Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Gradient domain high dynamic range compression
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
ACM SIGGRAPH 2003 Papers
An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
ACM SIGGRAPH 2004 Papers
Videoshop: A New Framework for Spatio-Temporal Video Editing in Gradient Domain
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Texture optimization for example-based synthesis
ACM SIGGRAPH 2005 Papers
On the Removal of Shadows from Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
GradientShop: A gradient-domain optimization framework for image and video filtering
ACM Transactions on Graphics (TOG)
Error-tolerant image compositing
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
A Variational Framework for Exemplar-Based Image Inpainting
International Journal of Computer Vision
Image Completion Using Efficient Belief Propagation Via Priority Scheduling and Dynamic Pruning
IEEE Transactions on Image Processing
Using photographs to enhance videos of a static scene
EGSR'07 Proceedings of the 18th Eurographics conference on Rendering Techniques
Towards fast, generic video inpainting
Proceedings of the 10th European Conference on Visual Media Production
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In the context of video editing, enforcing spatio-temporal consistency is an important issue. With that purpose, the current variational models for gradient domain video editing include space and time regularization terms. The spatial terms are based on the usual space derivatives, the temporal ones are based on the convective derivative, and both are balanced by a parameter ß. However, the usual discretizations of the convective derivative limit the value of ß to a certain range, thus limiting these models from achieving their full potential. In this paper, we propose a new numerical scheme to compute the convective derivative, the deblurring convective derivative, which allows us to lift this constraint. Moreover, the proposed scheme introduces less errors than other discretization schemes without adding computational complexity. We use this scheme in the implementation of two gradient domain models for temporally consistent video editing, based on Poisson and total variation type formulations, respectively. We apply these models to three video editing tasks: inpainting correction, object insertion and object removal.