Abstract
Image restoration is a process of reducing the effect of noise and damaged portions in the digital images, and restores images with respective values of neighboring pixels which enhances the image and restores it to original image. To perform this operation filtration, transformation, in-painting, and many other approaches were followed; compressive sensing-based approaches produce best results. In this paper, compressive sensing-based image restoration was studied with different techniques and their comparisons were laid in results section.
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References
Zhang, J., Zhao, D., Xiong, R., Ma, S., Gao, W.: Image restoration using joint statistical modeling in a space-transform domain. IEEE Trans. Circ. Syst. Video Technol. 24(6), 915–928 (2014)
Yang, J., Sha, W.E.I., Chao, H., et al.: Multimed. Tools Appl. 75, 6189 (2016). https://doi.org/10.1007/s11042-015-2566-9
Chen, J., Iqbal, M., Yang, W., Wang, P.B., Sun, B.: Mitigation of azimuth ambiguities in spaceborne stripmap SAR images using selective restoration. IEEE Trans. Geosci. Remote Sens. 52(7), 4038–4045 (2014)
Avolio, C., Mario, C., Di Martino, G., Antonio, I., Flavia, M,, Giuseppe, R., Daniele, R., Massimo, Z.: A method for the reduction of ship detection false alarms due to SAR azimuth ambiguity. In: 2014 IEEE Geoscience and Remote Sensing Symposium (2014)
Xie, Z., et al.: Restoration of sparse aperture images using spatial modulation diversity technology based on a binocular telescope testbed. IEEE Photon. J. 9(3), 1–11 (2017)
Huang, S., Zhu, J.: Removal of salt-and-pepper noise based on compressed sensing. Electron. Lett. 46(17), 1198–1199 (2010)
Chunhong, C., Gao, X.: Compressed sensing image restoration based on data-driven multi-scale tight frame. J. Comput. Appl. Math. 309, pp. 622–629 (2017)
Mun, S., Fowler, J.E.: Block compressed sensing of images using directional transforms. In: 2010 Data Compression Conference, Snowbird, UT, pp. 547–547 (2010)
Nie, G., Fu, Y., Zheng, Y., Huang, H.: Image restoration from patch-based compressed sensing measurement arXiv:1706.00597 (2017)
Zhang, J., Zhao, D., Zhao, C., Xiong, R., Ma, S., Gao, W.: Image compressive sensing recovery via collaborative sparsity. IEEE J. Emerg. Sel. Top. Circ. Syst. 2(3), 380–391 (2012)
Eslahi, N., Aghagolzadeh, A.: Compressive sensing image restoration using adaptive curvelet thresholding and nonlocal sparse regularization. IEEE Trans. Image Process. 25(7), 3126–3140 (2016)
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Ramesh, C., Venkat Rao, D., Murthy, K.S.N. (2019). Short Note on the Application of Compressive Sensing in Image Restoration. In: Satapathy, S., Bhateja, V., Das, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 105. Springer, Singapore. https://doi.org/10.1007/978-981-13-1927-3_28
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DOI: https://doi.org/10.1007/978-981-13-1927-3_28
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