A local structure adaptive super-resolution reconstruction method based on BTV regularization

Super-resolution (SR) image reconstruction has been one of the hottest research fields in recent years. The main idea of SR is to utilize complementary information from a set of low resolution (LR) images of the same scene to reconstruct a high-resolution image with more details. Under the framework of the regularization based SR, this paper presents a local structure adaptive BTV regularization based super-resolution reconstruction method to overcome the shortcoming of the Bilateral Total Variation (BTV) super resolution reconstruction model. The proposed method adaptively chooses prior model and regularization parameter according to the local structures. Experimental results show that the proposed method can get better reconstruction results and significantly reduces the manual workload of the regularization parameter selection.

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

[2]  W. Zeng,et al.  A robust variational approach to super-resolution with nonlocal TV regularisation term , 2013 .

[3]  Thomas S. Huang,et al.  Image Super-Resolution Via Sparse Representation , 2010, IEEE Transactions on Image Processing.

[4]  Edmund Y. Lam,et al.  Application of Tikhonov Regularization to Super-Resolution Reconstruction of Brain MRI Images , 2007, MIMI.

[5]  Andrew Zisserman,et al.  Super-resolution enhancement of text image sequences , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[6]  J. L. Harris,et al.  Diffraction and Resolving Power , 1964 .

[7]  Michael Elad,et al.  Multiframe demosaicing and super-resolution of color images , 2006, IEEE Transactions on Image Processing.

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

[9]  Michael Elad,et al.  Fast and Robust Multi-Frame Super-Resolution , 2004, IEEE Transactions on Image Processing.

[10]  Liangpei Zhang,et al.  Adaptive Multiple-Frame Image Super-Resolution Based on U-Curve , 2010, IEEE Transactions on Image Processing.

[11]  Hao Chen,et al.  Blind Super-Resolution Image Reconstruction Based on POCS Model , 2009, 2009 International Conference on Measuring Technology and Mechatronics Automation.

[12]  Marek Rudnicki,et al.  Regularization Parameter Selection in Discrete Ill-Posed Problems — The Use of the U-Curve , 2007, Int. J. Appl. Math. Comput. Sci..

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

[14]  Michael Elad,et al.  Super-Resolution Without Explicit Subpixel Motion Estimation , 2009, IEEE Transactions on Image Processing.

[15]  A. N. Rajagopalan,et al.  Superresolution of License Plates in Real Traffic Videos , 2007, IEEE Transactions on Intelligent Transportation Systems.

[16]  Aggelos K. Katsaggelos,et al.  Iterative algorithm for improving the resolution of video sequences , 1996, Other Conferences.

[17]  Moon Gi Kang,et al.  Regularized adaptive high-resolution image reconstruction considering inaccurate subpixel registration , 2003, IEEE Trans. Image Process..

[18]  Edward A. Watson,et al.  High-Resolution Image Reconstruction from a Sequence of Rotated and Translated Frames and its Application to an Infrared Imaging System , 1998 .

[19]  Michael Elad,et al.  Super Resolution With Probabilistic Motion Estimation , 2009, IEEE Transactions on Image Processing.

[20]  Xiaohai He,et al.  Video superresolution reconstruction based on subpixel registration and iterative back projection , 2009, J. Electronic Imaging.

[21]  S. Chaudhuri,et al.  High-resolution slow-motion sequencing: how to generate a slow-motion sequence from a bit stream , 2005, IEEE Signal Processing Magazine.

[22]  Kwang In Kim,et al.  Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[24]  Nelson D. A. Mascarenhas,et al.  Multispectral image data fusion using POCS and super-resolution , 2006, Comput. Vis. Image Underst..

[25]  Zhu Jianjun.  POCS Super-Resolution Sequence Image Reconstruction Based on Improvement Approach of Keren Registration Method , 2006 .

[26]  Min-Cheol Hong,et al.  An iterative weighted regularized algorithm for improving the resolution of video sequences , 1997, Proceedings of International Conference on Image Processing.