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SUSAN: structure-preserving noise reduction

SUSAN: structure-preserving noise reduction. EE264: Image Processing Final Presentation by Luke Johnson 6/7/2007. SUSAN Principle. Published by Stephen M. Smith and J. Michael Brady (1997) USAN Univalue Segment Assimilating Nucleus Image is assumed to be made up of features

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SUSAN: structure-preserving noise reduction

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  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) • USAN • Univalue Segment Assimilating Nucleus • Image is assumed to be made up of features • 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 Shaded area = USAN

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

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

  5. USAN determination • Binary comparison: • Gaussian comparison: • 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 • Spatial Gaussian weighting: • σis 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.

  7. Averaging • Apply both weighting functions and average: • 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 • 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) residual (contrast enhanced) two iterations (rms = 2.80)

  11. with impulse noise (rms = 24.8) two iterations of SUSAN filter (rms = 5.72) residual 3x3 median filter (rms = 6.81)

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

  13. 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)

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

  15. Film Grain Noise image corrupted by film grain noise Results of SUSAN filter Results of bilateral filter

  16. Conclusions • 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. • More recent denoising algorithms have surpassed SUSAN in performance however many of them use the same general ideas as the SUSAN filter

  17. References • 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. • 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. • Takeda, H., "Kernel Regression for Image Processing and Reconstruction", M.S. Thesis, Electrical Engineering, UC Santa Cruz, March 2006. • 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, "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. • Tomasi, C. and R. Manduchi, "Bilateral Filtering for Gray and Color Images", Proceedings of the 1998 IEEE International Conference on Computer Vision, Bombay, India.

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