A Total Variation Motion Adaptive Deinterlacing Scheme

We propose a new way of deinterlacing using a total variation scheme. Starting by the Bayesian inference formulation of total variation we do MAP by rewriting the problem into PDEs that can be solved by simple numerical schemes. Normally deinterlacing schemes are developed ad hoc with online hardware implementation directly at eye, sometimes with some frequency analysis as only theoretical base. Our belief is that mathematically well based image models are needed to do optimal deinterlacing and by our work presented here, we hope to prove it. Comparing the output of our scheme with those of ten known deinterlacing schemes shows very promising results.