Zobrazeno 1 - 10
of 481
pro vyhledávání: '"Selesnick Ivan"'
This paper proposes a precise signal recovery method with multilayered non-convex regularization, enhancing sparsity/low-rankness for high-dimensional signals including images and videos. In optimization-based signal recovery, multilayered convex reg
Externí odkaz:
http://arxiv.org/abs/2409.14768
The past few years have seen a surge of activity around integration of deep learning networks and optimization algorithms for solving inverse problems. Recent work on plug-and-play priors (PnP), regularization by denoising (RED), and deep unfolding h
Externí odkaz:
http://arxiv.org/abs/2202.02388
Publikováno v:
IEEE Transactions on Signal Processing, vol. 69, pp. 2923-2938, 2021
This paper proposes an epigraphical relaxation (ERx) technique for non-proximable mixed norm minimization. Mixed norm regularization methods play a central role in signal reconstruction and processing, where their optimization relies on the fact that
Externí odkaz:
http://arxiv.org/abs/2008.04565
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2007, Iss 1, p 041658 (2007)
Externí odkaz:
https://doaj.org/article/be8fbc4bc6f1433994f6c24eb4377608
Publikováno v:
EURASIP Journal on Image and Video Processing, Vol 2007, Iss 1, p 042761 (2007)
This work investigates the use of the 3D dual-tree discrete wavelet transform (DDWT) for video coding. The 3D DDWT is an attractive video representation because it isolates image patterns with different spatial orientations and motion directions and
Externí odkaz:
https://doaj.org/article/5ba79cb588784ed39790030f86269c38
The L1 norm regularized least squares method is often used for finding sparse approximate solutions and is widely used in 1-D signal restoration. Basis pursuit denoising (BPD) performs noise reduction in this way. However, the shortcoming of using L1
Externí odkaz:
http://arxiv.org/abs/1905.09645
Autor:
Selesnick, Ivan
Publikováno v:
IEEE Transactions on Signal Processing, vol. 65, no. 17, pp. 4481-4494, 2017
Sparse approximate solutions to linear equations are classically obtained via L1 norm regularized least squares, but this method often underestimates the true solution. As an alternative to the L1 norm, this paper proposes a class of non-convex penal
Externí odkaz:
http://arxiv.org/abs/1803.06765
Autor:
Grossman, Scott N., Calix, Rachel, Hudson, Todd, Rizzo, John Ross, Selesnick, Ivan, Frucht, Steven, Galetta, Steven L., Balcer, Laura J., Rucker, Janet C.
Publikováno v:
In Journal of the Neurological Sciences 15 November 2022 442
Autor:
Selesnick, Ivan
Total variation denoising is a nonlinear filtering method well suited for the estimation of piecewise-constant signals observed in additive white Gaussian noise. The method is defined by the minimization of a particular non-differentiable convex cost
Externí odkaz:
http://arxiv.org/abs/1701.00439
Autor:
Parekh, Ankit, Selesnick, Ivan W.
Publikováno v:
Signal Processing, Apr. 2017
We address the problem of estimating a sparse low-rank matrix from its noisy observation. We propose an objective function consisting of a data-fidelity term and two parameterized non-convex penalty functions. Further, we show how to set the paramete
Externí odkaz:
http://arxiv.org/abs/1605.00042