Date Presented:

Abstract:

Motion estimation is a common ingredient in many state-of- the-art video processing algorithms, serving as an effective way to capture the spatial-temporal correlation in video signals. However, the robustness of motion estimation often suffers from problems such as ambiguities of motion trajectory (i.e. the aperture problem) and illumination variances. In this paper, we explore a new framework for video processing based on the recently proposed surfacelet transform. Instead of containing an explicit motion estimation step, the surfacelet transform provides a motion-selective subband decomposition for video signals. We demonstrate the potential of this new technique in a video denoising application.