ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
PatchMatch: a randomized correspondence algorithm for structural image editing
ACM SIGGRAPH 2009 papers
A Variational Framework for Non-local Image Inpainting
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
A Learning-Based Framework for Low Bit-Rate Image and Video Coding
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
IEEE Transactions on Image Processing
Hierarchical region-based representation for segmentation and filtering with depth in single images
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Image inpainting by patch propagation using patch sparsity
IEEE Transactions on Image Processing
Towards more efficient and effective LP-based algorithms for MRF optimization
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Image inpainting based on probabilistic structure estimation
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
A Variational Framework for Exemplar-Based Image Inpainting
International Journal of Computer Vision
Image inpainting with salient structure completion and texture propagation
Pattern Recognition Letters
Temporally consistent gradient domain video editing
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Virtual restoration of the ghent altarpiece using crack detection and inpainting
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
A mixed reality system for virtual glasses try-on
Proceedings of the 10th International Conference on Virtual Reality Continuum and Its Applications in Industry
Iterative gradient-driven patch-based inpainting
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II
Geometrically Guided Exemplar-Based Inpainting
SIAM Journal on Imaging Sciences
Quality prediction for image completion
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Statistics of patch offsets for image completion
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
A non-local MRF model for heritage architectural image completion
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Rectangling panoramic images via warping
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
An efficient framework for image/video inpainting
Image Communication
Spot the differences: from a photograph burst to the single best picture
Proceedings of the 21st ACM international conference on Multimedia
Pattern Recognition Letters
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In this paper, a new exemplar-based framework is presented, which treats image completion, texture synthesis, and image inpainting in a unified manner. In order to be able to avoid the occurrence of visually inconsistent results, we pose all of the above image-editing tasks in the form of a discrete global optimization problem. The objective function of this problem is always well-defined, and corresponds to the energy of a discrete Markov random field (MRF). For efficiently optimizing this MRF, a novel optimization scheme, called priority belief propagation (BP), is then proposed, which carries two very important extensions over the standard BP algorithm: ldquopriority-based message schedulingrdquo and ldquodynamic label pruning.rdquo These two extensions work in cooperation to deal with the intolerable computational cost of BP, which is caused by the huge number of labels associated with our MRF. Moreover, both of our extensions are generic, since they do not rely on the use of domain-specific prior knowledge. They can, therefore, be applied to any MRF, i.e., to a very wide class of problems in image processing and computer vision, thus managing to resolve what is currently considered as one major limitation of the BP algorithm: its inefficiency in handling MRFs with very large discrete state spaces. Experimental results on a wide variety of input images are presented, which demonstrate the effectiveness of our image-completion framework for tasks such as object removal, texture synthesis, text removal, and image inpainting.