1,Pierre Buyssens, David Tschumperlé and Olivier Lézoray GREYC - CNRS UMR 6072 Image team Université de Caen Normandie, FRANCE SIGGRAPH Asia 2015 2nd November 2015 1. This research was supported by French national grant Action 3DS
hypothesis for artifact locations • local sharp variations in the inpainted image • source patches come from very different locations PA = k r I k . div ( ) : inpainting correspondence map published in CAIP 2013 : "Spatial Patch Blending for Artefact Reduction in Pattern-Based Inpainting Techniques" M. Daisy (GREYC, Caen - FRANCE) SIGGRAPH Asia 2015 2nd November 2015 7 / 32
hypothesis for artifact locations • local sharp variations in the inpainted image • source patches come from very different locations PA = k r I k . div ( ) : inpainting correspondence map 2 Strongest artifact locations LA = { p | PA( p ) > ⌧} published in CAIP 2013 : "Spatial Patch Blending for Artefact Reduction in Pattern-Based Inpainting Techniques" M. Daisy (GREYC, Caen - FRANCE) SIGGRAPH Asia 2015 2nd November 2015 7 / 32
hypothesis for artifact locations • local sharp variations in the inpainted image • source patches come from very different locations PA = k r I k . div ( ) : inpainting correspondence map 2 Strongest artifact locations LA = { p | PA( p ) > ⌧} 3 Blending amplitude map ( p ) = ⇢ . P q 2 LA w ( p , q ) max r 2I P q 2 LA w ( p , q ) with w ( p , q ) = exp ⇣ k p q k2 PA( q )2 ⌘ (1) published in CAIP 2013 : "Spatial Patch Blending for Artefact Reduction in Pattern-Based Inpainting Techniques" M. Daisy (GREYC, Caen - FRANCE) SIGGRAPH Asia 2015 2nd November 2015 7 / 32
( p ) = P q 2 p w ( p , q ) . q ( p q ) P q 2 p w ( q , p ) (2) • w ( p , q ) = exp ⇣ k p q k2 ( p )2 ⌘ • p = { q | q \ p 6= ?} ! one gaussian function for each possible blending amplitude ) quite slow M. Daisy (GREYC, Caen - FRANCE) SIGGRAPH Asia 2015 2nd November 2015 8 / 32
"A Fast Spatial Patch Blending Algorithm for Artefact Reduction in Pattern-based Image Inpainting" M. Daisy (GREYC, Caen - FRANCE) SIGGRAPH Asia 2015 2nd November 2015 9 / 32
Multiscale scheme SIGGRAPH ASIA 2013 Fast Spatial Patch Blending + + Anisotropic model CAIP 2015 Fast Geometry-guided Patch Blending + + Video inpainting SIGGRAPH Asia 2015 Space-time Geometry-guided Patch Blending M. Daisy (GREYC, Caen - FRANCE) SIGGRAPH Asia 2015 2nd November 2015 13 / 32
p ) i = S( p ) max p 2I S( p ) i 2 Blending tensors eigen values computation B i = 1 ( 1 + ˆ S 1 + ˆ S 2 + ˆ S 3 ) i M. Daisy (GREYC, Caen - FRANCE) SIGGRAPH Asia 2015 2nd November 2015 19 / 32
p ) i = S( p ) max p 2I S( p ) i 2 Blending tensors eigen values computation B i = 1 ( 1 + ˆ S 1 + ˆ S 2 + ˆ S 3 ) i 3 Blending tensor building B = s B 1 e? S 1 .T e? S 1 + s B 2 e? S 2 .T e? S 2 | {z } spatial term + t Be? S t .T e? S t | {z } temporal term (3) • eS i : structure tensor eigen vectors • s1 , 2 = s B 1 , 2 t = t B 3 • s , t : spatial (resp. temporal) blending bandwidth M. Daisy (GREYC, Caen - FRANCE) SIGGRAPH Asia 2015 2nd November 2015 19 / 32
using Eq. (2) with w ( p , q ) = 8 > < > : exp ⇣ k p q k2 ( p )2 ⌘ isotropic expXT B( p ) 1 X anisotropic where X = q p M. Daisy (GREYC, Caen - FRANCE) SIGGRAPH Asia 2015 2nd November 2015 22 / 32
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