Abstract
A new image super-resolution reconstruction (SRR) method, combined a modified K-means based Singular Value Decomposition (K-SVD) and Regularized Adaptive Matching Pursuit (RAMP) algorithm is proposed in this paper. In the modified K-SVD algorithm, the maximum sparsity is considered. First, the K-SVD denoising model is first to preprocess the Low Resolution (LR) images. And then, the high-resolution (HR) and LR images are both trained by the RAMP based K-SVD algorithm. The LR and HR dictionaries are also classed by the K-mean method. In test, a human-made and real LR image, namely millimeter wave (MMW) image are respectively used to testify our method proposed. Further, compared our image SRR method with methods of the basic K-SVD and RAMP, experimental results testified the validity of our method proposed.
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References
Liu, Y.X., Zhao, R.Z., Hu, S.H., Jiang, C.H.: Regularized adaptive matching pursuit algorithm for signal reconstruction based on compressive sensinig. Journal of Electronics & Information Technology 32(11), 2713–2717 (2010)
Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Boissonnat, J.-D., Chenin, P., Cohen, A., Gout, C., Lyche, T., Mazure, M.-L., Schumaker, L. (eds.) Curves and Surfaces 2011. LNCS, vol. 6920, pp. 711–730. Springer, Heidelberg (2012)
Dong, W.S., Zhang, L., Shi, G.: Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. IEEE Transaction on Image Process. 20(7), 1838–1857 (2011)
Yang, J.C., Wright, J., Huang, T.S.: Image super-resolution via sparse representation of raw image patchs. IEEE Transactions on Image Processing 19(11), 1–8 (2010)
Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transaction on Image Processing 54(11), 4311–4322 (2006)
Elad, M., Aharon, M.: Image denoising via learned dictionaries and sparse representation. Computer Vison and Pattern Recogniton 1, 895–900 (2006)
Sun, J., Xu, Z.B., 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)
Do, T.T., Lu, G., Nam, N., Trac, D.T.: Sparsity adaptive matching pursuit algorithm for practical compressed sensing. In: The 42nd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, California, pp. 234–240 (2008)
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Shang, L., Liu, T., Sun, Zl. (2014). Image Super-resolution Reconstruction Utilizing the Combined Method of K-SVD and RAMP. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theory. ICIC 2014. Lecture Notes in Computer Science, vol 8588. Springer, Cham. https://doi.org/10.1007/978-3-319-09333-8_51
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DOI: https://doi.org/10.1007/978-3-319-09333-8_51
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09332-1
Online ISBN: 978-3-319-09333-8
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