Directionally adaptive single frame image super resolution

The single image super resolution recovers missing high resolution details so as to reconstruct a high resolution image from a single low resolution image. This paper proposes a novel directionally adaptive, learning-based, single image super resolution method using multiple direction wavelet transform, called directionlets. Here, critically sampled directionlets are used to capture directional features effectively and to extract edge information along different directions from a set of available high resolution images. This information is used as the training set for super resolving a low resolution input image. The directionlet coefficients at finer scales of its high resolution image are learned locally from this training set and the inverse directionlet transform recovers the super resolved high resolution image. The simulation results showed that the proposed directionlet approach outperforms standard interpolation techniques like cubic spline interpolation as well as standard wavelet-based learning, both visually and in terms of the mean squared error (MSE) values. The SNR scores for cubic spline interpolation, wavelet and directionlet method are 13.6998 dB, 23.8324 dB and 30.8654 dB respectively for Barbara.

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

[2]  Nirmal K. Bose,et al.  Recursive reconstruction of high resolution image from noisy undersampled multiframes , 1990, IEEE Trans. Acoust. Speech Signal Process..

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

[4]  Baltasar Beferull-Lozano,et al.  Directionlets: anisotropic multidirectional representation with separable filtering , 2006, IEEE Transactions on Image Processing.

[5]  Stephen J. Roberts,et al.  A Sampled Texture Prior for Image Super-Resolution , 2003, NIPS.

[6]  Daniel Gross,et al.  Improved resolution from subpixel shifted pictures , 1992, CVGIP Graph. Model. Image Process..

[7]  William T. Freeman,et al.  Learning low-level vision , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[8]  Andrew Blake,et al.  Super-resolution Enhancement of Video , 2003, AISTATS.

[9]  Andrew Zisserman,et al.  Super-resolution from multiple views using learnt image models , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[10]  Peyman Milanfar,et al.  A computationally efficient superresolution image reconstruction algorithm , 2001, IEEE Trans. Image Process..

[11]  Manjunath V. Joshi,et al.  A learning-based method for image super-resolution from zoomed observations , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[12]  Dahua Lin,et al.  Hallucinating faces: TensorPatch super-resolution and coupled residue compensation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[13]  Xiaogang Wang,et al.  Hallucinating face by eigentransformation , 2005, IEEE Trans. Syst. Man Cybern. Part C.

[14]  Harry Shum,et al.  A two-step approach to hallucinating faces: global parametric model and local nonparametric model , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[15]  Yücel Altunbasak,et al.  Eigenface-domain super-resolution for face recognition , 2003, IEEE Trans. Image Process..

[16]  Takeo Kanade,et al.  Limits on Super-Resolution and How to Break Them , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[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]  Manjunath V. Joshi,et al.  Single‐frame image super‐resolution using learned wavelet coefficients , 2004, Int. J. Imaging Syst. Technol..

[20]  Deepu Rajan,et al.  Simultaneous Estimation of Super-Resolved Scene and Depth Map from Low Resolution Defocused Observations , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Michael Elad,et al.  Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images , 1997, IEEE Trans. Image Process..

[22]  Manjunath V. Joshi,et al.  Single Frame Super-Resolution: A New Learning Based Approach and Use of IGMRF Prior , 2008, 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing.

[23]  Dattatraya S. Bormane,et al.  Super Resolution Using Neural Network , 2008, 2008 Second Asia International Conference on Modelling & Simulation (AMS).