Image Denoising Based on Online Dictionary Learning

Many state-of-the-art denoising algorithms often employ dictioanary learning methods to acquire the mapping relationship between the polluted image by noise and the original clean image. It is critical to generate the appropriate dictionary in image de noising based on dictionary learning. In order to promote the de noising efficiency, the proposed algorithm employs Online Dictionary Learning (ODL) to train the overcomplete dictionaries to make the dictionary more accurate. The dictionary updating procedure is improved with a warm start. The dictionary is updated by the last computed dictionary and the current input image patches. Hence the dictionary is more accurate to get better denoising images. In the experiments, the PSNR of ODL dictionary is 0.12dB higher than SCDL and 0.21dB higher than K-SVD in average. The proposed method can eliminate the artifacts and recover the texture details efficiently.

[1]  David J. Field,et al.  Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.

[2]  Jaakko Astola,et al.  From Local Kernel to Nonlocal Multiple-Model Image Denoising , 2009, International Journal of Computer Vision.

[3]  Quan Pan,et al.  Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Peyman Milanfar,et al.  Global Image Denoising , 2014, IEEE Transactions on Image Processing.

[5]  Soosan Beheshti,et al.  Adaptive Bayesian Denoising for General Gaussian Distributed Signals , 2014, IEEE Transactions on Signal Processing.

[6]  Yongqiang Zhao,et al.  Hyperspectral Image Denoising via Sparse Representation and Low-Rank Constraint , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[7]  David Zhang,et al.  Gradient Histogram Estimation and Preservation for Texture Enhanced Image Denoising , 2014, IEEE Transactions on Image Processing.

[8]  Rajat Raina,et al.  Efficient sparse coding algorithms , 2006, NIPS.

[9]  Lei Zhang,et al.  Centralized sparse representation for image restoration , 2011, 2011 International Conference on Computer Vision.

[10]  Michael Elad,et al.  Sparse and Redundant Representation Modeling—What Next? , 2012, IEEE Signal Processing Letters.

[11]  祝世平 Zhu Shiping,et al.  A Stereo Matching Algorithm Using Improved Gradient and Adaptive Window , 2015 .

[12]  Michael Elad,et al.  Improving K-SVD denoising by post-processing its method-noise , 2013, 2013 IEEE International Conference on Image Processing.

[13]  Guillermo Sapiro,et al.  Non-local sparse models for image restoration , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[14]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[15]  Lei Zhang,et al.  Sparsity-based image denoising via dictionary learning and structural clustering , 2011, CVPR 2011.

[16]  Guy Gilboa,et al.  A Total Variation Spectral Framework for Scale and Texture Analysis , 2014, SIAM J. Imaging Sci..

[17]  Stefan Harmeling,et al.  Image denoising: Can plain neural networks compete with BM3D? , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Rama Chellappa,et al.  Sparse Representations and Compressive Sensing for Imaging and Vision , 2013, Springer Briefs in Electrical and Computer Engineering.