Rank%20Ordered%20Mean%20Noise%20Blanker%20or%20Sliding%20Median%20Noise%20Blanker - PowerPoint PPT Presentation

About This Presentation
Title:

Rank%20Ordered%20Mean%20Noise%20Blanker%20or%20Sliding%20Median%20Noise%20Blanker

Description:

Noise Clipper. Noise Blanker. A DSP Solution. An image processing technique ... The median is the middle of a distribution: ... 7, 9, 11, 12, 14, 15, 17, 18, 200 ... – PowerPoint PPT presentation

Number of Views:45
Avg rating:3.0/5.0
Slides: 19
Provided by: philh5
Learn more at: http://www.tracey.org
Category:

less

Transcript and Presenter's Notes

Title: Rank%20Ordered%20Mean%20Noise%20Blanker%20or%20Sliding%20Median%20Noise%20Blanker


1
Rank Ordered Mean Noise BlankerorSliding Median
Noise Blanker
  • (or how NB2 works!)
  • Phil Harman VK6APH

2
The Problem
3
Conventional (Analogue) Solutions
Noise Blanker
Noise Clipper
4
A DSP Solution
5
An image processing technique
6
An image processing technique
Original Image
Image Impulse noise
7
Median Filtering
Median Filtered Image
Image Impulse noise
8
How Median Filtering works
7 17 18
15 200 9
12 11 14
9
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

10
Median Filtering Example
11
Median Filtering Example
12
Median Filtering Example
13
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.

14
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 gt (threshold x average)
  • apply median filter

15
Pseudo Code
  • for i lt buffer_size
  • mag mag(signal,i)
  • median 0.75median 0.25(signal,i)
  • average 0.999average 0.001mag
  • if mag gt (threshold x average)
  • (signal,i) median
  • next i

16
SDR1000 Code
  • void
  • SDROMnoiseblanker(NB nb)
  • int i
  • for (i 0 i lt CXBsize(nb-gtsigbuf) i)
  • REAL cmag Cmag(CXBdata(nb-gtsigbuf, i))
  • nb-gtaverage_sig Cadd(Cscl(nb-gtaverage_sig,
    0.75),
  • Cscl(CXBdata(nb-gtsigbuf, i), 0.25))
  • nb-gtaverage_mag 0.999 (nb-gtaverage_mag)
    0.001 cmag
  • if (cmag gt (nb-gtthreshold nb-gtaverage_mag))
  • CXBdata(nb-gtsigbuf, i) nb-gtaverage_sig

17
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!

18
Questions?
  • Rank Order Mean (ROM) Noise Banker
  • Sliding ROM Noise Blanker
  • Median Impulse Reduction
Write a Comment
User Comments (0)
About PowerShow.com