A modified spectral conjugate gradient projection method for signal recovery

Signal, Image and Video Processing - Tập 12 - Trang 1455-1462 - 2018
Zhong Wan1, Jie Guo1, Jingjing Liu1, Weiyi Liu1
1School of Mathematics and Statistics, Central South University, Changsha, China

Tóm tắt

In this paper, signal recovery problems are first reformulated as a nonlinear monotone system of equations such that the modified spectral conjugate gradient projection method proposed by Wan et al. can be extended to solve the signal recovery problems. In view of the equations’ analytic properties, an improved projection-based derivative-free algorithm (IPBDF) is developed. Compared with the similar algorithms available in the literature, an advantage of IPBDF is that the search direction is always sufficiently descent as well as being close to the quasi-Newton direction, without requirement of computing the Jacobian matrix. Then, IPBDF is applied into solving a number of test problems for reconstruction of sparse signals and blurred images. Numerical results indicate that the proposed method either can recover signals in less CPU time or can reconstruct the images with higher quality than the other similar ones.

Tài liệu tham khảo

Liu, H., Peng, J.: Sparse signal recovery via alternating projection method. Sig. Process. 143, 161–170 (2018) Ambat, S.K., Hari, K.V.S.: An iterative framework for sparse signal reconstruction algorithms. Sig. Process. 108, 351–364 (2015) Zhang, H., Dong, Y., Fan, Q.: Wavelet frame based Poisson noise removal and image deblurring. Sig. Process. 137, 363–372 (2017) Chen C., Tramel E.W., Fowler J.E.: Compressed-sensing recovery of images and video using multihypothesis predictions. In: Proceedings of the 45th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, 2011, pp. 1193–1198 Zhang, J., Xiang, Q., Yin, Y., et al.: Adaptive compressed sensing for wireless image sensor networks. Multimed. Tools Appl. 76(3), 4227–4242 (2017) Kumar, A.: Deblurring of motion blurred images using histogram of oriented gradients and geometric moments. Sig. Process. Image Commun. 55, 55–65 (2017) D’Acunto, M., Benassi, A., Moroni, D., et al.: 3D image reconstruction using Radon transform. SIViP 10(1), 1–8 (2016) Zhang, X.M., Han, Q.L.: Network-based H\(_\infty \) filtering using a logic jumping-like trigger. Automatica 49(5), 1428–1435 (2013) Mallat, S.G., Zhang, Z.: Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal Process. 41(12), 3397–3415 (1993) Tropp, J.A., Gilbert, A.C.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53(12), 4655–4666 (2007) Zhang, L., Zhou, W.D., Chen, G.R., et al.: Sparse signal reconstruction using decomposition algorithm. Knowl.-Based Syst. 54, 172–179 (2013) Narayanan, S., Sahoo, S.K., Makur, A.: Greedy pursuits assisted basis pursuit for reconstruction of joint-sparse signals. Sig. Process. 142, 485–491 (2018) Kowalski, M., Torrésani, B.: Sparsity and persistence: mixed norms provide simple signal models with dependent coefficients. SIViP 3(3), 251–264 (2009) Ouaddah, A., Boughaci, D.: Harmony search algorithm for image reconstruction from projections. Appl. Soft Comput. 46, 924–935 (2016) Bahaoui, Z., El Fadili, H., Zenkouar, K., et al.: Exact Zernike and pseudo-Zernike moments image reconstruction based on circular overlapping blocks and Chamfer distance. SIViP 11(1), 1–8 (2017) Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006) Wang X., Sabne A., Kisner S., et al.: High performance model based image reconstruction. In: Proceedings of the 21st ACM 2016: SIGPLAN Symposium on Principles and Practice of Parallel Programming, vol. 2. ACM (2016) Bruckstein, A.M., Donoho, D.L., Elad, M.: From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Rev. 51(1), 34–81 (2009) Donoho, D.L.: For most large underdetermined systems of linear equations the minimal \(\ell _1\) norm solution is also the sparsest solution. Commun. Pure Appl. Math. 59(6), 797–829 (2006) Daubechies, I., Defrise, M., De Mol, C.: An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Commun. Pure Appl. Math. 57(11), 1413–1457 (2004) Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2(1), 183–202 (2009) Hale, E.T., Yin, W., Zhang, Y.: A fixed-point continuation method for \(\ell _1\) regularized minimization with applications to compressed sensing. CAAM TR07-07, Rice University 43, 44 (2007) Huang, S., Wan, Z.: A new nonmonotone spectral residual method for nonsmooth nonlinear equations. J. Comput. Appl. Math. 313(15), 82–101 (2017) Figueiredo, M.A.T., Nowak, R.D., Wright, S.J.: Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems. IEEE J. Sel. Top. Signal Process. 1(4), 586–597 (2007) Xiao, Y., Zhu, H.: A conjugate gradient method to solve convex constrained monotone equations with applications in compressive sensing. J. Math. Anal. Appl. 405(1), 310–319 (2013) Xiao, Y., Wang, Q., Hu, Q.: Nonsmooth equations based method for \(l_1\) norm problems with applications to compressed sensing. Nonlinear Anal. Theory Methods Appl. 74(11), 3570–3577 (2011) Wan, Z., Liu, W.Y., Wang, C.: An improved projection based derivative-free algorithm for solving nonlinear monotone symmetric equations. Pac. J. Optim. 12(3), 603–622 (2016) Lajevardi, S.M.: Structural similarity classifier for facial expression recognition. SIViP 8(6), 1103–1110 (2014)