1. SUSAN: structure-preserving noise reduction EE264: Image Processing Final Presentation by Luke Johnson 6/7/2007

2. SUSAN Principle Published by Stephen M. Smith and J. Michael Brady (1997) Published by Stephen M. Smith and J. Michael Brady (1997) USAN USAN Univalue Segment Assimilating Nucleus Univalue Segment Assimilating Nucleus Image is assumed to be made up of features Image is assumed to be made up of features Each feature is assumed to be of uniform brightness Each feature is assumed to be of uniform brightness USAN is defined as the area that corresponds to the feature which the center mask pixel is associated with USAN is defined as the area that corresponds to the feature which the center mask pixel is associated with Shaded area = USAN

3. SUSAN Principle SUSAN – Smallest USAN SUSAN – Smallest USAN Used for edge and corner detection Used for edge and corner detection Area of USAN is minimized at edges and corners Area of USAN is minimized at edges and corners No derivatives = better performance on noisy images No derivatives = better performance on noisy images Noise reduction Noise reduction USAN used as kernel for weighted averaging USAN used as kernel for weighted averaging Preserves underlying structure of image Preserves underlying structure of image Non-linear Non-linear

4. SUSAN denoising algorithm For each image pixel: For each image pixel: Overlay mask centered at image pixel Overlay mask centered at image pixel Determine USAN Determine USAN Replace image pixel with average of USAN pixels Replace image pixel with average of USAN pixels

5. USAN determination Binary comparison: Binary comparison: Gaussian comparison: Gaussian comparison: t is the brightness threshold set by the user t is the brightness threshold set by the user

6. Spatial weighting Also assume that pixels spatially nearer to the nucleus are more likely to be part of the same feature Also assume that pixels spatially nearer to the nucleus are more likely to be part of the same feature Spatial Gaussian weighting: Spatial Gaussian weighting: σ is the spatial smoothing factor chosen by the user σ is the spatial smoothing factor chosen by the user This means that the weight for each pixel is determined by how “close” it is to the center pixel both in the spatial domain and in the brightness domain. This means that the weight for each pixel is determined by how “close” it is to the center pixel both in the spatial domain and in the brightness domain.

7. Averaging Apply both weighting functions and average: Apply both weighting functions and average: or: or:

8. Zero-area USAN Since the center pixel is not counted as part of the USAN, it is possible to have an USAN area of zero or close to zero Since the center pixel is not counted as part of the USAN, it is possible to have an USAN area of zero or close to zero If this is the case then the nucleus is assumed to be impulse noise and its value is replaced by the median of its eight closest neighbors If this is the case then the nucleus is assumed to be impulse noise and its value is replaced by the median of its eight closest neighbors

9. SUSAN filter demonstration Test image used in Smith (1997)Residual after one pass with SUSAN filter (contrast enhanced)

10. Gaussian noise added (rms = 15.1)one filter iteration (rms = 3.51) two iterations (rms = 2.80) residual (contrast enhanced)

11. with impulse noise (rms = 24.8)two iterations of SUSAN filter (rms = 5.72) residual3x3 median filter (rms = 6.81)

12. Parameter dependence σ and t must be optimized for each iteration of the filter

15. Gaussian noise in natural images with Gaussian noise (rms = 25)SUSAN filter result (rms = 7.99)steering kernel result (rms = 6.66) Iterative steering Kernel – Takeda, Farsiu, Milanfar (2006)

16. Compression artifacts Poor-quality JPEG (rms = 9.76)SUSAN filter (rms = 8.60)bilateral filter (rms = 8.52) Bilateral filter – Tomasi (1998)

