Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Pyramid-based texture analysis/synthesis
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Total Variation Wavelet Inpainting
Journal of Mathematical Imaging and Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Submodular Approximation: Sampling-based Algorithms and Lower Bounds
FOCS '08 Proceedings of the 2008 49th Annual IEEE Symposium on Foundations of Computer Science
Image Inpainting Using Structure-Guided Priority Belief Propagation and Label Transformations
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Region filling and object removal by exemplar-based image inpainting
IEEE Transactions on Image Processing
Image Completion Using Efficient Belief Propagation Via Priority Scheduling and Dynamic Pruning
IEEE Transactions on Image Processing
Statistics of patch offsets for image completion
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
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MRF models have shown state-of-the-art performance for many computer vision tasks. In this work, we propose a non-local MRF model for image completion problem. The goal of image completion is to fill user specified "target" region with patches of "source" regions in a way that is visually plausible to an observer. We represent the patches in the target region of the image as random variables in an MRF, and introduce a novel energy function on these variables. Each variable takes a label from a label set which is a collection of patches of the source region. The quality of the image completion is determined by the value of the energy function. The non-locality in the MRF is achieved through long range pairwise potentials. These long range pairwise potentials are defined to capture the inherent repeating patterns present in heritage architectural images. We minimize this energy function using Belief Propagation to obtain globally optimal image completion. We have tested our method on a wide variety of images and shown superior performance over previously published results for this task.