Rank ordered mean noise blanker or sliding median noise blanker
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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 Blanker or Sliding Median Noise Blanker

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Rank ordered mean noise blanker or sliding median noise blanker

Rank Ordered Mean Noise BlankerorSliding Median Noise Blanker

(or how NB2 works!)

Phil Harman VK6APH


The problem

The Problem


Conventiona l analogue solutions

Conventional (Analogue) Solutions

Noise Blanker

Noise Clipper


A dsp solution

A DSP Solution


An image processing technique

An image processing technique


An image processing technique1

An image processing technique

Original Image

Image + Impulse noise


Median filtering

Median Filtering

Image + Impulse noise

Median Filtered Image


How median filtering works

How Median Filtering works


How median filtering works1

How Median Filtering works

  • 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


Median filtering example

Median Filtering Example


Median filtering example1

Median Filtering Example


Median filtering example2

Median Filtering Example


Median filtering example recap

Median Filtering Example - recap

  • 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.


Median filtering example3

Median Filtering Example

  • 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


Pseudo code

Pseudo Code

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


Sdr1000 code

SDR1000 Code

void

SDROMnoiseblanker(NB nb) {

int i;

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

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

nb->average_sig = Cadd(Cscl(nb->average_sig, 0.75),

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;

}

}


Future techniques

Future Techniques

  • 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!


Questions

Questions?

Rank Order Mean (ROM) Noise Banker

Sliding ROM Noise Blanker

Median Impulse Reduction


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