Image Inpainting with Modified F-Transform

Restoring damaged images is an important problem in image processing and has been studied for applications such as inpainting missing regions, art restoration. In this work, we consider a modified (fuzzy transform) F-transform for restoration of damages such as holes, scratches. By utilizing weights calculated from known image regions using local variance from patches, we modify the classical F-transform to handle the missing regions effectively with edge preservation and local smoothness. Comparison with interpolation - nearest neighbor, bilinear and modern inpainting - Navier - Stokes, fast-marching methods illustrate that by using our proposed modified F-transform we obtain better results.

[1]  Jean-Michel Morel,et al.  A Review of Image Denoising Algorithms, with a New One , 2005, Multiscale Model. Simul..

[2]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid , 2012 .

[3]  Irina Perfilieva,et al.  Image reconstruction by means of F-transform , 2014, Knowl. Based Syst..

[4]  V. B. Surya Prasath Color Image Segmentation Based on Vectorial Multiscale Diffusion with Inter-scale Linking , 2009, PReMI.

[5]  Patrick Pérez,et al.  Region filling and object removal by exemplar-based image inpainting , 2004, IEEE Transactions on Image Processing.

[6]  Irina Perfilieva,et al.  Fuzzy transforms: Theory and applications , 2006, Fuzzy Sets Syst..

[7]  Guna Seetharaman,et al.  Multichannel texture image segmentation using local feature fitting based variational active contours , 2012, ICVGIP '12.

[8]  V. B. Surya Prasath A well-posed multiscale regularization scheme for digital image denoising , 2011, Int. J. Appl. Math. Comput. Sci..

[9]  V. B. Surya Prasath,et al.  Feature preserving anisotropic diffusion for image restoration , 2013, 2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG).

[10]  V. B. Surya Prasath,et al.  Well-Posed Inhomogeneous Nonlinear Diffusion Scheme for Digital Image Denoising , 2010, J. Appl. Math..

[11]  V. B. Surya Prasath,et al.  Weighted and well-balanced anisotropic diffusion scheme for image denoising and restoration , 2014 .

[12]  Alexandru Telea,et al.  An Image Inpainting Technique Based on the Fast Marching Method , 2004, J. Graphics, GPU, & Game Tools.

[13]  Guillermo Sapiro,et al.  Inpainting the colors , 2005, IEEE International Conference on Image Processing 2005.

[14]  G. Aubert,et al.  Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Applied Mathematical Sciences) , 2006 .

[15]  Guillermo Sapiro,et al.  Image inpainting , 2000, SIGGRAPH.

[16]  V. B. Surya Prasath,et al.  Multispectral image denoising by well-posed anisotropic diffusion scheme with channel coupling , 2010 .

[17]  Guillermo Sapiro,et al.  Navier-stokes, fluid dynamics, and image and video inpainting , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[18]  Kannappan Palaniappan,et al.  Color image denoising by chromatic edges based vector valued diffusion , 2013, ArXiv.

[19]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[20]  V. B. Surya Prasath,et al.  An Adaptive Diffusion Scheme for Image Restoration and Selective Smoothing , 2012, Int. J. Image Graph..