ABSTRACT
Inpainting, the technique of modifying an image in an undetectable form, is as ancient as art itself. The goals and applications of inpainting are numerous, from the restoration of damaged paintings and photographs to the removal/replacement of selected objects. In this paper, we introduce a novel algorithm for digital inpainting of still images that attempts to replicate the basic techniques used by professional restorators. After the user selects the regions to be restored, the algorithm automatically fills-in these regions with information surrounding them. The fill-in is done in such a way that isophote lines arriving at the regions' boundaries are completed inside. In contrast with previous approaches, the technique here introduced does not require the user to specify where the novel information comes from. This is automatically done (and in a fast way), thereby allowing to simultaneously fill-in numerous regions containing completely different structures and surrounding backgrounds. In addition, no limitations are imposed on the topology of the region to be inpainted. Applications of this technique include the restoration of old photographs and damaged film; removal of superimposed text like dates, subtitles, or publicity; and the removal of entire objects from the image like microphones or wires in special effects.
- 1.S. Walden. The Ravished Image. St. Martin's Press, New York, 1985.Google Scholar
- 2.G. Emile-Male. The Restorer's Handbook of Easel Painting. Van Nostrand Reinhold, New York, 1976.Google Scholar
- 3.D. King. The Commissar Vanishes. Henry Holt and Company, 1997.Google Scholar
- 4.A.C. Kokaram, R.D. Morris, W.J. Fitzgerald, P.J.W. Rayner. Detection of missing data in image sequences. IEEE Transac-tions on Image Processing 11(4), 1496-1508, 1995. Google ScholarDigital Library
- 5.A.C. Kokaram, R.D. Morris, W.J. Fitzgerald, P.J.W. Rayner. Interpolation of missing data in image sequences. IEEE Trans-actions on Image Processing 11(4), 1509-1519, 1995. Google ScholarDigital Library
- 6.C. Braverman. Photoshop retouching handbook. IDG Books Worldwide, 1998.Google Scholar
- 7.A. Hirani and T. Totsuka. Combining Frequency and spatial domain information for fast interactive image noise removal. Computer Graphics, pp. 269-276, SIGGRAPH 96, 1996. Google ScholarDigital Library
- 8.A. Efros and T. Leung, "Texture synthesis by non-parametric sampling," Proc. IEEE International Conference Computer Vi-sion, pp. 1033-1038, Corfu, Greece, September 1999. Google ScholarDigital Library
- 9.D.Heeger andJ.Bergen. Pyramid based texture analy-sis/ synthesis. Computer Graphics, pp. 229-238, SIGGRAPH 95, 1995. Google ScholarDigital Library
- 10.E. Simoncelli and J. Portilla. Texture characterization via joint statistics of wavelet coefficient magnitudes. 5th IEEE Int'l Conf. on Image Processing, Chicago, IL. Oct 4-7, 1998.Google ScholarCross Ref
- 11.M. Nitzberg, D. Mumford, and T. Shiota, Filtering, Segmen-tation, and Depth, Springer-Verlag, Berlin, 1993. Google ScholarDigital Library
- 12.S. Masnou and J.M. Morel. Level-lines based disocclusion. 5th IEEE Int'l Conf. on Image Processing, Chicago, IL. Oct 4-7, 1998.Google ScholarCross Ref
- 13.C. Kenney and J. Langan. A new image processing primitive: reconstructing images from modified flow fields. University of California Santa Barbara Preprint, 1999.Google Scholar
- 14.P. Perona and J. Malik Scale-space and edge detection using anisotropic diffusion. IEEE-PAMI 12, pp. 629-639, 1990. Google ScholarDigital Library
- 15.L. Alvarez, P.L. Lions, J.M. Morel. Image selective smoothing and edge detection by nonlinear diffusion.SIAMJ.Numer. Anal. 29, pp. 845-866, 1992. Google ScholarDigital Library
- 16.S. Osher and J. Sethian. Fronts propagating with curvature dependent speed: algorithms based on Hamilton-Jacobi for-mulations. Journal of Computational Physics, 79:12-49, 1988. Google ScholarDigital Library
- 17.A. Marquina and S. Osher. Explicit algorithms for a new time dependent model based on level set motion for nonlinear de-bluring and noise removal. UCLA CAM Report 99-5, January 1999.Google Scholar
- 18.L. Rudin, S. Osher and E. Fatemi. Nonlinear total variation based noise removal algorithms. Physica D, 60, pp. 259-268, 1992. Google ScholarDigital Library
- 19.S. Osher, personal communication, October 1999.Google Scholar
- 20.H. K. Zhao, T. Chan, B. Merriman, and S. Osher, "A varia-tional level-set approach to multiphase motion," J. of Compu-tational Physics 127, pp. 179-195, 1996. Google ScholarDigital Library
- 21.A. Bertozzi The mathematics of moving contact lines in thin liquid films. Notices Amer. Math. Soc., Volume 45, Number 6, pp. 689-697, June/July 1998.Google Scholar
- 22.J. Tumblin and G. Turk, "LCIS: A boundary hierarchy for detail-preserving contrast reduction," Computer Graphics, pp. 83-90, SIGGRAPH 99, 1999. Google ScholarDigital Library
- 23.T. Chan and J. Shen, "Mathematical models for local deter-ministic inpaintings," UCLA CAM TR 00-11, March 2000.Google Scholar
- 24.C. Ballester, M. Bertalmio, V. Caselles, G. Sapiro, and J. Verdera, "Filling-in by joint interpolation of vector fields and grey levels," University of Minnesota IMA TR, April 2000.Google Scholar
Index Terms
- Image inpainting
Recommendations
Error Analysis for Image Inpainting
Image inpainting refers to restoring a damaged image with missing information. In recent years, there have been many developments on computational approaches to image inpainting problem [2, 4, 6, 9, 11---13, 27, 28]. While there are many effective ...
Interactive Image Restoration Using Inpainting and Denoising
NCVPRIPG '11: Proceedings of the 2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing and GraphicsDigital in painting is the technique of filling in the missing regions of an image using information from the surrounding area in a visually indistinguishable way. In this paper, we try to improve the Exemplar based method by manipulating the values of ...
Image Retrieval Using Digital Image Inpainting Techniques
Image retrieval is an inverse problem in digital image processing. In this paper, the authors deal with restoration of image using digitally image inpainting methods. In this inpainting technique, one can extract a missing an important part or can ...
Comments