Affine Invariant Self-Similarity for Exemplar-Based Inpainting

Vadim Fedorov (1) , Pablo Arias (1) , Gabriele Facciolo (2, 3, 4) , Coloma Ballester (1)

1 DITIC, universitat Pompeu Fabra
2 imagine [Marne-la-Vallée]
3 ENPC - École des Ponts ParisTech
4 LIGM - Laboratoire d'Informatique Gaspard-Monge

This paper presents a new method for exemplar-based image inpainting using transformed patches. We build upon a recent affine invariant self-similarity measure which automatically transforms patches to compare them in an appropriate manner. As a consequence, it intrinsically extends the set of available source patches to copy information from. When comparing two patches, instead of searching for the appropriate patch transformation in a highly dimensional parameter space, our approach allows us to determine a single transformation from the texture content in both patches. We incorporate the affine invariant similarity measure in a variational formulation for inpainting and present an algorithm together with experimental results illustrating this approach.

Domains: Image Processing [eess.IV]; Signal and Image Processing; Modeling and Simulation; Computer Vision and Pattern Recognition [cs.CV]

Vadim Fedorov, Pablo Arias, Gabriele Facciolo, Coloma Ballester. Affine Invariant Self-Similarity for Exemplar-Based Inpainting. VISAPP, 2016, Rome, Italy. ⟨hal-01240846⟩