Abstract
Due to its low encoding complexity, compressed sensing (CS) has gained wide attention in image processing related areas such as image compression, medical imaging and remote sensing. In existing research on CS based image processing, the commonly used sparse representation scheme for image recovery is the discrete wavelet transform (DWT), which is limited by poor directionality and lack of phase space information. What’s more, the structural information of transform-domain coefficients other than pure sparsity is seldom explored. In this paper, to improve the image recovery performance, we propose a new recovery method by adopting the double-density dual-tree complex wavelet transform (DDDT-CWT) as the sparse representation scheme. In addition, the structural characteristics of the DDDT-CWT coefficients are utilized as extra prior knowledge in the recovery process to further improve the recovery quality. Extensive simulation results have been conducted, and the results show that under the same compression ratio, the proposed method has achieved considerable PSNR gain compared with the traditional recovery algorithm.
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References
Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52, 1289–1306 (2006)
Colonnese, S., Rinauro, S., Cusani, R., Scarano, G.: The restricted isometry property of the Radon-like CS matrix. In 2013 IEEE 15th International Workshop on Multimedia Signal Processing, MMSP, pp. 248–253. IEEE, Pula (2013)
Hayashi, K., Nagahara, M., Tanaka, T.: A user’s guide to compressed sensing for communications systems. IEICE Trans. Commun. 96, 685–712 (2013)
Davenport, M.A., Wakin, M.B.: Analysis of orthogonal matching pursuit using the restricted isometry property. IEEE Trans. Inf. Theory 56, 4395–4401 (2010)
Wang, W., Ni, L.: Multipath subspace pursuit for compressive sensing signal reconstruction. In: 2014 7th International Congress on Image and Signal Processing (CISP), pp. 1141–1145. IEEE (2014)
Sathyabama, B., Siva Sankari, S.G., Nayagara, S.: Fusion of satellite images using compressive sampling matching pursuit (CoSaMP) method. In: 2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), pp. 1–4. IEEE (2013)
Ekanadham, C., Tranchina, D., Simoncelli, E.P.: Recovery of sparse translation-invariant signals with continuous basis pursuit. IEEE Trans. Sig. Process. 59, 4735–4744 (2011)
Li, J., Shen, Y., Wang, Q.: Stepwise suboptimal iterative hard thresholding algorithm for compressive sensing. In: 2012 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp. 1332–1336. IEEE (2012)
Lai, L., Wang, Q., Wang, Q.: Research on one kind of improved GPSR algorithm. In: 2012 International Conference on Computer Science and Electronics Engineering (ICCSEE), pp. 715–718 (2012)
Duarte, M.F., Davenport, M.A., Takhar, D., Laska, J.N., Sun, T., Kelly, K.F., Baraniuk, R.G.: Single-Pixel imaging via compressive sampling. IEEE Sig. Process. Mag. 25, 83–91 (2008)
Goyal, V.K., Fletcher, A.K., Rangan, S.: Compressive sampling and lossy compression. IEE Sig. Process. Mag. 25, 48–56 (2008)
Selesnick, I.W.: The double-density dual-tree DWT. IEEE Trans. Signal Process. 52, 1304–1314 (2004)
Sarawale, R.K., Chougule, S.R.: Noise removal using double-density dual-tree complex DWT. In: 2013 IEEE Second International Conference on Image Information Processing (ICIIP), pp. 219–224. IEEE (2013)
Prasanthi, G., Harini, P.: Robust satellite image resolution enhancement using double density dual tree complex wavelet transform. Int. J. Adv. Trends Comput. Sci. Eng. 223–228 (2014)
Selesnick, I.W., Baraniuk, R.G., Kingsbury, N.C.: The dual-tree complex wavelet transform. IEEE Sig. Process. Mag. 22, 123–151 (2005)
Baraniuk, R.G., Cevher, V., Duarte, M.F., Hegde, C.: Model-based compressive sensing. IEEE Trans. Inf. Theory 56, 1982–2001 (2010)
Baraniuk, R.G., De Vore, R.A., Kyriazis, G., Yu, X.M.: Near best tree approximation. Adv. Comput. Math. 16, 357–373 (2002)
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Wang, Hx., Wu, Sh., Yang, Jr., Ding, Cj. (2015). High Performance DDDT-CWT Based Compressed Sensing Recovery of Images via Structured Sparsity. In: Xu, K., Zhu, H. (eds) Wireless Algorithms, Systems, and Applications. WASA 2015. Lecture Notes in Computer Science(), vol 9204. Springer, Cham. https://doi.org/10.1007/978-3-319-21837-3_51
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DOI: https://doi.org/10.1007/978-3-319-21837-3_51
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