Automatic Contrast Parameter Estimation in Anisotropic Diffusion for Image Restoration

Anisotropic diffusion is used widely in image processing for edge preserving filtering and image smoothing tasks. One of the important class of such model is by Perona and Malik (PM) who used a gradient based diffusion to drive smoothing along edges and not across it. The contrast parameter used in the PM method needs to be carefully chosen to obtain optimal denoising results. Here we consider a local histogram based cumulative distribution approach for selecting this parameter in a data adaptive way so as to avoid manual tuning. We use spatial smoothing based diffusion coefficient along with adaptive contrast parameter estimation for obtaining better edge maps. Moreover, experimental results indicate that this adaptive scheme performs well for a variety of noisy images and comparison results indicate we obtain better peak signal to noise ratio and structural similarity scores with respect to fixed constant parameter values.

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

[2]  P. Lions,et al.  Image selective smoothing and edge detection by nonlinear diffusion. II , 1992 .

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

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

[5]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

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

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

[8]  Dmitry Vorotnikov,et al.  On a System of Adaptive Coupled PDEs for Image Restoration , 2012, Journal of Mathematical Imaging and Vision.

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

[10]  V. B. Surya Prasath,et al.  On convergent finite difference schemes for variational–PDE-based image processing , 2013, ArXiv.

[11]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  V. B. Surya Prasath,et al.  A hybrid convex variational model for image restoration , 2010, Appl. Math. Comput..

[13]  J. Craggs.  Applied Mathematical Sciences , 1973 .

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