1. DSP final project proosal From Bilateral-filter to Trilateral-filter : A better improvement on denoising of images R94922077 張錦文

2. outline Denoising Bilateral filtering Trilateral filtering Reference

3. Denoising Detect noise –Gaussian noise –Impulse noise –Others Remove noise –Gaussian filter –Other techniques

4. Bilateral filtering Two components –Spatial –Radiometric Functionality –Remove gaussian noise & preserve edges Advantages –Not iterative –Easy to implement

5. Bilateral filtering(cont.) For a gray level image, remove gaussian noise & preserve edge.

6. Bilateral filtering(cont.)

7. Trilateral filtering Add the ability to detect & remove impulse noise. Three components –Spatial –Radiometric –Impulse detection factor

8. Trilateral filtering(cont.)

9. Reference [1] C. Tomasi and R. Manduchi, “ Bilateral Filtering for Gray and Color Images, ” in Proc. IEEE Int. Conf. Computer Vision, 1998, pp. 839-846 [2] Roman Garnett, Timothy Huegerich, Charles Chui, Fellow, IEEE, and Wenjie He, Member, IEEE, “ A Universal Noise Removal Algorithm With an Impulse Detector, ” IEEE Trans. Image Process., vol. 14, no. 11, pp. 1747-1754, Nov. 2005 [3] J. Immerkaer, “ Fast Noise Variance Estimation, ” Comput. Vis. Image Understand., vol.64, pp.300-302, Sep. 1996 [4] Charles Kervrann and Jerome Boulanger, “ Optimal Spatial Adaptation for Patch-Based Image Denoising, ” IEEE Trans. Image Process., vol.15, no.10, pp.2866-2878, Oct. 2006