DFT-domain space-variable approximation of uniformly sampled images from samples on irregular grids

We present a space-variable POCS-based (projection onto convex sets) method for the reconstruction of a regularly sampled image from its irregularly spaced samples. Such reconstruction is often needed in image processing and coding, for example in stereovision and motion compensation. The proposed approach applies two operators sequentially: bandwidth limitation and sample substitution. Our contribution is the space-variable implementation of bandwidth limitation operator, which has been postulated. The operator is realized in the DFT domain by using two or more masks, a measure of local density of irregular grid determines results from which mask are used. The technique is efficient computationally, also in terms of memory requirements. Experimental results demonstrate that indeed, the new technique is much better in terms of PSNR, convergence speed, and visual quality.