Image super-resolution using gradient profile prior

In this paper, we propose an image super-resolution approach using a novel generic image prior - gradient profile prior, which is a parametric prior describing the shape and the sharpness of the image gradients. Using the gradient profile prior learned from a large number of natural images, we can provide a constraint on image gradients when we estimate a hi-resolution image from a low-resolution image. With this simple but very effective prior, we are able to produce state-of-the-art results. The reconstructed hi-resolution image is sharp while has rare ringing or jaggy artifacts.

[1]  Hsieh Hou,et al.  Cubic splines for image interpolation and digital filtering , 1978 .

[2]  T. Poggio,et al.  Fingerprints theorems for zero crossings , 1985 .

[3]  M. Varanasi,et al.  Parametric generalized Gaussian density estimation , 1989 .

[4]  Michal Irani,et al.  Motion Analysis for Image Enhancement: Resolution, Occlusion, and Transparency , 1993, J. Vis. Commun. Image Represent..

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

[6]  Richard G. Lane,et al.  Gradient methods for superresolution , 1997, Proceedings of International Conference on Image Processing.

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

[8]  Benjamin Kuipers,et al.  Integrating Vision and Spatial Reasoning for Assistive Navigation , 1998, Assistive Technology and Artificial Intelligence.

[9]  David Mumford,et al.  Statistics of natural images and models , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[10]  Michael Unser,et al.  Image interpolation and resampling , 2000 .

[11]  Takeo Kanade,et al.  Limits on super-resolution and how to break them , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[12]  Bryan S. Morse,et al.  Image magnification using level-set reconstruction , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[13]  M. Orchard,et al.  New edge-directed interpolation , 2001, IEEE Trans. Image Process..

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

[15]  William T. Freeman,et al.  Example-Based Super-Resolution , 2002, IEEE Computer Graphics and Applications.

[16]  Assaf Zomet,et al.  Learning how to inpaint from global image statistics , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[17]  Bryan C. Russell,et al.  Exploiting the sparse derivative prior for super-resolution , 2003 .

[18]  Nanning Zheng,et al.  Image hallucination with primal sketch priors , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[19]  Heung-Yeung Shum,et al.  Fundamental limits of reconstruction-based superresolution algorithms under local translation , 2004 .

[20]  Zhouchen Lin,et al.  Response to the Comments on "Fundamental Limits of Reconstruction-Based Superresolution Algorithms under Local Translation' , 2006, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  William T. Freeman,et al.  Learning Low-Level Vision , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[22]  D. Yeung,et al.  Super-resolution through neighbor embedding , 2004, CVPR 2004.

[23]  Shmuel Peleg,et al.  Seamless Image Stitching in the Gradient Domain , 2004, ECCV.

[24]  Michael J. Black,et al.  Fields of Experts: a framework for learning image priors , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[25]  Eric Dubois,et al.  Image up-sampling using total-variation regularization with a new observation model , 2005, IEEE Transactions on Image Processing.

[26]  Jean-Michel Morel,et al.  A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[27]  Serge J. Belongie,et al.  Big Little Icons , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[28]  Harry Shum,et al.  Patch based blind image super resolution , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[29]  Chi-Keung Tang,et al.  Perceptually-Inspired and Edge-Directed Color Image Super-Resolution , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[30]  Harry Shum,et al.  Face Hallucination: Theory and Practice , 2007, International Journal of Computer Vision.

[31]  Mei Han,et al.  Soft Edge Smoothness Prior for Alpha Channel Super Resolution , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[32]  Peyman Milanfar,et al.  Kernel Regression for Image Processing and Reconstruction , 2007, IEEE Transactions on Image Processing.

[33]  Raanan Fattal,et al.  Image upsampling via imposed edge statistics , 2007, ACM Trans. Graph..

[34]  Moshe Ben-Ezra,et al.  Penrose Pixels Super-Resolution in the Detector Layout Domain , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[35]  Frédo Durand,et al.  Image and depth from a conventional camera with a coded aperture , 2007, SIGGRAPH 2007.

[36]  Michael J. Black,et al.  Steerable Random Fields , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[37]  Krystian Mikolajczyk,et al.  Combining High-Resolution Images With Low-Quality Videos , 2008, BMVC.

[38]  Xueting Liu,et al.  MAP-Based Image Super-resolution Reconstruction , 2008 .

[39]  Kwang In Kim,et al.  Example-Based Learning for Single-Image Super-Resolution , 2008, DAGM-Symposium.

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