into three categories (constant, oriented, and irregular). For each class we use a different zooming method that interpolates this feature in a visually optimized manner. Furthermore, we employ a nonlinear image enhancement which extracts perceptually important details from the original image and uses these in order to improve the visual impression of the zoomed images. Our results compare favorably to standard lowpass interpolation algorithms like bilinear, diamond- filter, or B-spline interpolation. Edges and details are much sharper and aliasing effects are eliminated. In the frequency domain we can clearly see that our adaptive algorithm not only suppresses the undesired spectral components that are folded down in the upsampling process. It is also capable of replacing them with new estimates, which accounts for the increased image sharpness. One application of this interpolation method is spatial interlaced-to- progressive conversion. Here, it yields again more pleasing images than comparable algorithms.