IJIGSP Vol. 7, No. 3, 8 Feb. 2015
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Image interpolation, nonlocal autoregressive model, sparse representation, super-resolution, structural similarity index, structural content
Sparse representation based super resolution deals with the problem of reconstructing a high resolution image from one or several of its low resolution counterparts. In this case the low resolution image is modelled as the down-sampled version of its high resolution counterpart after blurring. When the blurring kernel is the Dirac delta function, i.e. the low resolution image is directly down sampled from its high resolution counterpart without blurring and the super-resolution problem becomes an image interpolation problem. In such cases, the conventional sparse representation models become less effective, because the data fidelity term fails to constrain the image local structures. In natural images, the given image patch can be modelled as the linear combination of nonlocal similar neighbours. In this paper image nonlocal self-similarity for image interpolation is introduced. More specifically, wavelet based a nonlocal autoregressive model (NARM) is proposed and taken as the data fidelity term in sparse representation model. Our experimental results on benchmark test images clearly demonstrate that the proposed wavelet-NARM based image interpolation method outperforms the reconstruction of edge structures and suppression of jaggy/ringing artefacts, achieving the best image interpolation results so far in terms of PSNR as well as perceptual quality metrics such as structural similarity index and structural content. The proposed method is applied on bio medical images to emphasis on diagnostic information.
Sushma M, Malaya Kumar Nath, Lokeshwari R, Premalatha T, Santhini J V,"Wavelet-NARM Based Sparse Representation for Bio Medical Images", IJIGSP, vol.7, no.3, pp.38-44, 2015. DOI: 10.5815/ijigsp.2015.03.06
[1]Rafel C. Gonzalez, Richard E. Woods, "Digital Image Processing," 3rd Edition, Prentice Hall publications, 2008.
[2]R. G. Keys, "Cubic convolution interpolation for digital image processing," IEEE Trans. Acoust., Speech, Signal Process., vol. 29, no. 6, pp. 1153–1160, Dec. 1981.
[3]H. S. Hou and H. C. Andrews, "Cubic splines for image
interpolation and digital filtering," IEEE Trans. Acoust., Speech, Signal Process., vol. 26, no. 6, pp. 508–517, Dec. 1978.
[4]X. Zhang and X. Wu, "Image interpolation by adaptive 2D autoregressive modeling and soft-decision estimation," IEEE Trans. Image Process., vol. 17, no. 6, pp. 887–896, Jun. 2008.
[5]J. Yang, J. Wright, T. Huang, and Y. Ma, "Image super-resolution via sparse representation," IEEE Trans. Image Process., vol. 19, no. 11, pp. 2861–2873, Nov. 2010.
[6]W. Dong, L. Zhang, G. Shi, and X. Wu, "Image de blurring and super resolution by adaptive sparse domain selection and adaptive regularization," IEEE Trans. Image Process., vol. 20, no. 7, pp. 1838–1857, Jul. 2011.
[7]Jian Zhanga, Chen Zhaob, Ruiqin Xiongb, Siwei Mab, Debin Zhaoa "Image Super-Resolution via Dual-Dictionary Learning And Sparse Representation," in IEEE International Symposium on Circuits and Systems (ISCAS), 2012.
[8]A. M. Bruckstein, D. L. Donoho, and M. Elad, "From sparse solutions of systems of equations to sparse modeling of signals and images," SIAM Rev., vol. 51, no. 1, pp. 34–81, Feb. 2009.
[9]E. Candès, M. B. Wakin, and S. P. Boyd, "Enhancing sparsity by reweighted L1 minimization," J. Fourier Anal. Appl., vol. 14, no. 5, pp. 877–905, 2008.
[10]M. Elad, M. A. T. Figueiredo, and Y. Ma, "On the role of sparse and redundant representations in image processing," Proc. IEEE, vol. 98, no. 6, pp. 972–982, Jun. 2010.
[11]R. Rubinstein, A. M. Bruckstein, and M. Elad, "Dictionaries for sparse representation modeling," Proc. IEEE, vol. 98, no. 6, pp. 1045–1057, Jun. 2010.
[12]E. Candès, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans. Inf. Theory, vol. 52, no. 2, pp. 489–509, Feb. 2006.
[13]E. Candès and T. Tao, "Near optimal signal recovery from random projections: Universal encoding strategies?" IEEE Trans. Inf. Theory, vol. 52, no. 12, pp. 5406–5425, Dec. 2006.
[14]J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, "Non-local sparse models for image restoration," in Proc. IEEE Int. Conf. Comput. Vision, Tokyo, Japan, Sep. 2009, pp. 2272–2279.
[15]Weisheng Dong, Lei Zhang, Rastislav Lukac, Guangming Shi, "Sparse Representation Based Image Interpolation With Nonlocal Autoregressive Modeling," in IEEE Transactions On Image Processing, Vol. 22, No. 4, April 2013.
[16]Piotr Porwik, Agnieszka Lisowska, "The Haar–Wavelet Transform in Digital Image Processing: Its Status and Achievements", by Machine GRAPHICS & VISION vol.13, 2004, pp .79-98.
[17]Kyle Nelson, Asim Bhatti, Saeid Nahavandi "Performance Evaluation of Multi Frame Super Resolution Algorithms" in International Conference on Digital Image Computing Techniques and Applications (DICTA), 2012.
[18]Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: From error measurement to structural similarity," IEEE Trans. Image Process., vol. 3, no. 4, pp. 600–612, Apr. 2004.
[19]M. Irani and S. Peleg, "Motion analysis for image enhancement: Resolution, occlusion, and transparency," J. Visual Commun. Image Represent., vol. 4, no. 4, pp. 324–335, Dec. 1993.
[20]A. Buades, B. Coll, and J. M. Morel, "A non-local algorithm for image denoising," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2005, pp. 60–65.
[21]A. Buades, B. Coll, and J. M. Morel, "Nonlocal image and movie denoising," Int. J. Comput. Vis., vol. 76, no. 2, pp. 123–139, 2008.
[22]K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, "Image denoising by sparse 3-D transform-domain collaborative filtering," IEEE Trans. Image Process., vol. 16, no. 8, pp. 2080–2095, Aug. 2007.
[23]L. Zhang, W. Dong, D. Zhang, and G. Shi, "Two-stage image denoising by principal component analysis with local pixel grouping," Pattern Recognit., vol. 43, pp. 1531–1549, Apr. 2010.
[24]S. Kindermann, S. Osher, and P. W. Jones, "Deblurring and denoising of images by nonlocal functionals," Multisc. Model. Simul., vol. 4, no. 4, pp. 1091–1115, 2005.
[25]X. Zhang, M. Burger, X. Bresson, and S. Osher, "Bregmanized nonlocal regularization for deconvolution and sparse reconstruction," Dept. Math., UCLA, Los Angeles, Tech. Rep. 09-03, 2009.
[26]M. Protter, M. Elad, H. Takeda, and P. Milanfar, "Generalizing the nonlocal-means to super-resolution reconstruction," IEEE Trans. Image Process., vol. 18, no. 1, pp. 36–51, Jan. 2009.
[27]W. Dong, G. Shi, L. Zhang, and X. Wu, "Super-resolution with nonlocal regularized sparse representation," Proc. SPIE Visual Commun. Image Process., vol. 7744, p. 77440H, Jul. 2010.
[28]Michael Elad "Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing", Springer, ISBN 978-1-4419-7010-7.