Image processing and analysis - variational, PDE, wavelet, and stochastic methods

By Tony F. Chan, Jianhong Shen. 2005

1,106 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ea20cd90beb7c9105d482806caed5f4ca4087a68

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In Computer Vision and Pattern Recognition

Citations in the corpus

A Nonlinear Inverse Scale Space Method for a Convex Multiplicative Noise Model (2008, methodology): “In fact it yields improved denoising results among PDE-based methods [4, 5, 6, 9, 21, 23].”
Weighted Nuclear Norm Minimization and Its Applications to Low Level Vision (2016, methodology): “We compare WNNM-MC with NNM-MC and several representative and state-of-the-art inpainting methods, including the TV method (Chan and Shen, 2005), FOE method14 (Roth and Black, 2009), variational nonlocal example-based (VNL) method15 (Arias et al, 2011) and the beta process dictionary learning…”
From Local Kernel to Nonlocal Multiple-Model Image Denoising (2009, background): “A broad overview of these connections can be seen in the book by Chan and Shen (2005).”
Convergence Analysis of Primal-Dual Algorithms for a Saddle-Point Problem: From Contraction Perspective (2012, background): “Image inpainting refers to ﬁlling in missing or damaged regions in images either in the pixel domain or in a transformed domain, and it plays pivotal roles in many image precessing tasks; see, e.g., [3, 8].”
Adaptive total variation image deblurring: A majorization-minimization approach (2009, background): “To obtain meaningful image estimates, some form of regularization (prior knowledge, from a Bayesian viewpoint) has to be enforced to penalize " undesirable " solutions [1] [2] [3].”
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations (2017, methodology): “On a side note, differential equations is one of the most powerful tools used in low-level computer vision such as image denoising, deblurring, registration and segmentation (Osher & Paragios, 2003; Aubert & Kornprobst, 2006; Chan & Shen, 2005).”
This is SPIRAL-TAP: Sparse Poisson Intensity Reconstruction ALgorithms—Theory and Practice (2010, background): “Regularization based on a total variation (TV) seminorm has also garnered signiﬁcant recent attention (cf. [29], [30]).”
Nonlocal Discrete Regularization on Weighted Graphs: A Framework for Image and Manifold Processing (2008, background): “However, there are some limitations to the functionals used in regularization such as total variational ones [2].”