Adaptive blind deconvolution using generalized cross-validation with generalized lp/lq norm regularization

Abstract Blind image deconvolution is a typically ill-conditioned inverse problem that requires additional information to constrain the solution space. The purpose of this paper is to investigate the characteristics of generalized lp/lq norm on the derivatives of nature images and present a novel efficient method for blind image deconvolution with generalized lp/lq norm (0

[1]  Narendra Ahuja,et al.  A Comparative Study for Single Image Blind Deblurring , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Nikolas P. Galatsanos,et al.  Projection-based blind deconvolution , 1994 .

[3]  Ayan Chakrabarti,et al.  A Neural Approach to Blind Motion Deblurring , 2016, ECCV.

[4]  Nikolas P. Galatsanos,et al.  Variational Bayesian Blind Image Deconvolution with Student-T Priors , 2007, 2007 IEEE International Conference on Image Processing.

[5]  Behnam Jafarpour,et al.  Effective solution of nonlinear subsurface flow inverse problems in sparse bases , 2010 .

[6]  Frédo Durand,et al.  Efficient marginal likelihood optimization in blind deconvolution , 2011, CVPR 2011.

[7]  Gangyao Kuang,et al.  Spatial–Spectral Total Variation Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Li Xu,et al.  Unnatural L0 Sparse Representation for Natural Image Deblurring , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Jian-Feng Cai,et al.  Framelet-Based Blind Motion Deblurring From a Single Image , 2012, IEEE Transactions on Image Processing.

[10]  Stefan Roth,et al.  Normalized Blind Deconvolution , 2018, ECCV.

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

[12]  Jian-Jiun Ding,et al.  Blur kernel estimation using normalized color-line priors , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Thomas S. Huang,et al.  Close the loop: Joint blind image restoration and recognition with sparse representation prior , 2011, 2011 International Conference on Computer Vision.

[14]  Lei Zhang,et al.  Nonlocally Centralized Sparse Representation for Image Restoration , 2013, IEEE Transactions on Image Processing.

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

[16]  Michael K. Ng,et al.  Blind Deconvolution Using Generalized Cross-Validation Approach to Regularization Parameter Estimation , 2011, IEEE Transactions on Image Processing.

[17]  Zongben Xu,et al.  $L_{1/2}$ Regularization: A Thresholding Representation Theory and a Fast Solver , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[18]  Deqing Sun,et al.  Blind Image Deblurring Using Dark Channel Prior , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Deepa Kundur,et al.  Blind Image Deconvolution , 2001 .

[20]  Yair Weiss,et al.  From learning models of natural image patches to whole image restoration , 2011, 2011 International Conference on Computer Vision.

[21]  Youshen Xia,et al.  Joint blur kernel estimation and CNN for blind image restoration , 2020, Neurocomputing.

[22]  Jiaya Jia,et al.  High-quality motion deblurring from a single image , 2008, SIGGRAPH 2008.

[23]  Nikos Komodakis,et al.  A MAP-Estimation Framework for Blind Deblurring Using High-Level Edge Priors , 2014, ECCV.

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

[25]  Ling Shao,et al.  Blind Image Blur Estimation via Deep Learning , 2016, IEEE Transactions on Image Processing.

[26]  Daniele Perrone,et al.  A Logarithmic Image Prior for Blind Deconvolution , 2016, International Journal of Computer Vision.

[27]  Yanning Zhang,et al.  Blind image deblurring by promoting group sparsity , 2018, Neurocomputing.

[28]  Xiaochun Cao,et al.  Image Deblurring via Extreme Channels Prior , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Aggelos K. Katsaggelos,et al.  Variational Bayesian Blind Deconvolution Using a Total Variation Prior , 2009, IEEE Transactions on Image Processing.

[30]  Xiaochun Cao,et al.  Scene Text Deblurring Using Text-Specific Multiscale Dictionaries , 2015, IEEE Transactions on Image Processing.

[31]  Bernhard Schölkopf,et al.  Learning to Deblur , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Michael A. Malcolm,et al.  Computer methods for mathematical computations , 1977 .

[33]  Michal Irani,et al.  Blind Deblurring Using Internal Patch Recurrence , 2014, ECCV.

[34]  Gene H. Golub,et al.  Generalized cross-validation as a method for choosing a good ridge parameter , 1979, Milestones in Matrix Computation.

[35]  Ming-Hsuan Yang,et al.  Learning a Discriminative Prior for Blind Image Deblurring , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[36]  LianLin Li Sparsity-Promoted Blind Deconvolution of Ground-Penetrating Radar (GPR) Data , 2014, IEEE Geoscience and Remote Sensing Letters.

[37]  Ming-Hsuan Yang,et al.  Single image deblurring with adaptive dictionary learning , 2010, 2010 IEEE International Conference on Image Processing.

[38]  Aggelos K. Katsaggelos,et al.  Bayesian Blind Deconvolution with General Sparse Image Priors , 2012, ECCV.

[39]  Charless C. Fowlkes,et al.  Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[41]  Sunghyun Cho,et al.  Edge-based blur kernel estimation using patch priors , 2013, IEEE International Conference on Computational Photography (ICCP).

[42]  Jay Lee,et al.  Sparse filtering with the generalized lp/lq norm and its applications to the condition monitoring of rotating machinery , 2018 .

[43]  K. Egiazarian,et al.  Blind image deconvolution , 2007 .

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

[45]  Jay Lee,et al.  A geometrical investigation on the generalized lp/lq norm for blind deconvolution , 2017, Signal Process..

[46]  Bahram Javidi,et al.  Automatic regularization parameter selection by generalized cross-validation for total variational Poisson noise removal. , 2017, Applied optics.

[47]  Sannyuya Liu,et al.  RISIR: Rapid Infrared Spectral Imaging Restoration Model for Industrial Material Detection in Intelligent Video Systems , 2019, IEEE Transactions on Industrial Informatics.

[48]  Sannyuya Liu,et al.  Flexible FTIR Spectral Imaging Enhancement for Industrial Robot Infrared Vision Sensing , 2020, IEEE Transactions on Industrial Informatics.

[49]  Junfeng Yang,et al.  A Fast Algorithm for Edge-Preserving Variational Multichannel Image Restoration , 2009, SIAM J. Imaging Sci..