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April  2017, 11(2): 247-262. doi: 10.3934/ipi.2017012

## A two-step mixed inpainting method with curvature-based anisotropy and spatial adaptivity

 1 Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(ⅰ), FICH-UNL/CONICET, Argentina 2 Ciudad Universitaria, CC 217, Ruta Nac. No 168, km 472.4, (3000) Santa Fe, Argentina 3 Instituto de Matemática Aplicada del Litoral, IMAL, CONICET-UNL, Centro Científico 4 Tecnológico CONICET Santa Fe, Colectora Ruta Nac. 168, km 472, Paraje "El Pozo", 3000, Santa Fe, Argentina and Departamento de Matemática 5 Facultad de Ingeniería Química, Universidad Nacional del Litoral, Santa Fe, Argentina

* Corresponding author: R. D. Spies

Received  December 2015 Revised  October 2016 Published  March 2017

The image inpainting problem consists of restoring an image from a (possibly noisy) observation, in which data from one or more regions are missing. Several inpainting models to perform this task have been developed, and although some of them perform reasonably well in certain types of images, quite a few issues are yet to be sorted out. For instance, if the image is expected to be smooth, the inpainting can be made with very good results by means of a Bayesian approach and a maximum a posteriori computation [2]. For non-smooth images, however, such an approach is far from being satisfactory. Even though the introduction of anisotropy by prior smooth gradient inpainting to the latter methodology is known to produce satisfactory results for slim missing regions [2], the quality of the restoration decays as the occluded regions widen. On the other hand, Total Variation (TV) inpainting models based on high order PDE diffusion equations can be used whenever edge restoration is a priority. More recently, the introduction of spatially variant conductivity coefficients on these models, such as in the case of Curvature-Driven Diffusion (CDD) [4], has allowed inpainted images with well defined edges and enhanced object connectivity. The CDD approach, nonetheless, is not quite suitable wherever the image is smooth, as it tends to produce piecewise constant restorations.

In this work we present a two-step inpainting process. The first step consists of using a CDD inpainting to build a pilot image from which to infer a-priori structural information on the image gradient. The second step is inpainting the image by minimizing a mixed spatially variant anisotropic functional, whose weight and penalization directions are based upon the aforementioned pilot image. Results are presented along with comparison measures in order to illustrate the performance of this inpainting method.

Citation: Francisco J. Ibarrola, Ruben D. Spies. A two-step mixed inpainting method with curvature-based anisotropy and spatial adaptivity. Inverse Problems & Imaging, 2017, 11 (2) : 247-262. doi: 10.3934/ipi.2017012
##### References:
 [1] R. Acar and C. R. Vogel, Analysis of bounded variation penalty methods for ill-posed problems, Inverse Problems, 10 (1994), 1217-1229.  doi: 10.1088/0266-5611/10/6/003.  Google Scholar [2] D. Calvetti, F. Sgallari and E. Somersalo, Image inpainting with structural boostrap priors, Image and Vision Computing, 24 (2006), 782-793.  doi: 10.1016/j.imavis.2006.01.015.  Google Scholar [3] T. Chan, S. Kang and J. Shen, Euler's elastica and curvature based inpaintings, SIAM J. on Applied Mathematics, 63 (2002), 564-592.  doi: 10.1137/S0036139901390088.  Google Scholar [4] T. Chan and J. Shen, Mathematical models for local nontexture inpaintings, SIAM J. on Applied Mathematics, 62 (2002), 1019-1043.  doi: 10.1137/S0036139900368844.  Google Scholar [5] F. Ibarrola and R. Spies, Image restoration with a half-quadratic approach to mixed weighted smooth and anisotropic bounded variation regularization, SOP Transactions on Applied Mathematics, 1 (2014), 57-95.   Google Scholar [6] J. Idier, Bayesian Approach to Inverse Problems, John Wiley & Sons, 2008. doi: 10.1002/9780470611197.  Google Scholar [7] F. Li, Z. Li and L. Pi, Variable exponent functionals in image restoration, Applied Mathematics and Computation, 216 (2010), 870-882.  doi: 10.1016/j.amc.2010.01.094.  Google Scholar [8] G. L. Mazzieri, R. D. Spies and K. G. Temperini, Mixed spatially varying $L^2$-$BV$ regularization of inverse ill-posed problems, Journal of Inverse and Ill-posed Problems, 23 (2015), 571-585.  doi: 10.1515/jiip-2014-0034.  Google Scholar [9] R. Rockafellar, Convex Analysis, Princeton University Press, 1970.   Google Scholar [10] L. I. Rudin, S. Osher and E. Fatemi, Nonlinear total variation based noise removal algorithms (proceedings of the 11th annual international conference of the center for nonlinear studies), Physica D, 60 (1992), 259-268.  doi: 10.1016/0167-2789(92)90242-F.  Google Scholar

