Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Gupta, Sidharth"'
We propose a framework to jointly determine the deformation parameters and reconstruct the unknown volume in electron cryotomography (CryoET). CryoET aims to reconstruct three-dimensional biological samples from two-dimensional projections. A major c
Externí odkaz:
http://arxiv.org/abs/2211.14534
We propose a differentiable imaging framework to address uncertainty in measurement coordinates such as sensor locations and projection angles. We formulate the problem as measurement interpolation at unknown nodes supervised through the forward oper
Externí odkaz:
http://arxiv.org/abs/2211.10525
Autor:
Rezanejad, Morteza, Gupta, Sidharth, Gummaluru, Chandra, Marten, Ryan, Wilder, John, Gruninger, Michael, Walther, Dirk B.
Humans are excellent at perceiving illusory outlines. We are readily able to complete contours, shapes, scenes, and even unseen objects when provided with images that contain broken fragments of a connected appearance. In vision science, this ability
Externí odkaz:
http://arxiv.org/abs/2111.11322
Autor:
Gupta, Sidharth, Dokmanić, Ivan
We address the phase retrieval problem with errors in the sensing vectors. A number of recent methods for phase retrieval are based on least squares (LS) formulations which assume errors in the quadratic measurements. We extend this approach to handl
Externí odkaz:
http://arxiv.org/abs/2102.00927
Projected Gradient Descent (PGD) based adversarial training has become one of the most prominent methods for building robust deep neural network models. However, the computational complexity associated with this approach, due to the maximization of t
Externí odkaz:
http://arxiv.org/abs/2002.04237
We propose a numerical interferometry method for identification of optical multiply-scattering systems when only intensity can be measured. Our method simplifies the calibration of optical transmission matrices from a quadratic to a linear inverse pr
Externí odkaz:
http://arxiv.org/abs/1911.01006
In this paper we tackle the problem of recovering the phase of complex linear measurements when only magnitude information is available and we control the input. We are motivated by the recent development of dedicated optics-based hardware for rapid
Externí odkaz:
http://arxiv.org/abs/1907.01703
Publikováno v:
IEEE Transactions on Signal Processing, Vol. 68, 4782-4796, 2020
We tackle the problem of recovering a complex signal $\boldsymbol x\in\mathbb{C}^n$ from quadratic measurements of the form $y_i=\boldsymbol x^*\boldsymbol A_i\boldsymbol x$, where $\boldsymbol A_i$ is a full-rank, complex random measurement matrix w
Externí odkaz:
http://arxiv.org/abs/1902.05612
We propose a new learning-based approach to solve ill-posed inverse problems in imaging. We address the case where ground truth training samples are rare and the problem is severely ill-posed - both because of the underlying physics and because we ca
Externí odkaz:
http://arxiv.org/abs/1805.11718
Autor:
McCann, Kelly Elizabeth, Kaklamani, Virginia G., Osman, Noran, Cannon, Joan, Brent, Lonnie, Lucia, Rachel, Li, Chong, Duran, Nicole, Gupta, Sidharth, Martin, Nancy Elizabeth
Publikováno v:
Journal of Clinical Oncology; 2024 Supplement 10, Vol. 20, p391-391, 13p
