Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Fragment-based image completion
ACM SIGGRAPH 2003 Papers
Dense Matching of Multiple Wide-baseline Views
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Feature matching and deformation for texture synthesis
ACM SIGGRAPH 2004 Papers
Image completion with structure propagation
ACM SIGGRAPH 2005 Papers
Efficient Belief Propagation for Early Vision
International Journal of Computer Vision
Image Completion Using Global Optimization
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Scene completion using millions of photographs
ACM SIGGRAPH 2007 papers
Image completion based on views of large displacement
The Visual Computer: International Journal of Computer Graphics
Image inpainting by global structure and texture propagation
Proceedings of the 15th international conference on Multimedia
PatchMatch: a randomized correspondence algorithm for structural image editing
ACM SIGGRAPH 2009 papers
Image Repairing: robust image synthesis by adaptive ND tensor voting
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Region filling and object removal by exemplar-based image inpainting
IEEE Transactions on Image Processing
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A new image completion algorithm called Structural Priority Belief Propagation (SPBP) is presented to deal with LDV based image completion in this paper. LDV completion is a new form of image completion based on another large displacement view (LDV) of the same scene, no wonder, it has the potential of repairing large unknown region with salient structure information. In order to complete such unknown region, SPBP makes two important extensions over existing Priority-BP: dynamic weight of structural consistency and structural priority inheritance so as to propagate linear structure with correct priority, meanwhile it promotes texture propagation adhering to a global optimization scheme. Experimental results demonstrate that SPBP can obtain more satisfactory results than other LDV completion algorithms and it also performs well for traditional single image completion.