A modified LOT model for image denoising

In image processing, it is often desirable to remove the noise and preserve image features. Due to the strong edge preserving ability, the total variation (TV) based regularization has been widely studied. However, it produces undesirable staircase effect. To alleviate the staircase effect, the LOT model proposed by Lysaker et al. (IEEE Trans Image Process 13(10): 1345–1357, 2004) has been studied, which is called the two-step method. After that, this method has started to appear as one of the more effective methods for image denoising, which includes two energy functions: one is about the normal field, the other is about the reconstruction image using the normal field obtained in the first step. However, the smoothed normal field is only related to the original noisy image in the first step, which is not enough. In this paper, we proposed a modified LOT model for image denoising, which lets the reconstruction vector field be related to the restored image. In addition, to compute the new model, we design a relaxed alternative direction method. The numerical experiments show that the new model can obtain the better results compared with some state-of-the art methods.

[1]  Peyman Milanfar,et al.  Patch-Based Near-Optimal Image Denoising , 2012, IEEE Transactions on Image Processing.

[2]  Xiangchu Feng,et al.  Adaptive variational models for image decomposition , 2012, Science China Information Sciences.

[3]  Ling Shao,et al.  Nonlocal Hierarchical Dictionary Learning Using Wavelets for Image Denoising , 2013, IEEE Transactions on Image Processing.

[4]  Xue-Cheng Tai,et al.  Noise removal using smoothed normals and surface fitting , 2004, IEEE Transactions on Image Processing.

[5]  Zhen Liu,et al.  An Improved LOT Model for Image Restoration , 2009, Journal of Mathematical Imaging and Vision.

[6]  Anand Rangarajan,et al.  Image Denoising Using the Higher Order Singular Value Decomposition , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Arvid Lundervold,et al.  Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time , 2003, IEEE Trans. Image Process..

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

[9]  Alfred M. Bruckstein,et al.  Orientation-Matching Minimization for Image Denoising and Inpainting , 2011, International Journal of Computer Vision.

[10]  Xiangchu Feng,et al.  Image decomposition and staircase effect reduction based on total generalized variation , 2014 .

[11]  Talal Rahman,et al.  A Modified TV-Stokes Model for Image Processing , 2011, SIAM J. Sci. Comput..

[12]  Tingting Wu,et al.  Two-step methods for image zooming using duality strategies , 2014 .

[13]  Zhi-Guo Wang,et al.  Split Bregman method for the modified lot model in image denoising , 2011, Appl. Math. Comput..

[14]  Mostafa Kaveh,et al.  Fourth-order partial differential equations for noise removal , 2000, IEEE Trans. Image Process..

[15]  Hu Xiaohui,et al.  A flexible capacitive tactile sensor array with micro structure for robotic application , 2014 .

[16]  Mohammad Reza Hajiaboli.  An Anisotropic Fourth-Order Diffusion Filter for Image Noise Removal , 2011, International Journal of Computer Vision.

[17]  Yong Huang,et al.  Texture decomposition by harmonics extraction from higher order statistics , 2004, IEEE Trans. Image Process..

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

[19]  Xiangchu Feng,et al.  A coupled variational model for image denoising using a duality strategy and split Bregman , 2014, Multidimens. Syst. Signal Process..

[20]  Xue-Cheng Tai,et al.  Augmented Lagrangian Method for Generalized TV-Stokes Model , 2012, J. Sci. Comput..

[21]  Fang Su,et al.  A New TV-Stokes Model with Augmented Lagrangian Method for Image Denoising and Deconvolution , 2012, J. Sci. Comput..

[22]  Talal Rahman,et al.  A TV-Stokes Denoising Algorithm , 2007, SSVM.

[23]  Jian Bai,et al.  Primal-dual method for the coupled variational model , 2014, Comput. Electr. Eng..

[24]  Xiaobo Zhang,et al.  Image denoising using local adaptive layered Wiener filter in the gradient domain , 2014, Multimedia Tools and Applications.

[25]  Karl Kunisch,et al.  Total Generalized Variation , 2010, SIAM J. Imaging Sci..

[26]  Antonin Chambolle,et al.  A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging , 2011, Journal of Mathematical Imaging and Vision.