Sharpening and Enhancement 


PDE and variational approaches for stable sharpening and denoising.

Main topics

  • Image sharpening using forward-and-backward diffusion (simultaneous sharpening and denoising) – enhancing edges while keeping low noise.
  • Complex diffusion processes – using the imaginary part which imarges to be a stable nonlinear edge detector to guide the diffusion process.
  • Inverse scale space –  a generalization of Bregman iterations from a variational to a new non-standard PDE formulation (with Burger, Osher and Xu).

Complex diffusion real and imaginary kernels


Relaxed inverse scale space


Related papers

  1. E. Hait, G. Gilboa, “Blind facial image quality enhancement using non-rigid semantic patches.” IEEE Transactions on Image Processing Vol. 26, No. 6, pp. 2705-2720, 2017.
  2. M Benning, M. Moeller, R. Nossek, M. Burger, D. Cremers, G. Gilboa, C. Schoenlieb, “Nonlinear Spectral Image Fusion”, In International Conference on Scale Space and Variational Methods in Computer Vision (SSVM), 2017.
  3. O. Spier, T. Treibitz, G. Gilboa, “In situ target-less calibration of turbid media”, Int. Conf. on Computational Photography (ICCP), Stanford Univ., 2017.
  4. D. Horesh, G. Gilboa, “Separating surfaces for structure-texture decomposition using the TV transform”, IEEE Trans. Image Processing, Vol. 25, No. 9, pp. 4260 – 4270, 2016.
  5. O. Katzir, G. Gilboa, “A Maximal Interest-Point Strategy Applied to Image Enhancement with External Priors”, Proc. IEEE Int. Conf. Image Processing (ICIP), 2015.
  6. G. Gilboa, “Nonlinear Scale Space with Spatially Varying Stopping Time”, PAMI, Vol. 30, No. 12, pp. 2175-2187, 2008.
  7. M. Welk, G. Gilboa, J. Weickert, “Theoretical foundations for discrete forward-and-backward diffusion filtering”. SSVM 2009, pp. 527-538, 2009.
  8. M. Burger, G. Gilboa, S. Osher, J. Xu, , “Nonlinear inverse scale space methods”, Communications in Mathematical Sciences (CMS) Vol 4, No.1, pp. 179-212, 2006.
  9. G. Gilboa, N. Sochen, Y.Y. Zeevi, “Image sharpening by flows based on triple well potentials”,  J. of Math. Imaging and Vision, 20:121-131, 2004.
  10. G. Gilboa, N. Sochen, Y.Y. Zeevi, “Image enhancement and denoising by complex diffusion processes”, IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), Vol. 26, No. 8, pp. 1020-1036, 2004.
  11. G. Gilboa, N. Sochen, Y.Y. Zeevi, “Forward-and-Backward diffusion processes for adaptive image enhancement and denoising”, IEEE Trans. on Image Processing, Vol. 11, No. 7, pp. 689-703, 2002.
  12. G. Gilboa, N. Sochen, Y.Y. Zeevi, “Regularized shock filters and complex diffusion”,  – ECCV-’02, LNCS 2350, pp. 399-313, Springer-Verlag 2002.
  13. G. Gilboa, N. Sochen, Y.Y. Zeevi, “Image enhancement segmentation and denoising by time dependent nonlinear diffusion processes”, Proc. IEEE ICIP-’01, Thessaloniki, Greece, vol. 3, pp. 134-137, 2001.
  14. M. Burger, S. Osher, J. Xu, G. Gilboa, “Nonlinear Inverse Scale Space Methods for Image Restoration”, Variational and Level-Set Methods (VLSM) 2005,  LNCS 3752, pp. 25-36, Springer-Verlag, 2005.
  15. G. Gilboa, N. Sochen, Y.Y. Zeevi, “Complex diffusion processes for image filtering”, Scale-Space ’01, LNCS 2106, pp. 299-307, Springer-Verlag 2001.
  16. G. Gilboa, N. Sochen, Y.Y. Zeevi, “Resolution enhancement by forward-and-backward nonlinear diffusion processes”, Nonlinear Signal and Image Processing, Baltimore, Maryland, June 2001.
  17. N. Sochen, G. Gilboa, Y.Y. Zeevi, “Color image enhancement by a forward-and-backward adaptive Beltrami flow”, AFPAC-2000, LNCS 1888, pp. 319-328, 2000, Springer-Verlag.
  18. G. Gilboa, Y.Y. Zeevi, N. Sochen, “Signal and image enhancement by a generalized forward-and-backward adaptive diffusion process”, EUSIPCO-2000, Tampere, Finland, Sept. 2000.
  19. G. Gilboa, Y.Y. Zeevi, N. Sochen, “Anisotropic selective inverse diffusion for signal enhancement in the presence of noise”, Proc. IEEE ICASSP-2000, Istanbul, Turkey, vol. I, pp. 211-224, June 2000.

Research Keywords