Detail Preserving Filter

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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



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

2



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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4

Impulse Noise Model

1

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



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



Detail Preserving Filter



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.



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.



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



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



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



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.



• 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



• 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



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

Detail Preserving Filter

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