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# Rank Ordered Mean Noise Blanker or Sliding Median Noise Blanker - PowerPoint PPT Presentation

Rank Ordered Mean Noise Blanker or Sliding Median Noise Blanker. (or how NB2 works!) Phil Harman VK6APH. The Problem. Conventiona l (Analogue) Solutions. Noise Blanker. Noise Clipper. A DSP Solution. An image processing technique. An image processing technique. Original Image.

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### Rank Ordered Mean Noise BlankerorSliding Median Noise Blanker

(or how NB2 works!)

Phil Harman VK6APH

Conventional (Analogue) Solutions

Noise Blanker

Noise Clipper

Original Image

Image + Impulse noise

Image + Impulse noise

Median Filtered Image

• Record the values nearby

7, 9, 11, 12, 14, 15, 17, 18, 200

• Sort (Rank) the values

7, 9, 11, 12, 14, 15, 17, 18, 200

• The median is the middle of a distribution: half the scores are above the median and half below*.

7, 9, 11, 12, 14, 15, 17, 18, 200

• The median is much less sensitive to extreme values and makes it a better measure than the mean for highly skewed distributions e.g. the mean is 34

* For an even number of values use the average of centre values

• Look for samples that are outside the norm

• Sort (Rank) the samples either side in Order

• Calculate the median value

• Replace the suspect sample with the median

• Slide along to the next suspect sample and repeat

• Issues:

• Processor intensive

• Distortion if applied too aggressively

• Only effective on impulse noise

• Simpler technique gives equally good results.

• Q. How do we detect suspect samples?

• A. Keep an average of all samples and look for samples that are greater than the average by some amount

e.g. average = 0.999last_sample + 0.001current_sample

• Code:

If sample > (threshold x average)

apply median filter

for i < buffer_size

mag = mag(signal,i)

“median” = 0.75median + 0.25(signal,i)

average = 0.999average + 0.001mag

if mag > (threshold x average)

(signal,i) = median

next i

void

SDROMnoiseblanker(NB nb) {

int i;

for (i = 0; i < CXBsize(nb->sigbuf); i++) {

REAL cmag = Cmag(CXBdata(nb->sigbuf, i));

Cscl(CXBdata(nb->sigbuf, i), 0.25));

nb->average_mag = 0.999 * (nb->average_mag) + 0.001 * cmag;

if (cmag > (nb->threshold * nb->average_mag))

CXBdata(nb->sigbuf, i) = nb->average_sig;

}

}

• Noise “Subtraction” (N4HY)

• Detect the pulse

• Determine what the receiver has done to it

• Create a model of the pulse

• Subtract the model from the signal

• Completely linear process

• If you get it wrong it will add a noise pulse!

Rank Order Mean (ROM) Noise Banker

Sliding ROM Noise Blanker

Median Impulse Reduction