Image Motion Restoration Using Fractional-Order Gradient Prior

The choice of natural image prior decides the quality of restored image. Recently success- ful algorithms exploit heavy-tailed gradient distribution as image prior to restore latent image with piecewise smooth regions. However, these prior assumed also remove the mid-frequency compo- nent such as textural details regions while they preserve sharp edges. That because gradient profile in fractal-like texture do not have sparse characteristic. To restore textural features of expected latent image, in this paper, we introduce fractional-order gradient as image prior which is more appropriate to describe characteristic of image textures. From details comparison of our experiments, the textual details are more clear and visual quality is im- proved.

[1]  Zhang Jun,et al.  A class of fractional-order multi-scale variational models and alternating projection algorithm for image denoising , 2011 .

[2]  William T. Freeman,et al.  Removing camera shake from a single photograph , 2006, SIGGRAPH 2006.

[3]  José Luís Calvo-Rolle,et al.  Adaptive Inverse Control Using an Online Learning Algorithm for Neural Networks , 2014, Informatica.

[4]  Tom E. Bishop,et al.  Blind Deconvolution , 2014, Computer Vision, A Reference Guide.

[5]  Stanley Osher,et al.  Total variation based image restoration with free local constraints , 1994, Proceedings of 1st International Conference on Image Processing.

[6]  Yi-Fei Pu,et al.  A new CT metal artifacts reduction algorithm based on fractional-order sinogram inpainting. , 2011, Journal of X-ray science and technology.

[7]  R. Chan,et al.  An Adaptive Strategy for the Restoration of Textured Images using Fractional Order Regularization , 2013 .

[8]  Pu Yi-Fei Fractional Differential Analysis for Texture of Digital Image , 2007 .

[9]  Yi Zhou,et al.  Image deblurring in the presence of salt-and-pepper noise , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[10]  Richard Szeliski,et al.  Image deblurring and denoising using color priors , 2009, CVPR.

[11]  Stephen J. Wright,et al.  Sparse Reconstruction by Separable Approximation , 2008, IEEE Transactions on Signal Processing.

[12]  Weixing Wang,et al.  Fractional differential approach to detecting textural features of digital image and its fractional differential filter implementation , 2008, Science in China Series F: Information Sciences.

[13]  Donald Geman,et al.  Constrained Restoration and the Recovery of Discontinuities , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Tony F. Chan,et al.  Total variation blind deconvolution , 1998, IEEE Trans. Image Process..

[15]  Y. Zhang,et al.  A Class of Fractional-Order Variational Image Inpainting Models , 2012 .

[16]  Jian Bai,et al.  Fractional-Order Anisotropic Diffusion for Image Denoising , 2007, IEEE Transactions on Image Processing.

[17]  Donald Geman,et al.  Nonlinear image recovery with half-quadratic regularization , 1995, IEEE Trans. Image Process..

[18]  Frédo Durand,et al.  Understanding and evaluating blind deconvolution algorithms , 2009, CVPR.

[19]  Gérard Thomas,et al.  A deconvolution technique using optimal Wiener filtering and regularization , 1999, Signal Process..

[20]  Rob Fergus,et al.  Blind deconvolution using a normalized sparsity measure , 2011, CVPR 2011.

[21]  Li Xu,et al.  Two-Phase Kernel Estimation for Robust Motion Deblurring , 2010, ECCV.

[22]  Leiting Chen,et al.  A Survey of Video Enhancement Techniques , 2012, J. Inf. Hiding Multim. Signal Process..

[23]  Junfeng Yang,et al.  A New Alternating Minimization Algorithm for Total Variation Image Reconstruction , 2008, SIAM J. Imaging Sci..

[24]  Rob Fergus,et al.  Fast Image Deconvolution using Hyper-Laplacian Priors , 2009, NIPS.