Super Resolution With Probabilistic Motion Estimation

Matan Protter, Michael Elad

Abstract: Super-resolution reconstruction (SRR) has long been relying on very accurate motion estimation between the frames for a successful process. However, recent works propose SRR that bypasses the need for an explicit motion estimation [11], [15]. In this correspondence, we present a new framework that ultimately leads to the same algorithm as in our prior work [11]. The contribution of this paper is two-fold. First, the suggested approach is much simpler and more intuitive, relying on the classic SRR formulation, and using a probabilistic and crude motion estimation. Second, the new approach offers various extensions not covered in our previous work, such as more general re-sampling tasks (e.g., de-interlacing).