Abstract
Designing effective image priors is a key issue to image super-resolution. However, obtaining analytical forms for evaluating the smoothness of the priors is still a difficult and significant task. In this paper, we propose a prior-based method that divides image edges based on the hardness value, replacing the traditional binary classification of edges with a more detailed classification method. Through this partition, we can achieve smoother and better visual effect. Furthermore, we propose a non-uniform refinement approach to effectively improve the speed and reduce the processing time. Experimental results on multiple real world images have demonstrated the advantages of the proposed method over other existing prior methods both in visual effect and processing speed.
The work is supported by the National Science Foundation of China (Grant Nos.61035003, 61175042, 61021062), the National 973 Program of China (Grant No. 2009CB320702), the 973 Program of Jiangsu, China (Grant No. BK2011005) and Program for New Century Excellent Talents in University (Grant No. NCET-10-0476).
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References
Sun, J., Xu, Z., Shum, H.Y.: Image super-resolution using gradient profile prior. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)
Hou, H., Andrews, H.: Cubic splines for image interpolation and digital filtering. IEEE Transactions on Acoustics, Speech and Signal Processing 26(6), 508–517 (1978)
Sun, J., Xu, Z., Shum, H.Y.: Gradient profile prior and its applications in image super-resolution and enhancement. IEEE Transactions on Image Processing 20(6), 1529–1542 (2011)
Boykov, Y., Kolmogorov, V.: Computing geodesics and minimal surfaces via graph cuts. In: Proceedings of the Ninth IEEE International Conference on Computer Vision 2003, pp. 26–33. IEEE (2003)
Levin, A., Lischinski, D., Weiss, Y.: A closed-form solution to natural image matting. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(2), 228–242 (2008)
Dai, S., Han, M., Xu, W., Wu, Y., Gong, Y.: Soft edge smoothness prior for alpha channel super resolution. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8. IEEE (2007)
Dai, S., Han, M., Xu, W., Wu, Y., Gong, Y., Katsaggelos, A.K.: Softcuts: a soft edge smoothness prior for color image super-resolution. IEEE Transactions on Image Processing 18(5), 969–981 (2009)
Dai, S.: Conquering image imperfectness by priors with applications to image recovery (2009)
Shan, Q., Li, Z., Jia, J., Tang, C.K.: Fast image/video upsampling. ACM Transactions on Graphics (TOG) 27, 153 (2008)
Keys, R.: Cubic convolution interpolation for digital image processing. IEEE Transactions on Acoustics, Speech and Signal Processing 29(6), 1153–1160 (1981)
Irani, M., Peleg, S.: Motion analysis for image enhancement: Resolution, occlusion, and transparency. Journal of Visual Communication and Image Representation 4(4), 1993–1912 (1993)
Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence (6), 679–698 (1986)
Fattal, R.: Image upsampling via imposed edge statistics. ACM Transactions on Graphics (TOG) 26(3), 95 (2007)
Freedman, G., Fattal, R.: Image and video upscaling from local self-examples. ACM Transactions on Graphics (TOG) 30(2), 12 (2011)
Glasner, D., Bagon, S., Irani, M.: Super-resolution from a single image. In: IEEE 12th International Conference on Computer Vision 2009, pp. 349–356. IEEE (2009)
Sun, J., Zheng, N.N., Tao, H., Shum, H.Y.: Image hallucination with primal sketch priors. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2003, vol. 2, pp. II–729. IEEE (2003)
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Jia, Y., Yang, W., Gao, Y., Yin, H., Shi, Y. (2013). Image Super Resolution via Visual Prior Based Digital Image Characteristics. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_21
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DOI: https://doi.org/10.1007/978-3-642-41278-3_21
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