Description
Book SynopsisThis book publishes a collection of original scientific research articles that address the state-of-art in using partial differential equations for image and signal processing. Coverage includes: level set methods for image segmentation and construction, denoising techniques, digital image inpainting, image dejittering, image registration, and fast numerical algorithms for solving these problems.
Table of ContentsDigital Image Inpainting, Image Dejittering, and Optical Flow Estimation.- Image Inpainting Using a TV-Stokes Equation.- Error Analysis for H1 Based Wavelet Interpolations.- Image Dejittering Based on Slicing Moments.- CLG Method for Optical Flow Estimation Based on Gradient Constancy Assumption.- Denoising and Total Variation Methods.- On Multigrids for Solving a Class of Improved Total Variation Based Staircasing Reduction Models.- A Method for Total Variation-based Reconstruction of Noisy and Blurred Images.- Minimization of an Edge-Preserving Regularization Functional by Conjugate Gradient Type Methods.- A Newton-type Total Variation Diminishing Flow.- Chromaticity Denoising using Solution to the Skorokhod Problem.- Improved 3D Reconstruction of Interphase Chromosomes Based on Nonlinear Diffusion Filtering.- Image Segmentation.- Some Recent Developments in Variational Image Segmentation.- Application of Non-Convex BV Regularization for Image Segmentation.- Region-Based Variational Problems and Normal Alignment – Geometric Interpretation of Descent PDEs.- Fast PCLSM with Newton Updating Algorithm.- Fast Numerical Methods.- Nonlinear Multilevel Schemes for Solving the Total Variation Image Minimization Problem.- Fast Implementation of Piecewise Constant Level Set Methods.- The Multigrid Image Transform.- Minimally Stochastic Schemes for Singular Diffusion Equations.- Image Registration.- Total Variation Based Image Registration.- Variational Image Registration Allowing for Discontinuities in the Displacement Field.- Inverse Problems.- Shape Reconstruction from Two-Phase Incompressible Flow Data using Level Sets.- Reservoir Description Using a Binary Level Set Approach with Additional Prior Information About the Reservoir Model.
