International Journal of Computer Vision
Prior Learning and Gibbs Reaction-Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
International Journal of Computer Vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Image quilting for texture synthesis and transfer
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
Minimax Entropy Principle and Its Application to Texture Modeling
Neural Computation
The Second Order Local-Image-Structure Solid
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scene completion using millions of photographs
ACM SIGGRAPH 2007 papers
Using the complex Ginzburg-Landau equation for digital inpainting in 2D and 3D
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Image inpainting by cooling and heating
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Filling-in by joint interpolation of vector fields and gray levels
IEEE Transactions on Image Processing
Region filling and object removal by exemplar-based image inpainting
IEEE Transactions on Image Processing
Special Issue on Tribute Workshop for Peter Johansen
Journal of Mathematical Imaging and Vision
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We present a novel algorithm for solving the image inpainting problem based on a field of locally interacting particle filters. Image inpainting, also known as image completion, is concerned with the problem of filling image regions with new visually plausible data. In order to avoid the difficulty of solving the problem globally for the region to be inpainted, we introduce a field of local particle filters. The states of the particle filters are image patches. Global consistency is enforced by a Markov random field image model which connects neighbouring particle filters. The benefit of using locally interacting particle filters is that several competing hypotheses on inpainting solutions are kept active, allowing the method to provide globally consistent solutions on problems where other local methods may fail. We provide examples of applications of the developed method.