Gaussian Noised Single-Image Super Resolution Reconstruction

Feng Qing Qin; Li Hong Zhu; Li Lan Cao; Wa Nan Yang

doi:10.4028/www.scientific.net/AMM.457-458.1032

Paper Titles

A New Approach for 3D Edge Extraction by Fusing Point Clouds and Digital Images
p.1012

Study on the Application of Damping Control Method in Multi-Solution Interval of the QZS System
p.1017

Parallel Rendering Strategies for 3D Emulational Scene of Live Working
p.1021

Robust Visual Tracking Using Particle Filtering on SL(3) Group
p.1028

Gaussian Noised Single-Image Super Resolution Reconstruction
p.1032

The Simulated Annealing Algorithm Based on Multi-Populations Application of TSP
p.1037

Design and Realization of a Certain Type of Radio RF Circuit Board Detecting and Teaching Platform
p.1042

A Urinary Sediment Segmentation Algorithm of Cast and Epithelial Cells
p.1046

Research on Target Tracking Algorithm Based on Particle Filter and Mean-Shift
p.1050

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 457-458Gaussian Noised Single-Image Super Resolution...

Gaussian Noised Single-Image Super Resolution Reconstruction

Abstract:

A framework is proposed to reconstruct a super resolution image from a single low resolution image with Gaussian noise. The degrading processes of Gaussian blur, down-sampling, and Gaussian noise are all considered. For the low resolution image, the Gaussian noise is reduced through Wiener filtering algorithm. For the de-noised low resolution image, iterative back projection algorithm is used to reconstruct a super resolution image. Experiments show that de-noising plays an important part in single-image super resolution reconstruction. In the super reconstructed image, the Gaussian noise is reduced effectively and the peak signal to noise ratio (PSNR) is increased.

You might also be interested in these eBooks

Frontiers of Mechanical Engineering and Materials Engineering II

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 457-458)

Pages:

1032-1036

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.457-458.1032

Citation:

Cite this paper

Online since:

October 2013

Authors:

Feng Qing Qin*, Li Hong Zhu, Li Lan Cao, Wa Nan Yang

Keywords:

Gaussian Noise, Iterative Back Projection, Single-Image, Super Resolution, Wiener Filter

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] J. Tian, K. -K. Ma, A survey on super-resolution imaging, SIVip. 5 (2011) 329-342.

Google Scholar

[2] F.Q. Qin, X.H. He, W.L. Chen, X.M. Yang, W. Wu, Video super-resolution reconstruction based on sub-pixel registration and iterative back projection, Journal of Electronic Imaging. 18(1) (2009) 1-11.

DOI: 10.1142/9789812799524_0071

Google Scholar

[3] X.H. Zhang, M. Tang, R.F. Tong, Robust super resolution of compressed video, Visual Computer. 28 (2012) 1167-1180.

DOI: 10.1007/s00371-011-0666-8

Google Scholar

[4] D.C. Garcia, Super resolution for multiview images using depth information, IEEE Transactions on Circuits and System for Video Technology. 22(9) (2013) 1249-1256.

DOI: 10.1109/tcsvt.2012.2198134

Google Scholar

[5] S.Y. Yang, M. Wang, Y.G. Chen, Y.X. Sun, Single-image super-resolution Reconstruction via learned geometric dictionaries and clusters sparse coding, IEEE Transactions on Image Processing. 21(9) (2012) 4016-4028.

DOI: 10.1109/tip.2012.2201491

Google Scholar

[6] A. Rueda, N. Malpica, E. Romero, Single-image super resolution of brain MR images using overcomplete dictionaries, Medical Image Analysis. 17 (2013) 113-132.

DOI: 10.1016/j.media.2012.09.003

Google Scholar