show all references

##### References:
 [1] R. Acar and C. R. Vogel, Analysis of bounded variation penalty methods for ill-posed problems, Inverse Problems, 10 (1994), 1217-1229.  doi: 10.1088/0266-5611/10/6/003.  Google Scholar [2] D. Calvetti, F. Sgallari and E. Somersalo, Image inpainting with structural boostrap priors, Image and Vision Computing, 24 (2006), 782-793.  doi: 10.1016/j.imavis.2006.01.015.  Google Scholar [3] T. Chan, S. Kang and J. Shen, Euler's elastica and curvature based inpaintings, SIAM J. on Applied Mathematics, 63 (2002), 564-592.  doi: 10.1137/S0036139901390088.  Google Scholar [4] T. Chan and J. Shen, Mathematical models for local nontexture inpaintings, SIAM J. on Applied Mathematics, 62 (2002), 1019-1043.  doi: 10.1137/S0036139900368844.  Google Scholar [5] F. Ibarrola and R. Spies, Image restoration with a half-quadratic approach to mixed weighted smooth and anisotropic bounded variation regularization, SOP Transactions on Applied Mathematics, 1 (2014), 57-95.   Google Scholar [6] J. Idier, Bayesian Approach to Inverse Problems, John Wiley & Sons, 2008. doi: 10.1002/9780470611197.  Google Scholar [7] F. Li, Z. Li and L. Pi, Variable exponent functionals in image restoration, Applied Mathematics and Computation, 216 (2010), 870-882.  doi: 10.1016/j.amc.2010.01.094.  Google Scholar [8] G. L. Mazzieri, R. D. Spies and K. G. Temperini, Mixed spatially varying $L^2$-$BV$ regularization of inverse ill-posed problems, Journal of Inverse and Ill-posed Problems, 23 (2015), 571-585.  doi: 10.1515/jiip-2014-0034.  Google Scholar [9] R. Rockafellar, Convex Analysis, Princeton University Press, 1970.   Google Scholar [10] L. I. Rudin, S. Osher and E. Fatemi, Nonlinear total variation based noise removal algorithms (proceedings of the 11th annual international conference of the center for nonlinear studies), Physica D, 60 (1992), 259-268.  doi: 10.1016/0167-2789(92)90242-F.  Google Scholar
(a) Occluded image; (b) T1 inpainting; (c) Anistoropic T1 inpainting
Occluded image (a); TV inpainting (b)
(a) Occluded image; (b) TV inpainting
(a) Occluded image; (b) CDD inpainting
(a) Occluded image; (b) Isotropic T1 inpainting; (c) CDD inpainting; (d) Mixed anisotropic T1-TV inpainting
(a) Occluded noisy image; (b) Isotropic T1 inpainting; (c) CDD inpainting; (d) Anisotropic T1-TV inpainting
(a) Occluded noisy image; (b) Isotropic T1 inpainting; (c) CDD inpainting; (d) Anisotropic T1-TV inpainting
Left: CKS inpaintings; right: Anisotropic T1-TV inpaintings
(a) Original image; (b) Occluded (masked) image; (c) T1 inpainting; (d) CDD inpainting; (e) T1-TV inpainting
PSNR values for the test image (Figure 5)
 Isotropic T1 CDD Anisotropic T1-TV $\mathit{PSNR}$ 20.140 35.497 36.329
 Isotropic T1 CDD Anisotropic T1-TV $\mathit{PSNR}$ 20.140 35.497 36.329
$\mathit{PSNR}$ values for grayscale image (Figure 6)
 Isotropic T1 CDD Anisotropic T1-TV $\mathit{PSNR}$ 29.127 29.952 30.868
 Isotropic T1 CDD Anisotropic T1-TV $\mathit{PSNR}$ 29.127 29.952 30.868
$\mathit{PSNR}$ values for color image (Figure 7)
 Isotropic T1 CDD Anisotropic T1-TV $\mathit{PSNR}$ 20.568 21.360 22.112
 Isotropic T1 CDD Anisotropic T1-TV $\mathit{PSNR}$ 20.568 21.360 22.112
$\mathit{PSNR}$ values for color image (Figure 7)
 Gray CKS Gray A T1-TV Color CKS Color A T1-TV $\mathit{PSNR}$ 29.770 32.421 21.464 24.284
 Gray CKS Gray A T1-TV Color CKS Color A T1-TV $\mathit{PSNR}$ 29.770 32.421 21.464 24.284
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