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Md. Tanvir Al Amin (Presenter) tanviralamin@gmail Anupam Bhattacharjee abrbuet@yahoo

Impulsive Noise Reduction in Natural Images by Plane and Paraboloid Regression. Md. Tanvir Al Amin (Presenter) tanviralamin@gmail.com Anupam Bhattacharjee abrbuet@yahoo.com Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology,

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Md. Tanvir Al Amin (Presenter) tanviralamin@gmail Anupam Bhattacharjee abrbuet@yahoo

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  1. Impulsive Noise Reduction in Natural Images by Plane and Paraboloid Regression Md. Tanvir Al Amin (Presenter) tanviralamin@gmail.com Anupam Bhattacharjee abrbuet@yahoo.com Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.

  2. What we consider Noise Presence of unwanted components in a signal.Inherent with Signal Handling devices. In case of a digital image, noise is deviation of image pixels from their actual values. Standard Image : Lenna Corrupted Lenna

  3. Types of noise Various ways of Classification. Two general cases : 1. Dependent Noise (Gaussian Noise) 2. Independent Noise (Salt and Pepper Noise)

  4. Noise Reduction Problem It is clear that we need to remove noise. But we can only reduce it. An ill posed problem since Not well defined whether a pixel is corrupted or not. We Address here: One kind of random noise, appearing on the image as additive random impulsive dots or small regions.

  5. Our Assumptions • Impulsive Noise is uniformly distributed throughout the whole image having fixed noise density. • 2. Natural Images have continuous tones.Noisy pixels vary more than a threshold value. Simulated noisy images satisfying our assumptions

  6. Stages of the Solution Stage 1 : Detect the pixels which are corrupted. Stage 2 : Keep the uncorrupted pixels intact. Estimate values for the corrupted pixels from its neighboring good pixels.

  7. Basic Idea of Noise Detection • Take window of certain dimension s, depending on Noise Density ρ • Sweep it for all possible positions in the image array. • Process Each window. A window starting at (2,3)

  8. Basic Idea of Noise Detection • Each window verdicts about each of the s2 pixels inside, whether it is Corrupted or not. • Local Classification : Classification of each pixel by a single window. • Global Classification : Combined output of all Local Decision

  9. Processing Each Window Fit a plane with the pixel values in a window (Least Squares Regression) δ= 25 Let Z be plane approximation Good Pixel Select those pixels as corrupted for which deviation exceeds Parameter δ Corrupt Pixel

  10. Combining Local Solutions Each non-boundary pixel examined by S2 windows. Local Classifications are combined by “Majority vote”. Verdicts of each window considered as “votes”. Idea is : if most of the windows report a pixel “uncorrupted”, It is highly probable that this pixel is actually uncorrupted.

  11. Combining Local Solutions To discriminate between edge and noise we introduce, Classifier Parameter Ω = Ratio of successful judgments needed for any pixel to be flat We assume : In case of high contrast grainy parts or for edge pixels, large number of pixels inside a window will be reported wrong, causing judgment of that window unreliable.

  12. Combining Local Solutions Threshold Ratio, φ Minimum ratio of accepted verdicts needed for a pixel to be declared uncorrupted globally. Two Threshold ratios : Decision Tree

  13. Noise Filtering Fit a paraboloid with the good pixel values in each window From Paraboloid Approximation,Find suggestion for each corrupted pixel In case there is no estimate about a pixel, we use pixel averaging for it. Globally Estimate value of a noisy pixel by averaging all suggestions.

  14. Noise Detection Simulation Classification Efficiency, Error Detection Efficiency,

  15. Effect of Deviation Parameter φe = 0.7 and φn= 0.85, ρ = 0.34, Ω = 0.5, s = 4

  16. Effect of Density Parameter For noise density 30% optimal value of ρ is 0.4 as depicted

  17. Effect of Threshold ratio : For ρ = 0.4, Ω = 0.5, s=4, Noise Density = 30%, optimal value of φe = 0.7 and φn= 0.85.

  18. Various noise distribution.

  19. Noise Filtering Performance Peak Signal to Noise Ratio vs. Noise Density

  20. Visualization 30 % Noise, PSNR = 26 dB 6 % Noise PSNR = 32 dB 12 % Noise PSNR = 30 dB

  21. Complexity Number of windows = Cost per window for Local classification: O(s2) Time for Global Error Classification : O(mn) Filtering : O(ρs2) per window Final Estimation : O(mn) Total Cost : O((m-s+1)(n-s+1)s2 + mn + ρs2(m-s+1)(n-s+1)+mn) = O(mns2(1+ρ))

  22. Success No Blind mean or median filtering. Output doesn’t suffer from unwanted loss in sharpness. Main operations are solving systems of linear equations. No complicated mathematical operations or transformation. Specialized data structure is not necessary. Implementation logic is easy and economical with resources. We get more than 92% success on average.

  23. Shortcomings Noise detection is done in single pass, Filtering is also done in another single pas. Multilevel detection and filtering would improve it. For Regression, L1 norm is used. Less calculation needed  results in less accuracy. Only concentrates in algebraic methods considered. Considering frequency information and wavelet based statistics along with, would yield better result in noise detection and removal

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