Image Deconvolution Ringing Artifact Detection and Removal via PSF Frequency Analysis

We present a new method to detect and remove ringing artifacts produced by the deconvolution process in image deblurring techniques. The method takes into account non-invertible frequency components of the blur kernel used in the deconvolution. Efficient Gabor wavelets are produced for each non-invertible frequency and applied on the deblurred image to generate a set of filter responses that reveal existing ringing artifacts. The set of Gabor filters is then employed in a regularization scheme to remove the corresponding artifacts from the deblurred image. The regularization scheme minimizes the responses of the reconstructed image to these Gabor filters through an alternating algorithm in order to suppress the artifacts. As a result of these steps we are able to significantly enhance the quality of the deblurred images produced by deconvolution algorithms. Our numerical evaluations using a ringing artifact metric indicate the effectiveness of the proposed deringing method.

[1]  Frédo Durand,et al.  Image and depth from a conventional camera with a coded aperture , 2007, ACM Trans. Graph..

[2]  Ingrid Heynderickx,et al.  A Perceptually Relevant Approach to Ringing Region Detection , 2010, IEEE Transactions on Image Processing.

[3]  Erik Reinhard,et al.  Second order image statistics in computer graphics , 2004, APGV '04.

[4]  Stephen Lin,et al.  Image/video deblurring using a hybrid camera , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Matthieu Guillaumin,et al.  Segmentation Propagation in ImageNet , 2012, ECCV.

[6]  Ingrid Heynderickx,et al.  A No-Reference Metric for Perceived Ringing Artifacts in Images , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

[7]  Gene H. Golub,et al.  A Nonlinear Primal-Dual Method for Total Variation-Based Image Restoration , 1999, SIAM J. Sci. Comput..

[8]  Jinwen Tian,et al.  Perceptual ringing metric to evaluate the quality of images restored using blind deconvolution algorithms , 2009 .

[9]  Tom Goldstein,et al.  The Split Bregman Method for L1-Regularized Problems , 2009, SIAM J. Imaging Sci..

[10]  Anat Levin,et al.  Blind Motion Deblurring Using Image Statistics , 2006, NIPS.

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

[12]  L. Lucy.  An iterative technique for the rectification of observed distributions , 1974 .

[13]  Hui Ma,et al.  Image Deblurring with Blurred / Noisy Image Pairs , 2013 .

[14]  Thomas Deselaers,et al.  ClassCut for Unsupervised Class Segmentation , 2010, ECCV.

[15]  Adam Finkelstein,et al.  A no-reference metric for evaluating the quality of motion deblurring , 2013, ACM Trans. Graph..

[16]  Seungyong Lee,et al.  Handling outliers in non-blind image deconvolution , 2011, 2011 International Conference on Computer Vision.

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

[18]  Wolfgang Heidrich,et al.  Stochastic Deconvolution , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

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

[20]  Wolfgang Heidrich,et al.  High-quality computational imaging through simple lenses , 2013, TOGS.

[21]  Junfeng Yang,et al.  An Efficient TVL1 Algorithm for Deblurring Multichannel Images Corrupted by Impulsive Noise , 2009, SIAM J. Sci. Comput..

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

[23]  Nikolas P. Galatsanos,et al.  Regularized constrained total least-squares image restoration , 1994, Other Conferences.

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

[25]  Ramesh Raskar,et al.  Coded exposure photography: motion deblurring using fluttered shutter , 2006, SIGGRAPH '06.

[26]  Eli Peli,et al.  Image enhancement in the JPEG domain for people with vision impairment , 2004, IEEE Transactions on Biomedical Engineering.

[27]  A. N. Tikhonov,et al.  Solutions of ill-posed problems , 1977 .

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

[29]  Jian Sun,et al.  Progressive inter-scale and intra-scale non-blind image deconvolution , 2008, SIGGRAPH 2008.

[30]  Frédo Durand,et al.  Understanding and evaluating blind deconvolution algorithms , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[31]  Dima Damen,et al.  Recognizing linked events: Searching the space of feasible explanations , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[32]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[33]  Shree K. Nayar,et al.  Motion-based motion deblurring , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  William H. Richardson,et al.  Bayesian-Based Iterative Method of Image Restoration , 1972 .

[35]  Anil K. Jain,et al.  Unsupervised texture segmentation using Gabor filters , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[36]  Sundaresh Ram,et al.  Removing Camera Shake from a Single Photograph , 2009 .

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

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

[39]  Jianhong Shen,et al.  Deblurring images: Matrices, spectra, and filtering , 2007, Math. Comput..

[40]  Truong Q. Nguyen,et al.  An Augmented Lagrangian Method for Total Variation Video Restoration , 2011, IEEE Transactions on Image Processing.

[41]  Jiaya Jia,et al.  High-quality motion deblurring from a single image , 2008, ACM Trans. Graph..

[42]  E. Peli,et al.  Contrast sensitivity function and image discrimination. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.