A New Approach for Super resolution by Using Web Images and FFT Based Image Registration
Citation
Archana Vijayan , Vincy Salam. "A New Approach for Super resolution by Using Web Images and FFT Based Image Registration", International Journal of Engineering Trends and Technology (IJETT), V12(9),473-479 June 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Abstract
Preserving accuracy is a challenging issue in super resolution images. In this paper, we propose a new FFT based image registration algorithm and a sparse based super resolution algorithm to improve the accuracy of super resolution image. Given a low resolution image, our approach initially extracts the local descriptors from the input and then the local descriptors from the whole correlated images using the SIFT algorithm. Once this is completed, it will compare the local descriptors on the basis of a threshold value. The retrieved images could be having different focal length, illumination, inclination and size. To overcome the above differences of the retrieved images, we propose a new FFT based image registration algorithm. After the registration stage, we apply a sparse based super resolution on the images for recreating images with better resolution compared to the input. Based on the PSSNR calculation and SSIM comparison, we can see that the new methodology creates a better image than the traditional methods.
References
[1] Huanjing Yue, Xiaoyan Sun, , Jingyu Yang, Member and Feng Wu, “Landmark Image Super-Resolution byRetrieving Web Images,” IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 22, NO. 12, DECEMBER 2013
[2] L. Zhang and X. Wu, “An edge-guided image interpolation algorithm via directional filtering and data fusion,” IEEE Trans. Image Process., vol. 15, no. 8, pp. 2226–2238, Aug. 2006.
[3] S. C. Park, M. K. Park, and M. G. Kang, “Super-resolution image reconstruction: A technical overview,” IEEE Signal Process. Mag., vol. 20, no. 3, pp. 21–36, May 2003.
[4] S. Baker and T. Kanade, “Limits on super-resolution and how to break them,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 9,pp. 1167–1183, Sep. 2002.
[5] W. T. Freeman, T. R. Jones, and E. C. Pasztor, “Example-based super resolution,” IEEE Comput. Graph. Appl., vol. 22, no. 2, pp. 56–65,Mar./Apr. 2002.
[6] L. Sun and J. Hays, “Super-resolution from internet-scale scene matching,” in Proc. IEEE Conf. ICCP, Jun. 2012, pp. 1–12.
[7] Yung-Yuan CHIEN, Jin-Jang LEOU, and Hsuan-Ying CHEN,” Image Super-Resolution Reconstruction Using Image Registration and Error-Amended Sharp Edge Interpolation”, APSIPA ASC 2011 Xi’an,vol30, no.6,ppl.50-60,Jan 2002
[8] J. Sun and M F. Tappen, “Context-constrained hallucination for image super-resolution,” in Proc. CVPR, 2010, pp. 231–238.
[9] C. Hsu and C. Lin, “Image super-resolution via feature-based affine transform,” in Proc. 13th Int. Workshop MMSP, Oct. 2011, pp. 1–5.
[10] K. I. Kim and Y. Kwon, “Single-image super-resolution using sparse regression and natural image prior,” Pattern Anal. Mach. Intell., vol. 32, no. 6, pp. 1127–1133, Jun. 2010.
[11] O. Whyte, J . Sivic, and A. Zisserman, “Get out of my picture! Internet based inpainting,” in Proc. 20th Brit. Mach. Vis. Conf., Sep. 2009, pp. 1–11.
[11] J. Sun and M. F. Tappen, “Context-constrained hallucination for image super-resolution,” in Proc. CVPR, 2010, pp. 231–238.
References
SIFT extraction, FFT, Super resolution, Image retrieval, Bicubic Interpolation