1. המעבדה למערכות ספרתיות מהירות High speed digital systems laboratory הטכניון - מכון טכנולוגי לישראל הפקולטה להנדסת חשמל Technion - Israel institute of technology department of Electrical Engineering 2007 winter Characteristic presentation (part B) Performed by: Cohen Ido, Volokitina Irina Instructor: Rivkin Ina, Technion Almog Asaf, Intel Denoising video in real time

2. Agenda Motivation and background. Optional concepts and solutions (Why bi-lateral filter?). Block diagram. Environment and development tools. Validation’s concept. Expectations and destination for middle presentation. Time line.

3. Image noise – definition and more The term noise usually refers to the high frequency random perturbations. corresponds to visible grain or particles present in the image. Generally caused by the electronic noise in the input device sensor and circuitry (e.g. scanner, digital camera).

4. Noisy image-salt and pepper

5. Noisy image-Uniform

6. Noisy image-Normal

7. Type of noise

8. The solution is DENOISING Removing noise from data is often the first step in data analysis. Denoising techniques should not only reduce the noise, but do so without blurring or changing the location of the edges.

9. Optional concepts and solutions Diffused image. Bilateral filter.

10. Diffused image.

11. Examples

12. Bilateral filter Bilateral filtering smooths images while preserving edges, by means of a nonlinear combination of nearby image values. The bilateral filter can enforce the perceptual metric underlying the CIE-Lab color space, and smooth colors and preserve edges in a way that is tuned to human perception.

13. Bilateral Simple implementation. Noniterative. Local. YUV. (CIE) Diffusion Complicating implementation. Iterative. Not local. RGB. Bilateral and diffusion filtering comparison Conclusion: local and noniterative characterizations make us to choose in bilateral algorithm

14. Goals of project Implement denoise bilateral algorithm Denoise bilateral algorithm Noise No Noise = Video in Video out configuration

15. System Block Diagram Bilateral filterYUV RGB Denoise algorithm RGB YUV configuration Video in Video out

16. Bilateral filter block Diagram HPF (3X3) LPF (3X3) MEMORYMEMORY Image analysis & Fir select Controller YUV configuration X X +

17. VALIDATION Implement the algorithm with high level design tool MATLAB, and compare the denoising image and the original one.

18. Validation the solution’s correctness The indication for “success” or “fail” is MSE. The destination is to reduce the MSE by 50%.

19. Validation Process Noise (normal distribution) denoising unit (bilateral) + Input File (without noise) Output File MSE measurement RGBRGB RGBRGB RGBRGB RGBRGB RGBRGB RGBRGB RGBRGB configuration noisemaker MSE redaction percentage

20. Implementation and validation process MSE tester. Normal distribution noisemaker. Denoising unit. Stages in the implementation rgb2yuv function and opposite. Adaptive LPF. Fir select. Adaptive HPF.

21. Implementations stages Denoising unit (bilateral) RGB YUVYUV RGB RGBRGBRGBRGB YUV

22. Implementations stages Denoising unit (bilateral) RGB YUVYUV RGB RGBRGBRGBRGB YUV Adaptive LPF YUV configuration

23. Implementations stages Denoising unit (bilateral) RGB YUVYUV RGB RGBRGBRGBRGB YUV Adaptive LPF YUV configuration Fir select MUXMUX

24. Implementations stages Denoising unit (bilateral) RGB YUVYUV RGB RGBRGBRGBRGB YUV Adaptive LPF YUV configuration Fir select MUXMUX Adaptive HPF

25. expectations rgb2yuv and opposite – changing up to 10% in MSE. LPF – reducing MSE by at least 35% Adding fir select – reducing MSE by 5-10% HPF – reducing 10% MSE. Destination: reduction the MSE by 50%

26. Time Lines 3.1.07 1 week 10.1.07 Implement MSE tester, noisemaker, rbg2yuv, yuv2rgb 2 week 24.1.07 Implement LPF, fir sector 1 week 31.1.07 Implement HPF The target: till end of January we should validate the given bilateral filtering algorithm and reduce MSE by 50% Middle presentation