Relation between penalized least squares regression and Bayesian estimation in AWGN based on novel penalty function of Pareto density
Tài liệu tham khảo
Kim, 2016, A vehicular positioning with GPS/IMU using adaptive control of filter noise covariance, ICT Express, 2, 41, 10.1016/j.icte.2016.03.001
Kim, 2021, Learning-based accelerated sparse signal recovery algorithms, ICT Express, 7, 398, 10.1016/j.icte.2021.03.011
Makitalo, 2013, Optimal inversion of the generalized Anscombe transformation for Poisson–Gaussian noise, IEEE Trans. Image Process., 22, 91, 10.1109/TIP.2012.2202675
Dabov, 2007, Image denoising by sparse 3-D transform-domain collaborative filtering, IEEE Trans. Image Process., 16, 2080, 10.1109/TIP.2007.901238
Kittisuwan, 2018, Speckle noise reduction of medical imaging via logistic density in redundant wavelet domain, Int. J. Artif. Intell. Tools, 27, 10.1142/S0218213018500069
Fadili, 2005, Analytical form for a Bayesian wavelet estimator of images using the Bessel K form densities, IEEE Trans. Image Process., 14, 231, 10.1109/TIP.2004.840704
Wei, 2018, A survey on nonconvex regularization based sparse and low-rank recovery in signal processing, statistics, and machine learning, IEEE Acess, 6, 69883, 10.1109/ACCESS.2018.2880454
Wang, 2020, Non-negative variance component estimation for the partial EIV model by the expectation maximization algorithm, geomatics, Nat. Hazards Risk, 11, 1278, 10.1080/19475705.2020.1785955
Wang, 2021, Penalized total least squares method for dealing with systematic errors in partial EIV model and its precision estimation, Geod. Geodyn., 12, 249, 10.1016/j.geog.2021.04.001
Combettes, 2007, Proximal thresholding algorithm for minimization over orthonormal bases, SIAM J. Optim., 18, 1351, 10.1137/060669498
Chen, 2014, Group-sparse signal denoising: Non-convex regularization, convex optimization, IEEE Trans. Signal Process., 62, 3464, 10.1109/TSP.2014.2329274
Parekh, 2015, Convex denoising using non-convex tight frame regularization, IEEE Signal Process. Lett., 22, 1786, 10.1109/LSP.2015.2432095
Figueiredo, 2007, Majorization-minimization algorithms for wavelet-based image restoration, IEEE Trans. Image Process., 16, 2980, 10.1109/TIP.2007.909318
Goldstein, 2009, The split bregman method for L1-regularized problems, SIAM J. Imaging Sci., 2, 323, 10.1137/080725891
Blake, 1987
Selesnick, 2014, Sparse signal estimation by maximally sparse convex optimization, IEEE Trans. Signal Process., 62, 1078, 10.1109/TSP.2014.2298839
Nikolova, 2011
Geman, 1992, Constrained restoration and the recovery of discontinuities, IEEE Trans. Pattern Anal. Mach. Intell., 14, 367, 10.1109/34.120331
Gribonval, 2011, Should penalized least squares regression be interpreted as maximum a posteriori estimation?, IEEE Trans. Signal Process., 59, 2405, 10.1109/TSP.2011.2107908
Selesnick, 2017, Sparse signal approximation via nonseparable regularization, IEEE Trans. Signal Process., 65, 2561, 10.1109/TSP.2017.2669904
F. Shi, I.W. Selesnick, Multivariate quasi-Laplacian mixture models for wavelet-based image denoising, in: 2006 Proc. IEEE Inter. Conf. Image Process. (ICIP), Atlanta, USA, 2006, pp. 2625–2628.
Fan, 2001, Variable selection via nonconcave penalized likelihood and its oracle properties, J. Amer. Statist. Assoc., 96, 1348, 10.1198/016214501753382273
Bui, 1998, Translation-invariant denoising using multiwavelets, IEEE Trans. Signal Process., 46, 3414, 10.1109/78.735315
D.L. Donoho, A. Maleki, M. Shahram, Wavelab 850 [Online]. Available: http://www-stat.stanford.edu/7Ewavelab/.
Gonzalez, 2006