Video Coding With Superimposed Motion-Compensated Signals: Applications to H.264 and Beyond (Kluwer International Series in Engineering and Computer Science, "760)
Vector-Valued Image Regularization with PDEs: A Common Framework for Different Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency
IEEE Transactions on Signal Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
IEEE Transactions on Image Processing
A motion-compensated spatio-temporal filter for image sequences with signal-dependent noise
IEEE Transactions on Circuits and Systems for Video Technology
Combined spatial and temporal domain wavelet shrinkage algorithm for video denoising
IEEE Transactions on Circuits and Systems for Video Technology
Wavelet-Domain Video Denoising Based on Reliability Measures
IEEE Transactions on Circuits and Systems for Video Technology
Video Denoising Based on Inter-frame Statistical Modeling of Wavelet Coefficients
IEEE Transactions on Circuits and Systems for Video Technology
An Improved Motion-Compensated 3-D LLMMSE Filter With Spatio–Temporal Adaptive Filtering Support
IEEE Transactions on Circuits and Systems for Video Technology
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Multi-Hypothesis motion compensated filter (MHMCF) utilizes a number of hypotheses (temporal predictions) to estimate the current pixel which is corrupted with noise. While showing remarkable denoising results, MHMCF is computationally intensive as full search is employed in the expectation of finding good temporal predictions in the presence of noise. In the frame of MHMCF, a fast denoising algorithm FMHMCF is proposed in this paper. With edge preserved low-pass prefiltering and noise-robust fast multihypothesis search, FMHMCF could find reliable hypotheses while checking very few search locations, so that the denoising process can be dramatically accelerated. Experimental results show that FMHMCF can be 10 to 14 times faster than MHMCF, while achieving the same or even better denoising performance with up to 1.93 dB PSNR (peak-signal-noise-ratio) improvement.