Deinterlacing Using Variational Methods

We present a variational framework for deinterlacing that was originally used for inpainting and subsequently redeveloped for deinterlacing. From the framework, we derive a motion adaptive (MA) deinterlacer and a motion compensated (MC) deinterlacer and test them together with a selection of known deinterlacers. To illustrate the need for MC deinterlacing, the problem of details in motion (DIM) is introduced. It cannot be solved by MA deinterlacers or any simpler deinterlacers but only by MC deinterlacers. The major problem in MC deinterlacing is computing reliable optical flow [motion estimation (ME)] in interlaced video. We discuss a number of strategies for computing optical flows on interlaced video hoping to shed some light on this problem. We produce results on challenging real world video data with our variational MC deinterlacer that in most cases are indistinguishable from the ground truth.