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Haro, G., Randall G., & Sapiro G. (2008).  Translated poisson mixture model for stratification learning. International journal of computer vision. 80, 358–374.
Vitoria, P., & Ballester C. (2018).  Towards Artifact-free Images through Perceptual Image Restoration. Poster presentation. In: ECCV 2018 Workshop and Challenge on Perceptual Image Restoration and Manipulation. September 8-14, 2018.
Caselles, V., Chambolle A., & Novaga M. (2010).  Total Variation in Imaging. Handbook of Mathematical Methods in Imaging, Springer Verlag.
Ballester, C., & Gonzalez M. (1996).  Texture segmentation by variational methods. ICAOS'96 Images, Wavelets and PDE's. 219, 187–193.
Ballester, C., & Gonzalez M. (1995).  Texture discrimination by variational methods. Second European Conference on Image Processing, Proceedings pp. 251-306.
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Bernot, M., Caselles V., & Morel J. M. (2008).  The structure of branched transportation networks. Calculus of Variations and Partial Differential Equations. 32, 279–317.
Hervieu, A., Papadakis N., Bugeau A., Gargallo P., & Caselles V. (2011).  Stereoscopic Image Inpainting Using Scene Geometry.. IEEE International Conference on Multimedia and EXPO (ICME),.
Vitoria, P., Fedorov V., & Ballester C. (2017).  Spatio-temporal tube segmentation through a video metrics-based patch similarity measure. Proceedings of the 19th Irish Machine Vision and Image Processing confence (IMVIP 2017), pp. 147-154.
Oliver, M., P.Palomares R., Ballester C., & Haro G. (2017).  Spatio-temporal binary video inpainting via threshold dynamics. International Conference on Acoustics, Speech, and Signal Processing (ICASSP).
Liu, Y., & Caselles V. (2010).  Spatial string matching for image classification. International Conference on Pattern Recognition (ICPR 2010).
Haro, G. (2014).  Shape from silhouette consensus and photo-consistency. IEEE International Conference on Image Processing.
Haro, G. (2012).  Shape from silhouette consensus. Pattern Recognition. 45(9), 
Vitoria, P., Sintes J., & Ballester C. (2018).  Semantic Image Inpainting Through Improved Wasserstein Generative Adversarial Networks. accepted as an oral presentation in International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP) 2019.
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