Abstract
In this paper, an enhanced super-resolution image reconstruction method based on Markov random field (MRF) is proposed. This work incorporates an efficient training set. The most appropriate training set is utilized to find the similar patches based on the associated local activity of the image patch to improve the quality of reconstruction and to reduce the computational overload. Secondly, locality-sensitive hashing (LSH) method is used to search the similar patches from the training set. Finally, a robust and transformation invariant similarity measure named Image Euclidean distance (IMED) is incorporated to measure the patch similarity. Unlike traditional Euclidean distance, IMED measure considers the spatial relationships of pixels and provides an instinctively reasonable result. Experimental results demonstrate the outperforming nature of the proposed method in terms of reconstruction quality and computational time than several existing state-of-the-art methods.
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References
Rajan, Deepu and Chaudhuri, Subhasis.: An MRF-based approach to generation of super-resolution images from blurred observations. Journal of Mathematical Imaging and Vision. 16, 5–15 (2002)
Freeman, William T and Jones, Thouis R and Pasztor, Egon C.: Example-based super-resolution. J. Computer Graphics and Applications, IEEE. 22, 56–65 (2002)
Freeman, William T and Pasztor, Egon C and Carmichael, Owen T.: Learning low-level vision. J. International journal of computer vision. 40, 25–47 (2000)
Chen, Xiaoxuan and Qi, Chun.: Document image super-resolution using structural similarity and Markov random field. J. Image Processing, IET. 8, 687–698 (2014)
Gionis, Aristides and Indyk, Piotr and Motwani, Rajeev and others.: Similarity search in high dimensions via hashing: VLDB. 99, 518–529 (1999)
Wang, Liwei and Zhang, Yan and Feng, Jufu.: On the Euclidean distance of images. J. Pattern Analysis and Machine Intelligence, IEEE Transactions on. 27, 1334–1339 (2005)
The Berkeley Segmentation Dataset and Benchmark, https://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/
Computer Vision Group, http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/
Zhang, Haichao and Yang, Jianchao and Zhang, Yanning and Huang, Thomas S.: Non-local kernel regression for image and video restoration:Computer Vision–ECCV 2010, 566–579 (2010)
Zhang, Lin and Zhang, Lei and Mou, Xuanqin and Zhang, David.: FSIM: a feature similarity index for image quality assessment. J. Image Processing, IEEE Transactions on. 20, 2378–2386 (2011)
Channappayya, Sumohana S and Bovik, Alan Conrad and Heath Jr, Robert W.: Rate bounds on SSIM index of quantized images. J. Image Processing, IEEE Transactions on. 17, 1624–1639 (2008)
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Nayak, R., Sai Krishna, L.V., Patra, D. (2018). Enhanced Super-Resolution Image Reconstruction Using MRF Model. In: Sa, P., Sahoo, M., Murugappan, M., Wu, Y., Majhi, B. (eds) Progress in Intelligent Computing Techniques: Theory, Practice, and Applications. Advances in Intelligent Systems and Computing, vol 518. Springer, Singapore. https://doi.org/10.1007/978-981-10-3373-5_20
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DOI: https://doi.org/10.1007/978-981-10-3373-5_20
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