Region-Based Sub-pixel Motion Estimation from Noisy, Blurred, and Down-Sampled Sequences

Abstract

Motion estimation is one of the most important steps in super-resolution algorithms for a video sequence, which require estimating motion from a noisy, blurred, and down-sampled sequence; therefore the motion estimation has to be robust. In this paper, we propose a robust sub-pixel motion estimation algorithm based on region matching. Non-rectangular regions are first extracted by using a so-called watershed transform. For each region, the best matching region in a previous frame is found to get the integer-pixel motion vector. Then in order to refine the accuracy of the estimated motion vector, we search the eight sub-pixels around the estimated motion vector for a sub-pixel motion vector. Performance of our proposed algorithm is compared with the well known full search with both integer-pixel and sup-pixel accuracy. Also it is compared with the integer-pixel region matching algorithm for several noisy video sequences with various noise variances. The results show that our proposed algorithm is the most suitable for noisy, blurred, and down-sampled sequences among these conventional algorithms.