Fast Weighted Total Variation Regularization Algorithm for Blur Identification and Image Restoration

Images obtained from unconstrained environments may be blurred by unknown kernels and affected due to noise. This paper presents a new total variation minimization-based method for blindly deblurring such images. Unlike the alternating optimization-based algorithms, the proposed algorithm adopts a joint estimation strategy to estimate the unknown blurring kernel and the unknown image in an iterative manner, where each iteration performs two separate image denoising subproblems that admit fast implementation. Experiments are performed on multiple synthetic, grayscale, and color images, and the results demonstrate that the proposed method is effective in blind deblurring.

[1]  Mostafa Kaveh,et al.  A regularization approach to joint blur identification and image restoration , 1996, IEEE Trans. Image Process..

[2]  Raymond H. Chan,et al.  A Fast Algorithm for Deblurring Models with Neumann Boundary Conditions , 1999, SIAM J. Sci. Comput..

[3]  Zhiyong Zuo,et al.  An Adaptive Non-local Total Variation Blind Deconvolution Employing Split Bregman Iteration , 2012, ISCID 2012.

[4]  Weiguo Gong,et al.  Non-blind image deblurring method by local and nonlocal total variation models , 2014, Signal Process..

[5]  Joan Bruna,et al.  Blind Deconvolution with Re-weighted Sparsity Promotion , 2013, ArXiv.

[6]  Luís B. Almeida,et al.  Blind and Semi-Blind Deblurring of Natural Images , 2010, IEEE Transactions on Image Processing.

[7]  Yun Zou,et al.  Based on Total Variation Regularization Iterative Blind Image Restoration Algorithm , 2014 .

[8]  Subhasis Chaudhuri,et al.  Convergence analysis of a quadratic upper bounded TV regularizer based blind deconvolution , 2015, Signal Process..

[9]  Curtis R. Vogel,et al.  Ieee Transactions on Image Processing Fast, Robust Total Variation{based Reconstruction of Noisy, Blurred Images , 2022 .

[10]  Laurent D. Cohen,et al.  Non-local Regularization of Inverse Problems , 2008, ECCV.

[11]  Pascal Getreuer,et al.  Total Variation Deconvolution using Split Bregman , 2012, Image Process. Line.

[12]  Marc Teboulle,et al.  Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems , 2009, IEEE Transactions on Image Processing.

[13]  Lin He,et al.  Blind deconvolution using TV regularization and Bregman iteration , 2005, Int. J. Imaging Syst. Technol..

[14]  Karen O. Egiazarian,et al.  BM3D Frames and Variational Image Deblurring , 2011, IEEE Transactions on Image Processing.

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

[16]  Daniele Perrone,et al.  Total Variation Blind Deconvolution: The Devil Is in the Details , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Mostafa Kaveh,et al.  Blind image restoration by anisotropic regularization , 1999, IEEE Trans. Image Process..

[18]  Max Q.-H. Meng,et al.  De-blurring wireless capsule endoscopy images by total variation minimization , 2011, Proceedings of 2011 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing.

[19]  Peyman Milanfar,et al.  Robust Multichannel Blind Deconvolution via Fast Alternating Minimization , 2012, IEEE Transactions on Image Processing.

[20]  M. Sakurai,et al.  Blind image restoration based on total variation regularization of blurred images , 2012, The 1st IEEE Global Conference on Consumer Electronics 2012.

[21]  Marco Donatelli,et al.  A fast alternating minimization algorithm for total variation deblurring without boundary artifacts , 2013, 1308.6754.

[22]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[23]  Paul A. Rodríguez,et al.  Total Variation Regularization Algorithms for Images Corrupted with Different Noise Models: A Review , 2013, J. Electr. Comput. Eng..

[24]  Chuan He,et al.  Fast Total-Variation Image Deconvolution with Adaptive Parameter Estimation via Split Bregman Method , 2014 .

[25]  Houzhang Fang,et al.  Blind image deconvolution with spatially adaptive total variation regularization. , 2012, Optics letters.

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

[27]  Marc Teboulle,et al.  A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..

[28]  Dexing Kong,et al.  Nonlocal total variation models for multiplicative noise removal using split Bregman iteration , 2012, Math. Comput. Model..

[29]  Peyman Milanfar,et al.  A General Framework for Regularized, Similarity-Based Image Restoration , 2014, IEEE Transactions on Image Processing.

[30]  T. Chan,et al.  Convergence of the alternating minimization algorithm for blind deconvolution , 2000 .

[31]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[32]  Mamdouh F. Fahmy,et al.  A new total variation based image denoising and deblurring technique , 2013, Eurocon 2013.

[33]  J. Coatrieux,et al.  A New Fast Algorithm for Constrained Four-Directional Total Variation Image Denoising Problem , 2015 .

[34]  Justin K. Romberg,et al.  Blind Deconvolution Using Convex Programming , 2012, IEEE Transactions on Information Theory.

[35]  Hongming Zhang,et al.  Construction model for total variation regularization parameter. , 2014, Optics express.