AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Computational Visual Media Article
PDF (6.5 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

High-resolution images based on directional fusion of gradient

Liqiong Wu1, Yepeng Liu1,  Brekhna1, Ning Liu1, Caiming Zhang1( )
School of Computer Science and Technology, Shandong University, Jinan 250101, China.
Show Author Information

Abstract

This paper proposes a novel method for image magnification by exploiting the property that the intensity of an image varies along the direction of the gradient very quickly. It aims to maintain sharp edges and clear details. The proposed method first calculates the gradient of the low-resolution image by fitting a surface with quadratic polynomial precision. Then, bicubic interpolation is used to obtain initial gradients of the high-resolution (HR) image. The initial gradients are readjusted to find the constrained gradients of the HR image, according to spatial correlations between gradients within a local window. To generate an HR image with high precision, a linear surface weighted by the projection length in the gradient direction is constructed. Each pixel in the HR image is determined by the linear surface. Experimental results demonstrate that our method visually improves the quality of the magnified image. It particularly avoids making jagged edges and bluring during magnification.

Keywords

high-resolution (HR)image magnificationdirectional fusiongradient direction

References

[1]
Siu, W.-C.; Hung, K.-W. Review of image interpolation and super-resolution. In: Proceedings of Asia-Pacific Signal & Information Processing Association Annual Summit and Conference, 1-10, 2012.
[2]
Gonzalez, R. C.; Woods, R. E. Digital Image Processing, 3rd edn. Upper Saddle River, NJ, USA: Prentice-Hall, Inc., 2006.
[3]
Franke, R. Scattered data interpolation: Tests of some methods. Mathematics of Computation Vol. 38, No. 157, 181-200, 1982.
Crossref Google Scholar
[4]
Keys, R. G. Cubic convolution interpolation for digital image processing. IEEE Transactions on Acoustics, Speech and Signal Processing Vol. 29, No. 6, 1153-1160, 1981.
Crossref Google Scholar
[5]
Park, S. K.; Schowengerdt, R. A. Image reconstruction by parametric cubic convolution. Computer Vision, Graphics, and Image Processing Vol. 23, No. 3, 258-272, 1983.
Crossref Google Scholar
[6]
Duchon, C. E. Lanczos filtering in one and two dimensions. Journal of Applied Meteorology Vol. 18, No. 8, 1016-1022, 1979.
Crossref
[7]
Allebach, J.; Wong, P. W. Edge-directed interpolation. In: Proceedings of International Conference on Image Processing, Vol. 3, 707-710, 1996.
[8]
Zhang, C.; Zhang, X.; Li, X.; Cheng, F. Cubic surface fitting to image with edges as constraints. In: Proceedings of the 20th IEEE International Conference on Image Processing, 1046-1050, 2013.
[9]
Jing, G.; Choi, Y.-K.; Wang, J.; Wang, W. Gradient guided image interpolation. In: Proceedings of IEEE International Conference on Image Processing, 1822-1826, 2014.
Crossref
[10]
Li, X.; Orchard, M. T. New edge-directed interpolation. IEEE Transactions on Image Processing Vol. 10, No. 10, 1521-1527, 2001.
Crossref Google Scholar
[11]
Tam, W.-S.; Kok, C.-W.; Siu, W.-C. Modified edge-directed interpolation for images. Journal of Electronic Imaging Vol. 19, No. 1, 013011, 2010.
Crossref Google Scholar
[12]
Zhang, D.; Wu, X. An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Transactions on Image Processing Vol. 15, No. 8, 2226-2238, 2006.
Crossref Google Scholar
[13]
Zhang, L.; Zhang, C.; Zhou, Y.; Li, X. Surface interpolation to image with edge preserving. In: Proceedings of the 22nd International Conference on Pattern Recognition, 1055-1060, 2014.
Crossref
[14]
Fan, H.; Peng, Q.; Yu, Y. A robust high-resolution details preserving denoising algorithm for meshes. Science China Information Sciences Vol. 56, No. 9, 1-12, 2013.
Crossref Google Scholar
[15]
Chang, H.; Yeung, D.-Y.; Xiong, Y. Super-resolution through neighbor embedding. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, I, 2004.
[16]
Dong, W.; Zhang, L.; Lukac, R.; Shi, G. Sparse representation based image interpolation with nonlocal autoregressive modeling. IEEE Transactions on Image Processing Vol. 22, No. 4, 1382-1394, 2013.
Crossref Google Scholar
[17]
Freeman, W. T.; Jones, T. R.; Pasztor, E. C. Example-based super-resolution. IEEE Computer Graphics and Applications Vol. 22, No. 2, 56-65, 2002.
Crossref Google Scholar
[18]
Sun, J.; Sun, J.; Xu, Z.; Shum, H.-Y. Image super-resolution using gradient profile prior. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2008.
[19]
Wu, W.; Liu, Z.; He, X. Learning-based super resolution using kernel partial least squares. Image and Vision Computing Vol. 29, No. 6, 394-406, 2011.
Crossref Google Scholar
[20]
Yang, J.; Wright, J.; Huang, T. S.; Ma, Y. Image super-resolution via sparse representation. IEEE Transactions on Image Processing Vol. 19 No. 11, 2861-2873, 2010.
Crossref Google Scholar
[21]
Ohtake, Y.; Suzuki, H. Edge detection based multi-material interface extraction on industrial CT volumes. Science China Information Sciences Vol. 56, No. 9, 1-9, 2013.
Crossref Google Scholar
[22]
Li, C.; Gore, J. C.; Davatzikos, C. Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. Magnetic Resonance Imaging Vol. 32, No. 7, 913-923, 2014.
Crossref Google Scholar
[23]
Hore, A.; Ziou, D. Image quality metrics: PSNR vs. SSIM. In: Proceedings of the 20th International Conference on Pattern Recognition, 2366-2369, 2010.
Crossref
[24]
Wang, Z.; Bovik, A. C.; Sheikh, H. R.; Simoncelli, E. P. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing Vol. 3, No. 4, 600-612, 2004.
Crossref Google Scholar
[25]
Al-Fohoum, A. S.; Reza. A. M. Combined edge crispiness and statistical differencing for deblocking JPEG compressed images. IEEE Transactions on Image Processing Vol. 10, No. 9, 1288-1298, 2001.
Crossref Google Scholar
Computational Visual Media
Volume 2 Issue 1,
March 2016
Pages 31-43
DOI: 10.1007/s41095-016-0036-6
Cite this article:
Wu L, Liu Y, Brekhna, et al. High-resolution images based on directional fusion of gradient. Computational Visual Media, 2016, 2(1): 31-43. https://doi.org/10.1007/s41095-016-0036-6

737

Views

44

Downloads

10

Crossref

N/A

Web of Science

12

Scopus

0

CSCD

Altmetrics
Revised: 30 November 2015
Accepted: 09 December 2015
Published: 01 March 2016
© The Author(s) 2016

This article is published with open access at Springerlink.com

The articles published in this journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Other papers from this open access journal are available free of charge from http://www.springer.com/journal/41095. To submit a manuscript, please go to https://www.editorialmanager.com/cvmj.
Return