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El. knyga: Mathematical Methods in Image Processing and Inverse Problems: IPIP 2018, Beijing, China, April 21-24

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In honor of Raymond Chan IPIP 2018, Beijing, China, April 21-24

This book contains eleven original and survey scientific research articles arose from presentations given by invited speakers at International Workshop on Image Processing and Inverse Problems, held in Beijing Computational Science Research Center, Beijing, China, April 21–24, 2018. The book was dedicated to Professor Raymond Chan on the occasion of his 60th birthday.

The contents of the book cover topics including image reconstruction, image segmentation, image registration, inverse problems and so on. Deep learning, PDE, statistical theory based research methods and techniques were discussed. The state-of-the-art developments on mathematical analysis, advanced modeling, efficient algorithm and applications were presented. The collected papers in this book also give new research trends in deep learning and optimization for imaging science. It should be a good reference for researchers working on related problems, as well as for researchers working on computer vision and visualization, inverse problems, image processing and medical imaging.

C. Wang, R. Chan, R. Plemmons, Sudhakar Prasad,Point Spread Function
Engineering for 3D Imaging of Space Debris using a Continuous Exact $\ell_0$
Penalty (CEL0) Based Algorithm\.- S. Wei, S. Leung, An Adjoint State Method
for a Schr\"odinger Inverse Problem.- Ke Chen, On A New Diffeomorphic
Multi-modality Image Registration Model and Its Convergent Gauss-Newton
Solver.- Y. He, M. Huska, S. Ha Kang, H. Liu, Fast Algorithms for Surface
Reconstruction from Point Cloud.- H. Pan, Y. Wen, A Total Variation
Regularization Method for Inverse Source Problem with Uniform Noise.- S.
Morigi, A. Lanza, F. Sgallari, Automatic Parameter Selection Based on
Residual Whiteness for Convex non-convex Variational Restoration.- Michael
Ng, M. Qiao, Total Variation Gamma Correction Method for Tone Mapped HDR
Images.- X. Yuan, On the Optimal Proximal Parameter of an ADMM-like Splitting
Method for Separable Convex Programming.- X. Ding, H. Yang, R. Chan, Hui Hu,
Y. Peng, T. Zeng, A newinitialization method for neural networks with weight
sharing.- S.-Nee Chow, Jun Lu, H. Zhou, The Shortest path amid 3-D polyhedral
obstacles.- Y. Chen, J. Wan, Multigrid Methods for Image Registration Model
based on Optimal Mass Transport.
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