PROJECT TITLE :

Analysis of adaptive filter and ICA for noise Cancellation from a video frame - 2016

ABSTRACT:

Noise cancellation algorithms have been frequently applied in several fields including image/video processing. Adaptive noise cancellation algorithms exploit the correlation property of noise and take away the noise from the input signal more effectively than non-adaptive algorithms. During this paper completely different noise cancellation techniques are applied to de-noise a video frame. Three totally different variants of gradient based mostly adaptive filtering algorithms and freelance element analysis (ICA) procedure are implemented and compared on the premise of signal to noise ratio (SNR) and computational time. The common algorithms employed in adaptive filters are least mean square (LMS), normalized least means sq. (NLMS), and recursive least mean square (RLS). The simulation results demonstrates that NLMS algorithm is computationally economical however cannot handle impulsive noise whereas LMS and RLS will perform higher for long length noise signals. The comparative analysis of adaptive filtering algorithms and ICA shows that ICA can perform higher then all 3 iterative gradient primarily based algorithms as a result of of its non-iterative nature. For testing and simulations, three variants of white Gaussian noise (WGN) are used to corrupt the video frame.


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