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 - 2008 winter Poster Performed by: Cohen Ido, Volokitina Irina Instructor: Rivkin Ina, Technion Almog Asaf, Intel Denoising video in real time

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

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

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

5. Bilateral filter block Diagram HPF (3X3) LPF (3X3) MEMORYMEMORY Image analysis & Fir select Controller YCbCr configuration X X + Synplify implemented GIDEL implemented

6. System block diagram Denoising unit (bilateral) RGB YCbCrYCbCr RGB RGBRGB RGBRGB YCbCr Adaptive LPF YCbCr configuration Fir select MUXMUX HPF Noise No Noise GiDEL PROCWizard

7. Implementation : System level top view (a) – input domain; (b) – delay line block; (c) – RGB to YCbCr transform blocks – one for each line; (d) – delay sample blocks; (e) – min max block; (f), (g) – synchronizing pipes; (h1) – LPF block; (h2) –HPF block; (k) – filter select domain; (l) – synchronizing domain; (m) – YCbCr to RGB block; (n) – output domain (p) – simplify tool button

8. Summary and conclusions Summary We have implemented denoising bilateral algorithm using GIDEL's hardware and High Level Design tools as SynplifyDSP and SynplifyPro. We have implemented denoising bilateral algorithm using GIDEL's hardware and High Level Design tools as SynplifyDSP and SynplifyPro. The algorithm performs real-time adaptive filtering per pixel of video stream coming from the DVI input. The algorithm performs real-time adaptive filtering per pixel of video stream coming from the DVI input. Project goals are fully achieved. Project goals are fully achieved. Conclusions  Not easy to implement on such data rates but - Can be done  Better results using HPF bypass -> Implemented HPF algorithm is not a best solution  Possible improvement :  other HPF algorithm or another filter selection algorithm (few threshold etc.)