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1. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
ANALYSIS OF ADAPTIVE FILTER AND ICA FOR NOISECANCELLATION FROM A
VIDEO FRAME
ABSTRACT
Noise cancellation algorithms have been frequently applied in many fields
including image/video processing. Adaptive noise cancellation algorithms
exploit the correlation property of noise and remove the noise from the input
signal more effectively than non-adaptive algorithms. In this paper different
noise cancellation techniques are applied tode-noise a video frame. Three
different variants of gradient based adaptive filtering algorithms and
independent component analysis (ICA) procedure are implemented and
compared on the basis of signal to noise ratio (SNR) and computational time.
The common algorithms used in adaptive filters are least mean square (LMS),
normalized least means square(NLMS), and recursive least mean square (RLS).
The simulation results demonstrates that NLMS algorithm is computationally
efficient but cannot handle impulsive noise whereas LMS and RLS can perform
better for long duration noise signals. The comparative analysis of adaptive
filtering algorithms and ICA shows that ICAcan perform better then all three
iterative gradient based algorithms because of its non-iterative nature. For
testing and simulations, three variants of white Gaussian noise (WGN) areused
to corrupt the video frame.
CONSLUSION
In this paper, three test cases of WGusing four different noise cancellation
observed that different noise cancellation Tec differently to the noise present

2. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
in a vidsimulation results reveals that in a case affecting a video signal in a
continuous mannpixel in a video frame is distorted ICA can provides an output
SNR of 31.0444 dB in just Although the highest SNR value among all t33.4081
dB achieved by NLMS but the time case is 77.7041 seconds. For the similar
kinalso achieves an acceptable output SNR of drawback is the high
computational time of seconds. In the second case where the noise the time
but it is only affecting every 10thframe, which is the case where noise is
intspecific time, the technique that performsFig. 8. RLS Visual Results for all
three cases.Fig. 9. RLS Visual Results for all three cases.ONGN are evaluated
Techniques. It ischniques responddeo frame. Thewhere noise isner so that
everyn be used whicht 0.4992 seconds.the techniques ise required in thisnd of
noise RLS31.1 dB but thef about 377.6628is not present allpixel in a
videoterfering after abest among allothers is LMS enabling an output SNRLS
showed poor performance in toutput SNR of 2.2139 dB and 2.211Fig. 10 .
Comparison based oNR of 32.7 dB.NLMS andthis case achieving only an11 dB
respectively.
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3. CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
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