detail preserving filter
TRANSCRIPT
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“A Detail Preserving Filter for
Impulse Noise Detection &Removal”
Supervisor
ASeminar
On
By
Vikas K.
Bhangdiya(2008MEC004)
Dr. S. V. Bonde
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Outline1. Introduction
2. Impulse Noise Model
3. Need of DPF
4. Detail Preserving Filter (DPF)
5. Impulse Noise Detection6. Edge detection
7. Filtering Schemes
8. Blur Metric
9. Results
10. Conclusions
11. References
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Introduction
• Noise
• Linear Filter: - A neighborhood averaging mechanism to
remove impulse noise and tend to destroy all highfrequency details like edges, lines and other fine image
details.
• Non-linear Filter: - It also operates on neighborhoods,
however it operations based directly on the values of theneighborhood under consideration, and they don't useexplicitly use coefficients.
• Blur Metric: - It is based on the analysis of the spread of
edges in an image.
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Impulse Noise Model
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ij
ij
ij p
N with probabilty p X
I with probabilty
Where
i = 1,2,……..s1 j = 1,2,……..s2
: -Original Image: -Noisy Image
: -Observation Image
ij N
ij I
ij X
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Need of DPF
Classical Filter: - All input samples are unconditionally
affected by the filtering process.
Selective Filter: - First checked pixel is corrupted byan impulse. If so, replace it by a value estimated from itsneighbors in the window; otherwise pass it to the outputunprocessed.
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Is pixel
Noisy
Yes
Detail Preserving filter
(DPF)
Selective Filter
Noisy Image
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Detail Preserving Filter
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Impulse Noise detection
As a first step in DPF, Adaptive Median Filter based impulsedetector is applied for finding the position of impulses.
|( )
|
ij
pq
ij
X p q i p mW X
X q m j q m
Where p, q : - index of the current pixel.
It could be observed that the corrupted pixels belong to the
set {Wmin,Wmax }, where Wmin is the minimal pixel valuein the defined window and Wmax is the maximum pixelvalue.
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A pixel may be corrupted and assigned to a flag matrix ‘N’ as
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min max1 ( ) & ,( , )
0
ij ij ijif X M X W W
N i jelse
WhereX : - Corrupted ImagesM: - Filtered images
This impulse detection scheme detects impulse noise evenat higher corruption levels setting the flag matrix N(I,j)values as 1 wherever noise exists.
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Adaptive Median Filter is a good method for removingrandom-valued impulse noise.
We take large thresholds so it will only select pixels that are
most likely to be noisy, then we restore them.
Subsequent iterations, we decrease the thresholds toinclude more noise pixel. Since the edges and the detailsare preserved by the regularization successfully in each
iteration, the restored image will not be distorted by thismethod.
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• To isolate the noisy pixels present on the edge. Extractingedges from the corrupted image is a difficult task withouthaving the prior knowledge of the edge information.
• So median filtering is applied on the corrupted imagef’(x,y), Canny edge detector is applied on the medianfiltered output m(i,j).
• Canny detects true edges at higher level corruption also.
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Edge detection
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Where
G : - Gaussian function of standard deviation
I(i,j): - obtained from the median filtered output m(i,j).
e(i,j): - Edge matrix will have the value 1 if there is an
edge pixel
and value 0 for pixels not on edge.
• Initially edge is detected from the median filtered output,
and then edge is detected from the iterative filteredresult.
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( , ) ( )e i j G I
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Where
: -Noise Matrix
: -Edge Matrix
: -Noise on edge
: -Noise not on edge
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Categorization of Noisy Pixel
1 1 1( , ) & ( , )
0( , )e
if e i j N i j
Otherwise N i j
'
1 1( , ) 0 & ( , )
0( , )
e
if e i j N i j
Otherwise N i j
( , ) N i j
( , )e i j
( , )e N i j
' ( , )e N i j
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Filtering Schemes
• Noise indicated on edge pixels by Ne(i,j).
• Noise indicated not on edge indicated by Ne’ (i,j).
Each noisy edge pixel is replaced by taking the median of closest nonnoisy edge pixel present along the direction of the edge.
The direction of edge is found by using the connectivity of the
edge pixels.
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• where Z is a noisy edge pixel. Shaded regions indicate thedirection of edge, Here noisy edge pixels indicated by the
1’s and 0’s represent either noise free or non-edge pixels.
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• The proposed method tends to replace ‘Z’ by median of nearest non noisy pixels contained in the vector ‘Y’ along
the direction of the edge.
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, . , ,1 1i j i j s t i jS med Y Ne
,1i jS
, . , , 12 ( ) 'i j i j s t i jS med X Ne
: -Filtered output of the noise other than on edge.: -Filtered output of the noisy edge pixels.: -Final Filter Image
V : -Vector containing non noisy pixels present in itsneighborhood.
,2
i jS
( , ) 1( , ) 2( , )S i j S i j S i j U
( , )S i j
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• An image appears blurred when its high spatial frequencyvalues in the spectrum are attenuated.
• Motion blur, Out of focus blur, etc.
• A no-reference blur measurement technique. We assumeno knowledge of the original image, and do not make anyassumptions on the type of content or the blurring process.
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Blur Metric
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Fig: - One row of the blurred image. The detected edges areindicated by the dashed lines, and local minima and maximaaround the edge by dotted lines. The edge width at P1 is P2 −P2.
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Results
Blur Metric= 2.5166, Blur Metric= 2.4269SSIM=0.8691 SSIM=0.8923
Figure: - Comparison of filteredoutput
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Figure: - Comparison of Iterative Results
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Conclusion
The proposed Filtering technique applies Iterative,selective and directional filtering on the corruptedimage to reduce the blur. The results shows that this
method removes impulse noise, also simultaneouslypreserves edges at higher levels of noise as isevident from comparison with existing filters.
R f
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References
[1] S. Md. Mansoor Roomi , T. Pandy Maheswari , V. Abhai Kumar
“A Detail Preserving Filter for Impulse Noise Detection and
Removal”ICGST-GVIP Journal, Volume 7, Issue 3, November 2007.
[2] Rafael Gonzalez Richard Woods , Digital Image Processing,Pearson Publications.
[3] Raymont H. Chan, “An Iterative procedure for removingrandom- valued impulse noise," IEEESignal Process. Lett., vol.no. 11, pp. Dec 2004.
[4] P. Marziliano, F. Dufaux, S. Winkler, T. Ebrahimi,“A no-reference perceptual Blur metric”, in: Proceedings of theInternational Conference on ImageProcessing, Vol. 3,
Rochester, NY, 2002, pp. 57–60.
[5] Kh. Manglem Singh and Prabin K. Bora, “Features PreservingFilters
Using Fuzzy Kohonen Clustering Network in Detection of Impulse
Noise“23
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Thank You
…!!!
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Adaptive Median Filter