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http://dx.doi.org/10.5573/ieie.2014.51.3.105

Multiple Shortfall Estimation Method for Image Resolution Enhancement  

Kim, Won-Hee (Pukyong National University)
Kim, Jong-Nam (Pukyong National University)
Jeong, Shin-Il (Pukyong National University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.51, no.3, 2014 , pp. 105-111 More about this Journal
Abstract
Image resolution enhancement is a technique to generate high-resolution image through improving resolution of low-resolution obtained image. It is important to estimate correctly missing pixel value in low-resolution obtained image for image resolution enhancement. In this paper, multiple shortfall estimation method for image resolution enhancement is proposed. The proposed method estimate separate multiple shortfall by predictive degradation-restoration processing in sub-images of obtained image, and generate result image combining the estimated shortfall and interpolated obtained-image. Lastly, final reconstruction image is generated by deblurring of the result image. The experimental results demonstrate that the proposed method has the best results of all compared methods in objective image quality index: PSNR, SSIM, and FSIM. The quality of reconstructed image is superior to all compared methods, and the proposed method has better lower computational complexity than compared methods. The proposed method can be useful for image resolution enhancement.
Keywords
Image resolution enhancement; Image interpolation; Shortfall estimation; Super resolution;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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1 Du Sic Yoo, Ki Sun Song, and Moon Gi Kang, "A Deblurring Algorithm Combined with Edge Directional Color Demosaicing for Reducing Interpolation Artifacts," Journal of The Institute of Electronics Engineers of Korea, Vol. 50, No. 7, pp. 205-215, Jul. 2013.   과학기술학회마을   DOI   ScienceOn
2 Changwon Choi and Joonhwan Yi, "An Interpolation Method for a Barrel Distortion Using Nearest Pixelson a Corrected Image", Journal of The Institute of Electronics Engineers of Korea, Vol.50, No.7, pp. 181-190, Jul. 2013.
3 X. Li and M.T. Orchard, "New edge-directed interpolation," IEEE Transactions on Image Processing, Vol. 10, No. 10, pp. 1521-1527, 2001.   DOI   ScienceOn
4 N. Asuni and A. Giachetti, "Accuracy improvements and artifacts removal in edge based image interpolation," Proc. Int. Conf. Computer Vision Theory and Applications, pp. 58-65, 2008.
5 A. Giachetti and N. Asuni, "Real-Time Artifact-Free Image Upscaling," IEEE Transactions on Image Processing, Vol. 20, No. 10, pp. 2760-2768, 2011.   DOI   ScienceOn
6 D. Zhou, X. Shen, and W. Dong, "Image zooming using directional cubic convolution interpolation," IET Image Processing, Vol. 6, Issue 6, pp. 627-634, 2012.   DOI   ScienceOn
7 L. Zhang and X. Wu, "An Edge-Guided Image Interpolation Algorithm via Directional Filtering and Data Fusion," IEEE Transactions on Image Processing, Vol. 15, No. 8, pp. 2226-2238, 2006.   DOI   ScienceOn
8 Z. Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli, "Image Quality Assessment: From Error Visibility to Structural Similarity," IEEE Transactions on Image Processing, Vol. 13, No. 4, pp. 600-612, 2004.   DOI   ScienceOn
9 L. Zhang, L. Zhang, X. Mou, and D. Zhang, "FSIM: A Feature Similarity Index for Image Quality Assessment," IEEE Transactions on Image Processing, Vol. 20, No. 8, pp. 2378-2386, 2011.   DOI   ScienceOn