Nontexture Inpainting by Curvature-Driven Diffusions

By Tony F. Chan, Jianhong Shen. Journal of Visual Communication and Image Representation, 2001

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Kernel Regression for Image Processing and Reconstruction (2007, methodology): “Besides inpainting applications [1], interpolation of irregularly sampled image data is essential for applications such as multiframe super-resolution, where several”
Fast Anisotropic Smoothing of Multi-Valued Images using Curvature-Preserving PDE's (2006, methodology): “Choosing different smoothing functions f−, f and diffusion PDE’s detailed below leads to the unification o f m st unconstrained image regularization methods proposed in th e literature [1, 4, 7, 8, 13, 15, 16, 23, 28, 30, 33, 36, 37, 38].”
Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification (2001, background): “Various approaches have been proposed to restore these images [4], [8], [20], [24].”
Missing Information Reconstruction of Remote Sensing Data: A Technical Review (2015, background): “, the curvature-driven diffusion (CDD) models [9], high-order TV [30], and spectral-spatial adaptive TV [31].”
Recurrent Feature Reasoning for Image Inpainting (2020, background): “Image Inpainting Traditionally, inpainting algorithms attempt to find patches from the background area to inpaint the hole [1, 2, 3, 5, 12, 23, 7].”
Image Inpainting by Patch Propagation Using Patch Sparsity (2010, methodology): “Then a curvature-driven diffusion equation was proposed to realize the connectivity principle which does not hold in the TV model [4].”