| Abstract: |
| Euler elastica (EE), as a regulariser for the curvature and length of the image surface`s level lines, can effectively suppress the staircase artifacts of traditional regulariser and has attracted lots of attention in image processing. However, developing fast and stable algorithms for optimizing the EE energy is a great challenge due to its nonconvexity, strong nonlinearity, and singularity. This talk will present a novel, fast, globally convergent hybrid alternating minimization method (HALM) algorithm for the Euler elastica model based on a bilinear decomposition. The HALM algorithm comprises three sub-minimization problems, and each is either solved in the closed form or approximated by fast solvers, making the new algorithm highly accurate and efficient. Numerical experiments show that the new algorithm produces good results with much-improved efficiency compared to other state-of-the-art algorithms for the EE model. This work is joint with Baochen Sun, Xue-Cheng Tai, Qi Wang, and Huibin Chang. |
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