Stochastic super-resolution image reconstruction

The objective of super-resolution (SR) imaging is to reconstruct a single higher-resolution image based on a set of lower-resolution images that were acquired from the same scene to overcome the limitations of image acquisition process for facilitating better visualization and content recognition. In this paper, a stochastic Markov chain Monte Carlo (MCMC) SR image reconstruction approach is proposed. First, a Bayesian inference formulation, which is based on the observed low-resolution images and the prior high-resolution image model, is mathematically derived. Second, to exploit the MCMC sample-generation technique for the stochastic SR image reconstruction, three fundamental issues are observed as follows. First, since the hyperparameter value of the prior image model controls the degree of regularization and intimately affects the quality of the reconstructed high-resolution image, how to determine an optimal hyperparameter value for different low-resolution input images becomes a very challenging task. Rather than exploiting the exhaustive search, an iterative updating approach is developed in this paper by allowing the value of hyperparameter being simultaneously updated in each sample-generation iteration. Second, the samples generated during the so-called burn-in period (measured in terms of the number of samples initially generated) of the MCMC-based sample-generation process are considered unreliable and should be discarded. To determine the length of the burn-in period for each set of low-resolution input images, a time-period bound in closed form is mathematically derived. Third, image artifacts could be incurred in the reconstructed high-resolution image, if the number of samples (counting after the burn-in period) generated by the MCMC-based sample-generation process is insufficient. For that, a variation-sensitive bilateral filter is proposed as a 'complementary' post-processing scheme, to improve the reconstructed high-resolution image quality, when the number of samples is insufficient. Extensive simulation results have clearly shown that the proposed stochastic SR image reconstruction method consistently yields superior performance.

[1]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Lisimachos P. Kondi,et al.  Resolution enhancement of video sequences with simultaneous estimation of the regularization parameters , 2003, IS&T/SPIE Electronic Imaging.

[3]  Josiane Zerubia,et al.  Hyperparameter estimation for satellite image restoration using a MCMC maximum-likelihood method , 2002, Pattern Recognit..

[4]  Lisimachos P. Kondi,et al.  Resolution enhancement of video sequences with simultaneous estimation of the regularization parameter , 2004, J. Electronic Imaging.

[5]  Mario Bertero,et al.  Introduction to Inverse Problems in Imaging , 1998 .

[6]  Robert L. Stevenson,et al.  Spatial Resolution Enhancement of Low-Resolution Image Sequences A Comprehensive Review with Directions for Future Research , 1998 .

[7]  S. Chaudhuri.  Super-Resolution Imaging , 2001 .

[8]  Sergios Theodoridis,et al.  A Novel Efficient Cluster-Based MLSE Equalizer for Satellite Communication Channels with-QAM Signaling , 2006, EURASIP J. Adv. Signal Process..

[9]  Xiaodong Wang,et al.  Monte Carlo methods for signal processing , 2005 .

[10]  Aggelos K. Katsaggelos,et al.  Super Resolution of Images and Video , 2006, Super Resolution of Images and Video.

[11]  Jack J. Dongarra,et al.  Guest Editors Introduction to the top 10 algorithms , 2000, Comput. Sci. Eng..

[12]  Robert L. Stevenson,et al.  Extraction of high-resolution frames from video sequences , 1996, IEEE Trans. Image Process..

[13]  Sylvia Richardson,et al.  Markov Chain Monte Carlo in Practice , 1997 .

[14]  Christopher M. Bishop,et al.  Bayesian Image Super-Resolution , 2002, NIPS.

[15]  Nikolas P. Galatsanos,et al.  Hierarchical Bayesian image restoration from partially known blurs , 2000, IEEE Trans. Image Process..

[16]  Leonhard Held,et al.  Gaussian Markov Random Fields: Theory and Applications , 2005 .

[17]  Kai-Kuang Ma,et al.  Markov Chain Monte Carlo Super-Resolution Image Reconstruction with Simultaneous Adaptation of the Prior Image Model , 2006, PCM.

[18]  Kai-Kuang Ma,et al.  Markov Chain Monte Carlo Super-resolution Image Reconstruction With Artifacts Suppression , 2006, APCCAS 2006 - 2006 IEEE Asia Pacific Conference on Circuits and Systems.

[19]  Edmund Y. Lam,et al.  Superresolution imaging: Theory, Algorithms and Applications , 2007, Multidimens. Syst. Signal Process..

[20]  Russell C. Hardie,et al.  Joint MAP registration and high-resolution image estimation using a sequence of undersampled images , 1997, IEEE Trans. Image Process..

[21]  Peyman Milanfar,et al.  Super-Resolution Imaging: Analysis, Algorithms, and Applications , 2006, EURASIP J. Adv. Signal Process..

[22]  Moon Gi Kang,et al.  Super-resolution image reconstruction , 2010, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010).

[23]  Robert D. Nowak,et al.  Wavelet-based image estimation: an empirical Bayes approach using Jeffrey's noninformative prior , 2001, IEEE Trans. Image Process..

[24]  Kai-Kuang Ma,et al.  A MCMC approach for Bayesian super-resolution image reconstruction , 2005, IEEE International Conference on Image Processing 2005.

[25]  Xiaodong Wang,et al.  Monte Carlo methods for signal processing: a review in the statistical signal processing context , 2005, IEEE Signal Processing Magazine.

[26]  Robert L. Stevenson,et al.  Super-resolution from image sequences-a review , 1998, 1998 Midwest Symposium on Circuits and Systems (Cat. No. 98CB36268).

[27]  Sabine Süsstrunk,et al.  A Frequency Domain Approach to Registration of Aliased Images with Application to Super-resolution , 2006, EURASIP J. Adv. Signal Process..

[28]  Alison L. Gibbs,et al.  Convergence in the Wasserstein Metric for Markov Chain Monte Carlo Algorithms with Applications to Image Restoration , 2004 .

[29]  Stan Z. Li,et al.  Markov Random Field Modeling in Computer Vision , 1995, Computer Science Workbench.

[30]  Lisimachos P. Kondi,et al.  An image super-resolution algorithm for different error levels per frame , 2006, IEEE Transactions on Image Processing.

[31]  Klamer Schutte,et al.  Robust Fusion of Irregularly Sampled Data Using Adaptive Normalized Convolution , 2006, EURASIP J. Adv. Signal Process..

[32]  Li Chen,et al.  A soft MAP framework for blind super-resolution image reconstruction , 2009, Image Vis. Comput..

[33]  New York Dover,et al.  ON THE CONVERGENCE PROPERTIES OF THE EM ALGORITHM , 1983 .

[34]  T. S. Huang,et al.  Advances in computer vision & image processing , 1988 .

[35]  Gerhard Winkler,et al.  Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction , 2002 .

[36]  Roger Y. Tsai,et al.  Multiframe image restoration and registration , 1984 .

[37]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[38]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[39]  Stephen J. Roberts,et al.  Bayesian Image Super-resolution, Continued , 2006, NIPS.

[40]  Nirmal K. Bose,et al.  Advances in superresolution using L-curve , 2001, ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems (Cat. No.01CH37196).

[41]  Ali Mohammad-Djafari,et al.  Super-Resolution Using Hidden Markov Model and Bayesian Detection Estimation Framework , 2006, EURASIP J. Adv. Signal Process..

[42]  William J. Fitzgerald,et al.  Markov chain Monte Carlo methods with applications to signal processing , 2001, Signal Process..

[43]  Anil C. Kokaram,et al.  MCMC for joint noise reduction and missing data treatment in degraded video , 2002, IEEE Trans. Signal Process..