Image Inpainting is featured in the Science News (Vol. 161/19, 05/11/2002). Please read the article Filling in Blanks by Dr. Ivars Peterson based on the AMS annual conference at San Diego, Jan 2002.
Introduction: Inpainting is an artistic synonym for image interpolation, and has been circulated among museum restoration artists for a long time. The notion of digital inpainting was firstintroduced in the paper by Bertalmio-Sapiro-Caselles-Ballester (SIGGRAPH 2000). Smart digital inpainting models, techniques, and algorithms have broad applications in image interpolation, photo restoration, zooming and super-resolution, primal-sketch based perceptual image compression and coding, and the error concealment of (wireless) image transmission, etc.
Our approach is primarily based on the Bayesian (or equivalently, Helmholtz) philosophy of vision: an optimal guess of the complete ideal image from its incomplete and distorted data crucially relies on the answers to the two questions: (A) (Prior Model) what do we mean by ``images?" and (B) (Data Model) how have the observed data been generated from (or connected to) the original ideal image? We have developed our variational/PDE models and algorithms along this line of philosophy.
* BV Image Model and Inpainting of Noisy Blurred Images (March, 2002)
* Mumford-Shah-Euler Image Model for Digital Inpainting (Sept, 2001)
* Local Inpainting Models and TV (total variation) Inpainting (March, 2000)
* Non-Texture Inpainting by Curvature-Driven Diffusions (CDD) (Sept, 2000)
* Euler's Elastica and Curvature Based Inpainting (April, 2001)
* Landmark Based Inpainting from Multiple Views (March, 2002)
Chan and Shen , March, 2002: On the Role of the BV Image Model in Image Restoration . Dedicated to Stan Osher on the occasion of his 60th birthday.)
Selim Esedoglu and Jianhong Shen , European J. Appl. Math., 13, pp. 353-370, 2002.)
Report by Chan and Shen [SIAM Appl. Math, 62(3), 1019-1043, 2001]. (Wonder how this famous Kanizsa's "Entangled Man" illusion is related to the inpainting model? Find the answer in the paper.)
Report by Chan and Shen . [J. Visual Communication and Image Representation, 12(4), 436-449, 2001]
From the abstract : Most existing inpainting algorithms are local in nature and extrapolate information from neighboring pixels into the inpainting regions. In this paper, we are interested in the inpainting problem where the missing region are so large that these local inpainting methods fail. As an alternative to the local principle, we assume that there are other images with related global information to enable a reasonable inpainting. These additional images could be from a movie sequence, an image of the same object from a different time and a different viewpoint, or an image of a similar object.
Our method has roughly three phases: landmark matching, interpolation, and copying. For the landmark matching, modified shape context information is used to exploit the global information. Then matched information is interpolated (and regularized) using thin plate splines. Finally, we copy the information from one image to another. Using landmark matching and interpolation, allows the missing regions to be significantly larger compared to the local inpainting methods, and can be used when the object is distorted from one image to another. The experimental results are promising. (from CAM 02-11, March 2002) Report by Kang, Chan and Soatto [submitted to IEEE PAMI 2002]. Short version is also available at CAM 02-31, Proceedings of 3DPVT, June 2002
Reports on Image Restorations