Video denoising using vector estimation of wavelet coefficients

Abstract

Wavelet-based image denoising can be extended to a video by applying it to each video frame independently. The denoising performance can be improved by exploiting inter-frame correlations, for example, using appropriate temporal filtering. However, fixed temporal filters might not perform sufficiently well due to their inability to cope with the variability of inter-frame correlations across the video. While many adaptive temporal filtering approaches for denoising in spatial domain have been proposed, they do not straightforwardly extend to wavelet-based denoising. We propose a vector extension of popular hidden Markov tree modeling that flexibly exploits the color and frame dependency of wavelet coefficients. Experimental results confirm that the vector estimator of wavelet coefficients yields denoising performance superior to that of existing solutions, both in CPSNR and visual quality sense.