VIDEO DENOISING USING 3-D HYBRID WAVELETS AND DIRECTIONAL FILTER BANKS

We propose a new family of nonredundant 3-D directional transforms that are useful for video signals. In our construction, taking into account the correlation amongst frames of video, we first decorrelate the temporal data using a stage of 1-D wavelet transform and then we employ the efficient 2-D Hybrid Wavelets and Directional filter banks (HWD) transform family to the resulting spatial data where we achieve 3-D HWD transform family. We construct translation-invariant version of the proposed family and show its efficiency in video denoising compared to other wavelet-based methods through several experiments.