The existing non-hybrid image inpainting techniques can be broadly classified into two types. One is the texture-based inpainting and the other is the structure-based inpainting. One critical drawback of those techniques is that their inpainting results are not effective for the images with a mixture of texture and structure features in terms of visual quality or processing time. However, the conventional hybrid inpainting algorithms, which aim at inpainting images with texture and structure features, do not effectively deal with the two items: (i) what is the most effective application order of the constituents? and (ii) how can one extract a minimal sub-image that may contain best candidates of inpainting source? In this study, the authors propose a new hybrid inpainting algorithm to address the two tasks fully and effectively. Precisely, the authors’ algorithm attempts to solve two key ingredients: (i) (right time) determining the best application order for inpainting textural and structural missing regions and (ii) (right place) extracting the sub-image containing best candidates of source patches to be used to fill in a target region.

References

1. 1)
  - 5. Telea, A.: ‘An image inpainting technique based on the fast marching method’, J. of Graph. Tools, 2004, 9, (1), pp. 25–36 (doi: 10.1080/10867651.2004.10487596).
2. 2)
  - 4. Chan, T.F., Shen, J.: ‘Local inpainting models and TV inpainting’, SIAM J. Appl. Math., 2001, 62, (3), pp. 1019–1043.
3. 3)
  - 18. Bugeau, A., Bertalmio, M., Caselles, V., Sapiro, G.: ‘A comprehensive framework for image inpainting’, IEEE Trans. Image Process., 2010, 19, (10), pp. 2634–2645 (doi: 10.1109/TIP.2010.2049240).
4. 4)
  - 3. Bertalmio, M., Bertozzi, A.L., Sapiro, G.: ‘Navier–Stokes, fluid dynamics, and image and video inpainting’. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), December 2001, pp. 355–362.
5. 5)
  - 12. Guillemot, C., Turkan, M., Le Meur, O., Ebdelli, M.: ‘Object removal and loss concealment using neighbor embedding methods’, Signal Process., Image Commun., 2013, 28, (10), pp. 1405–1419 (doi: 10.1016/j.image.2013.08.020).
6. 6)
  - 6. Qin, C., Wang, S., Zhang, X.: ‘Simultaneous inpainting for image structure and texture using anisotropic heat transfer model’, Multimedia Tools Appl., 2012, 56, (3), pp. 469–483 (doi: 10.1007/s11042-010-0601-4).
7. 7)
  - 15. Qin, C., Cao, F., Zhang, X.P.: ‘Efficient image inpainting using adaptive edge-preserving propagation’, Image Sci. J., 2011, 59, (4), pp. 211–218 (doi: 10.1179/1743131X10Y.0000000010).
8. 8)
  - 2. Bertalmio, M., Sapiro, G., Caselles, V., Ballester, C.: ‘Image inpainting’. ACM SIGGRAPH, July 2000, pp. 417–424.
9. 9)
  - 17. Wu, J., Ruan, Q.: ‘A novel hybrid image inpainting model’. Int. Conf. on Audio, Language and Image Processing (ICALIP), July 2008, pp. 138–142.
10. 10)
  - 14. Rane, S.D., Sapiro, G., Bertalmio, M.: ‘Structure and texture filling-in of missing image blocks in wireless transmission and compression applications’, IEEE Trans. Image Process., 2003, 12, (3), pp. 296–303 (doi: 10.1109/TIP.2002.804264).
11. 11)
  - 16. Bertalmio, M., Vese, L., Sapiro, G., Osher, S.: ‘Simultaneous structure and texture image inpainting’, IEEE Trans. Image Process., 2003, 12, (8), pp. 882–889 (doi: 10.1109/TIP.2003.815261).
12. 12)
  - 7. Getreuer, P.: ‘Total variation inpainting using split bregman’, Image Process. On Line, 2012, 2, (1), pp. 147–157 (doi: 10.5201/ipol.2012.g-tvi).
13. 13)
  - 1. Guillemot, C., Le Meur, O.: ‘Image inpainting: overview and recent advances’, IEEE Signal Process. Mag., 2014, 31, (1), pp. 127–144 (doi: 10.1109/MSP.2013.2273004).
14. 14)
  - 11. Xu, Z., Sun, J.: ‘Image inpainting by patch propagation using patch sparsity’, IEEE Trans. Image Process., 2010, 19, (5), pp. 1153–1165 (doi: 10.1109/TIP.2010.2042098).
15. 15)
  - 9. Criminisi, A., Perez, P., Toyama, K.: ‘Region filling and object removal by exemplar-based inpainting’, IEEE Trans. Image Process., 2004, 13, (9), pp. 1200–1212 (doi: 10.1109/TIP.2004.833105).
16. 16)
  - 13. Le Meur, O., Ebdelli, M., Guillemot, C.: ‘Hierarchical super-resolution-based inpainting’, IEEE Trans. Image Process., 2013, 22, (10), pp. 3779–3790 (doi: 10.1109/TIP.2013.2261308).
17. 17)
  - 10. Wong, A., Orchard, J.: ‘A nonlocal-means approach to exemplar-based inpainting’. IEEE Int. Conf. on Imag. Proc., October, 2008, pp. 2600–2603.
18. 18)
  - 8. Wei, L.-Y., Levoy, M.: ‘Fast texture synthesis using tree-structured vector quantization’. ACM SIGGRAPH, July 2000, pp. 479–488.

Context-driven hybrid image inpainting

References

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