Bilateral Back-Projection for Single Image Super Resolution

In this paper, a novel algorithm for single image super resolution is proposed. Back-projection [1] can minimize the reconstruction error with an efficient iterative procedure. Although it can produce visually appealing result, this method suffers from the chessboard effect and ringing effect, especially along strong edges. The underlining reason is that there is no edge guidance in the error correction process. Bilateral filtering can achieve edge-preserving image smoothing by adding the extra information from the feature domain. The basic idea is to do the smoothing on the pixels which are nearby both in space domain and in feature domain. The proposed bilateral back-projection algorithm strives to integrate the bilateral filtering into the back-projection method. In our approach, the back-projection process can be guided by the edge information to avoid across-edge smoothing, thus the chessboard effect and ringing effect along image edges are removed. Promising results can be obtained by the proposed bilateral back-projection method efficiently.

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

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

[3]  Michael Elad,et al.  Fast and robust multiframe super resolution , 2004, IEEE Transactions on Image Processing.

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

[5]  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.

[6]  Ping Wah Wong,et al.  Edge-directed interpolation , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[7]  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).

[8]  BarashDanny A Fundamental Relationship between Bilateral Filtering, Adaptive Smoothing, and the Nonlinear Diffusion Equation , 2002 .

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

[10]  Mei Han,et al.  Video Super-resolution with Scene-specific Priors , 2006, BMVC.

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

[12]  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).

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

[14]  Masahiro Yamamoto,et al.  CONVOLUTION INEQUALITIES AND APPLICATIONS , 2003 .