Analysis of Adaptive and Advanced Speckle Filters on SAR Data
ppt hari
1. p.hari kumar
MEKAPATI RAJAMOHAN REDDY INSTITUTE OF
TECHNOLOGY AND SCIENCE
(Affiliated to Jawaharlal Nehru Technological University, Ananthapur-515001)
UDAYAGIRI, NELLORE (DIST)-524 226
2. A NEW AND EFFICIENT
ALGORITHM FOR THE REMOVAL OF
HIGH DENSITY SALT AND PEPPER
NOISE IN IMAGES AND VIDEOS
3. ABSTRACT:
A new and efficient algorithm for high-density saltand pepper
noise removal in images and videos is proposed.
The existing non-linear filter like Standard Median Filter (SMF),
Adaptive Median Filter (AMF), Decision Based Algorithm (DBA) and
Robust Estimation Algorithm (REA) shows better results at low and
medium noise densities.
At high noise densities, their performance is poor .
A new algorithm using sheer sorting method, which has low
computation time when compared with other algorithms.
It is proved that the new method has better visual appearance
and quantitative measures at higher noise densities as high as
90%.
5. History in the evaluation of algorithm:
1.Median Filter:2.Adaptive Median filter3.Shear sorting algorithm
Median filters are known for their capability to
remove impulse noise without damaging the edges.
This median filters are used at low and medium
densitys only, At higher densitys it undergo distortion
If we pass the noise image through the median filter
it will effect both the corrupted and non-corrupted
pixels.,
Adaptive Median is a “decision-based” or “switching”
filter that first identifies possible noisy pixels and then
replaces them using the median filter or its variants, while
leaving all other pixels unchanged.
This filter is good at detecting noise even at a high noise
level
Streaking effect occurs
It is based on parallel architecture. In practice the
parallel architectures help to reduce the number of logic
cells required for its implementation.
In the odd phases (1,3,5) even rows are sorted in
descending order and rows are sorted out in ascending
order. In the even phases columns are sorted out
independently in ascending order
For example:
4.Modified shear sorting
In order to improve the computational efficiency
shear sorting algorithm is modified as follows:
i)All the three rows of the window are arranged
in
ascending order.
ii)Then all the columns are arranged in ascending
order.
iii)The right diagonal of the window is now arranged
in ascending order
13 17 12
18 11 19
15 14 16
Original pixels
11 13 16
12 15 17
14 18 19
11 13 16
12 15 17
14 18 19
12 13 17
11 18 19
14 15 16
Step1
Step-2
Step-3
6. Decision based un symmetric
trimmed median filter(DBUTM)
In DBA each pixel is processed for de noising using a 3 X 3
window.
During processing if a pixel is ‘0’ or ‘255’ then it is
processed else it is left unchanged. In DBA the corrupted pixel
is replaced by the median of the window.
.At higher noise densities the median itself will be noisy,
and, the processing pixel will be replaced by the
neighborhood-processed pixel.
This repeated replacement of neighborhood pixels produce
streaking effect
7. ALGORITHMS
FOR images FOR videos
STEP 1:A 2D 3x3 matrix is
selected
STEP 2:Pixel values are sorted
in ascending order and sorted
in 1D array
STEP 3:If pixel value is ‘0’ or
‘255’, then the median of the
remaining values is calculated.
STEP 4:The pixel which is
processed is replaced by the
median value.
STEP 1:video to frames
STEP 2:Frames to images
STEP 3:Noise removal using
DBUTM algorithm
STEP 4:Images to frames
STEP 5:Frames to video
8. Select a 2-D 3 X 3 window with
center element Zxy as processing pixel
Eliminate the elements with values
‘0’ or ‘255’ in the 1-D array
Replace the processing pixel with
the median of 1-D array
0<Zxy<255
Yes
No
Noisy
image
Restored image
37 69 87
0 39
255 57 43
255
37 39 43 57 65 89
(43+57)/2= 50
37 69 87
0 39
255 57 43
255
50
9. AVI to FRAME
conversion
Read in frames
DBUTM
Write out frames
Frame to aviFor Video’s
By DBUTM algorithm
Noisy video
Restored
Video
10. RESULTS & DISCUSSIONS
The performance of the proposed algorithm is tested for
various levels of noise corruption and compared with
standard filters namely standard median filter (SMF), adaptive
median filter (AMF) and decision based algorithm (DBA) .
The performance of the developed algorithm and other
standard algorithms are quantitatively measured by the
following parameters such as peak signal-to-noise ratio
(PSNR), Mean square error (MSE) and Image Enhancement
Factor (IEF) .
11. The quantitative performances in terms of PSNR, MSE and IEF for all the
algorithms are given in Tables
METRICS SMF AMF DBA DBUTM
PSNR 23.84 28.36 28.09 29.37
MSE 268.74 94.94 100.98 75.13
IEF 22.01 62.4 58.6 78.82
METRICS SMF AMF DBA DBUTM
PSNR 8.26 17.11 19.03 23.68
MSE 9699.2 1264.2 812.77 278.82
IEF 1.9 14.59 22.71 66.45
Results for 50% noise corrupted Barbara image (a) original
image (b) 30% noise corrupted image. Restoration results of
(c) SMF (d) AMF (e) DBA (f) DBUTM
. Results for 90% noise corrupted Parrot image (a) original
image(b) 90% noise corrupted image. Restoration results of
(c) SMF (d) AMF (e) DBA (f) DBUTM
Practical results at different noise densities
13. Conclusion:
The modified sheer sorting architecture reduces
the computational time required for finding the
median, which increases the efficiency of the
system.
The algorithm removes noise even at higher noise
densities and preserves the edges and fine details.
14. oDigital image processing:
mathematical
and computational methods
Jonathan M. Blackledge
oDigital image processing
David Collins, William K. Pratt
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