AN EFFICIENT DENOISING     ARCHITECTURE FOR    REMOVAL OF RANDOM  VALUED IMPULSE NOISE IN          IMAGESProject guide    ...
AREA OF PROJECT• Very Large Scale Integration• Digital Image Processing                         2 of 14                   ...
ABSTRACT• An efficient denoising scheme and its VLSI  architecture for the removal of random-valued  impulse noise• A deci...
EXISTING SYSTEM• ATMBM[Alpha Trimmed Mean Based Method]• DRID[Differential Rank Impulse Detector]• RORD-WMF[Rank Order Rel...
DRAWBACKS• Lower performance• Higher complexity• Full frame buffer                      5 of 14   5
PROPOSED SYSTEM• DTBDM[Decision Tree Based Denoising Method]• Decision tree based impulse detector• Edge preserving image ...
BLOCK DIAGRAM                  0                                     controller          Odd     1                        ...
EXPLANATIONLINE BUFFER• Odd line buffer and even line buffer are used to store  the pixel at odd and even rows respectivel...
CONT…DECISION TREE BASED IMPULSE DETECTOR• The decision tree is a binary tree and can determine the  status of pi,j by usi...
CONT…CONTROLLER• Controller sends signals to control pipelining and  timing statuses of the proposed circuits• Sends contr...
ADVANTAGES• Two line memory buffer• Low complexity technique• It requires simple computations• It remove the noise from co...
APPLICATIONS• Medical imaging• Scanning techniques• Face recognition                        12 of 14   12
CONCLUSION• Decision-tree-based detector to detect the noisy pixel  and employs an effective design to locate the edge• Th...
REFERENCES• Chih-Yuan Lien, Chien-Chuan Huang, Pei-Yin Chen,  and Yi-Fan Lin “An efficient denoising architecture  for rem...
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An efficient denoising architecture for removing impulse noise

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An efficient denoising architecture for removing impulse noise

  1. 1. AN EFFICIENT DENOISING ARCHITECTURE FOR REMOVAL OF RANDOM VALUED IMPULSE NOISE IN IMAGESProject guide Project membersR.Devaraj B.E., D.Mohan raj (100407622007) D.Raja (100407622009) A.Thangapandi (100407622013) 1 of 14 1
  2. 2. AREA OF PROJECT• Very Large Scale Integration• Digital Image Processing 2 of 14 2
  3. 3. ABSTRACT• An efficient denoising scheme and its VLSI architecture for the removal of random-valued impulse noise• A decision-tree-based impulse noise detector to detect the noisy pixels• Edge-preserving filter to reconstruct the intensity values of noisy pixels 3 of 14 3
  4. 4. EXISTING SYSTEM• ATMBM[Alpha Trimmed Mean Based Method]• DRID[Differential Rank Impulse Detector]• RORD-WMF[Rank Order Relative Difference – Wavelet Median Filter] 4 of 14 4
  5. 5. DRAWBACKS• Lower performance• Higher complexity• Full frame buffer 5 of 14 5
  6. 6. PROPOSED SYSTEM• DTBDM[Decision Tree Based Denoising Method]• Decision tree based impulse detector• Edge preserving image filter 6 of 14 6
  7. 7. BLOCK DIAGRAM 0 controller Odd 1 decision 1 line register Edge tree based 0 buffer 1 bank preserving impulse 0 filter detector Even 0 line Output 1 buffer image Inputimage 7 of 14 7
  8. 8. EXPLANATIONLINE BUFFER• Odd line buffer and even line buffer are used to store the pixel at odd and even rows respectivelyREGISTER BANK• It consists of nine register• To store the 3x3 pixel values of the current mask 8 of 14 8
  9. 9. CONT…DECISION TREE BASED IMPULSE DETECTOR• The decision tree is a binary tree and can determine the status of pi,j by using the different equations in different modulesEDGE PRESERVING FILTER• To reconstruct the intensity values of noisy pixels• adaptive technology is used to enhance the effects of removal of impulse noise 9 0f 14 9
  10. 10. CONT…CONTROLLER• Controller sends signals to control pipelining and timing statuses of the proposed circuits• Sends control signal to schedule reading and writing statuses of the data that are stored in register bank 10 of 14 10
  11. 11. ADVANTAGES• Two line memory buffer• Low complexity technique• It requires simple computations• It remove the noise from corrupted images efficiently and requires no previous training• Better performance 11 of 14 11
  12. 12. APPLICATIONS• Medical imaging• Scanning techniques• Face recognition 12 of 14 12
  13. 13. CONCLUSION• Decision-tree-based detector to detect the noisy pixel and employs an effective design to locate the edge• The VLSI architecture of our design yields a processing rate of about 200 mhz• It requires only low computational complexity and two line memory buffers 13 Of 14 13
  14. 14. REFERENCES• Chih-Yuan Lien, Chien-Chuan Huang, Pei-Yin Chen, and Yi-Fan Lin “An efficient denoising architecture for removal of impulse noise in image ”,IEEE .2012• T. Sun and Y. Neuvo, “Detail-preserving median based filters in image processing,” Pattern Recognit. Lett., vol. 15, pp. 341–347, Apr. 1994• Barry De Ville, Decision Trees for Business Intelligence and Data Mining. 2007 14 of 14 14

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