The document proposes an efficient denoising architecture using a decision tree based method (DTBDM) to remove random valued impulse noise from images. It uses a decision tree impulse noise detector to identify noisy pixels and an edge-preserving filter to reconstruct pixel intensities. The architecture requires only two line memory buffers and low computational complexity. It can remove noise efficiently from corrupted images with better performance than existing methods.

1. AN EFFICIENT DENOISING
ARCHITECTURE FOR
REMOVAL OF RANDOM
VALUED IMPULSE NOISE IN
IMAGES
Project guide Project members
R.Devaraj B.E., D.Mohan raj (100407622007)
D.Raja (100407622009)
A.Thangapandi (100407622013)
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2. AREA OF PROJECT
• Very Large Scale Integration
• Digital Image Processing
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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
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4. EXISTING SYSTEM
• ATMBM[Alpha Trimmed Mean Based Method]
• DRID[Differential Rank Impulse Detector]
• RORD-WMF[Rank Order Relative Difference –
Wavelet Median Filter]
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5. DRAWBACKS
• Lower performance
• Higher complexity
• Full frame buffer
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6. PROPOSED SYSTEM
• DTBDM[Decision Tree Based Denoising Method]
• Decision tree based impulse detector
• Edge preserving image filter
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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
Input
image
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8. EXPLANATION
LINE BUFFER
• Odd line buffer and even line buffer are used to store
the pixel at odd and even rows respectively
REGISTER BANK
• It consists of nine register
• To store the 3x3 pixel values of the current mask
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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
modules
EDGE PRESERVING FILTER
• To reconstruct the intensity values of noisy pixels
• adaptive technology is used to enhance the effects of
removal of impulse noise
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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
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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
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12. APPLICATIONS
• Medical imaging
• Scanning techniques
• Face recognition
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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
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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
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