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Inpainting

Inpainting. Ya -Fan Su and Tao- Sheng Ou Group 31. A. Criminisi , P. Perez, and K. Toyama, "Region Filling and Object Removal by Exemplar-Based Image Inpainting ," IEEE Trans. Image Processing , 13(9), pp. 1200-1212, September 2004.

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Inpainting

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  1. Inpainting Ya-Fan Su and Tao-ShengOu Group 31 A. Criminisi, P. Perez, and K. Toyama, "Region Filling and Object Removal by Exemplar-Based Image Inpainting," IEEE Trans. Image Processing, 13(9), pp. 1200-1212, September 2004. J. Sun, L. Yuan, J. Jia, and H.-Y. Shum, “Image Completion with Structure Propagation,” SIGGRAPH, Vol. 24, pp. 861-868, 2005

  2. What can Inpainting do? • You can remove the annoying things in the image by inpainting Could we remove (inpaint) him?

  3. Two Systems we Implemented • Exemplar-Based Inpainting • Structure Propagation Inpainting A. Criminisi, P. Perez, and K. Toyama, "Region Filling and Object Removal by Exemplar-Based Image Inpainting," IEEE Trans. Image Processing, 13(9), pp. 1200-1212, September 2004. J. Sun, L. Yuan, J. Jia, and H.-Y. Shum, “Image Completion with Structure Propagation,” SIGGRAPH, Vol. 24, pp. 861-868, 2005

  4. Two Systems we Implemented • Exemplar-Based Inpainting • Structure Propagation Inpainting

  5. Systems • Exemplar-Based Inpainting • Structure Propagation Inpainting

  6. Exemplar-Based Inpainting

  7. 2 Key Ideas • Exemplar-based synthesis • Filling order

  8. 2 Key Ideas • Exemplar-based synthesis • Filling order

  9. Exemplar-Based Synthesis • Inpaint the target region patch by patch, and each pasted patch is sampled from the source region candidate patches target patch

  10. 2 Key Ideas • Exemplar-based synthesis • Filling order

  11. Filling Order (1/2) onion peel filling order of this work

  12. Filling Order (2/2) • The regions which are on the continuation of strong edges should be inpainted earlier Unit vector orthogonal to the contour Tangent direction of gradient

  13. Structure Propagation Inpainting

  14. Markov Random Field (MRF) Label1 Label1 Label1 Label1 Label1 Label1 Label2 Label2 Label2 Label2 Label2 Label2 Label3 Label3 Label3 Label3 Label3 Label3 …… …… …… …… …… ……

  15. Markov Random Field – Data Term DcostA,1 DcostC,1 DcostA,2 DcostC,2 A C DcostA,3 DcostC,3 …… ……

  16. Markov Random Field – Smoothness Term Scost(A,1),(B,1) Scost(A,2),(B,1) A B Scost(A,3),(B,1) ……

  17. Utilizing Markov Random Field • Various robust algorithms, such as belief propagation and graph cut, are developed to solve the MRF energy minimization problem • By formulating a problem as a MRF problem, it can easily be solved by applying these algorithms

  18. Structure Propagation

  19. Energy Terms for Structure Propagation Data cost 1 Data cost 2 Smoothness cost

  20. Experimental Results

  21. Experimental Results

  22. Experimental Results

  23. Experimental Results

  24. Experimental Results

  25. Experimental Results

  26. Experimental Results

  27. Experimental Results

  28. Experimental Results

  29. Experimental Results

  30. Experimental Results

  31. Experimental Results

  32. Experimental Results

  33. Experimental Results We could not inpaint the rainbow well because there are no suitable examples

  34. Experimental Results

  35. Experimental Results

  36. Experimental Results

  37. Experimental Results

  38. Experimental Results

  39. Experimental Results

  40. Experimental Results

  41. Experimental Results

  42. Experimental Results

  43. Experimental Results

  44. Experimental Results

  45. Experimental Results

  46. Experimental Results

  47. Experimental Results

  48. Experimental Results

  49. Experimental Results

  50. Conclusions • The algorithms applied in this project are rather robust in the sense that the parameters are not sensitive • Exemplar-based inpaintingalgorithm still has its limitations, because there may be no suitable example-patch in the image

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