17. Film Grain Noise image corrupted by film grain noiseResults of SUSAN filterResults of bilateral filter

18. Conclusions SUSAN filter works well at reducing noise while preserving the underlying structure of images although it does have difficulty in certain situations. SUSAN filter works well at reducing noise while preserving the underlying structure of images although it does have difficulty in certain situations. The need to adjust three different parameters (spatial smoothing, brightness threshold, number of iterations) makes it a very time consuming method to use. Some way of automatically calculating these parameters would be useful. The need to adjust three different parameters (spatial smoothing, brightness threshold, number of iterations) makes it a very time consuming method to use. Some way of automatically calculating these parameters would be useful. More recent denoising algorithms have surpassed SUSAN in performance however many of them use the same general ideas as the SUSAN filter More recent denoising algorithms have surpassed SUSAN in performance however many of them use the same general ideas as the SUSAN filter

19. References Paris, S. and F. Durand, “A Fast Approximation of the Bilateral Filter using a Signal Processing Approach”, ECCV, 2006). Paris, S. and F. Durand, “A Fast Approximation of the Bilateral Filter using a Signal Processing Approach”, ECCV, 2006). Portilla, J., V Strela, M Wainwright, and E P Simoncelli, “Image Denoising using Scale Mixtures of Gaussians in the Wavelet Domain”, IEEE Transactions on Image Processing. vol 12, no. 11, pp. 1338- 1351, November 2003. Portilla, J., V Strela, M Wainwright, and E P Simoncelli, “Image Denoising using Scale Mixtures of Gaussians in the Wavelet Domain”, IEEE Transactions on Image Processing. vol 12, no. 11, pp. 1338- 1351, November 2003. Rudin, L., S. Osher, and E. Fatemi, “Nonlinear Total Variation based noise removal algorithms", Physica D, 60 259-268, 1992. Rudin, L., S. Osher, and E. Fatemi, “Nonlinear Total Variation based noise removal algorithms", Physica D, 60 259-268, 1992. Smith, Stephen M. and J. Michael Brady, “SUSAN -- A New Approach to Low Level Image Processing”, International Journal of Computer Vision, 1997. Smith, Stephen M. and J. Michael Brady, “SUSAN -- A New Approach to Low Level Image Processing”, International Journal of Computer Vision, 1997. Takeda, H., "Kernel Regression for Image Processing and Reconstruction", M.S. Thesis, Electrical Engineering, UC Santa Cruz, March 2006. Takeda, H., "Kernel Regression for Image Processing and Reconstruction", M.S. Thesis, Electrical Engineering, UC Santa Cruz, March 2006.Kernel Regression for Image Processing and ReconstructionKernel Regression for Image Processing and Reconstruction Takeda, H., S. Farsiu, and P. Milanfar, "Kernel Regression for Image Processing and Reconstruction", IEEE Transactions on Image Processing, Vol. 16, No. 2, pp. 349-366, February 2007. Takeda, H., S. Farsiu, and P. Milanfar, "Kernel Regression for Image Processing and Reconstruction", IEEE Transactions on Image Processing, Vol. 16, No. 2, pp. 349-366, February 2007.Kernel Regression for Image Processing and ReconstructionKernel Regression for Image Processing and Reconstruction Takeda, H., S. Farsiu, and P. Milanfar, "Robust Kernel Regression for Restoration and Reconstuction of Images from Sparse Noisy Data", Proceedings of the International Conference on Image Processing (ICIP), Atlanta, GA, October 2006. Takeda, H., S. Farsiu, and P. Milanfar, "Robust Kernel Regression for Restoration and Reconstuction of Images from Sparse Noisy Data", Proceedings of the International Conference on Image Processing (ICIP), Atlanta, GA, October 2006.Robust Kernel Regression for Restoration and Reconstuction of Images from Sparse Noisy DataRobust Kernel Regression for Restoration and Reconstuction of Images from Sparse Noisy Data Tomasi, C. and R. Manduchi, "Bilateral Filtering for Gray and Color Images", Proceedings of the 1998 IEEE International Conference on Computer Vision, Bombay, India. Tomasi, C. and R. Manduchi, "Bilateral Filtering for Gray and Color Images", Proceedings of the 1998 IEEE International Conference on Computer Vision, Bombay, India.