1 code implementation • 11 Oct 2023 • Weijie Gan, Qiuchen Zhai, Michael Thompson McCann, Cristina Garcia Cardona, Ulugbek S. Kamilov, Brendt Wohlberg
Ptychography is an imaging technique that captures multiple overlapping snapshots of a sample, illuminated coherently by a moving localized probe.
no code implementations • 30 Aug 2023 • Luke Lozenski, Hanchen Wang, Fu Li, Mark A. Anastasio, Brendt Wohlberg, Youzuo Lin, Umberto Villa
Once trained, the CNN can perform real-time FWI image reconstruction from USCT waveform data.
1 code implementation • 9 Mar 2023 • Zihao Zou, Jiaming Liu, Brendt Wohlberg, Ulugbek S. Kamilov
ELDER is based on a regularization functional parameterized by a CNN and a deep equilibrium learning (DEQ) method for training the functional to be MSE-optimal at the fixed points of the reconstruction algorithm.
no code implementations • 24 Feb 2023 • Thilo Balke, Alexander M. Long, Sven C. Vogel, Brendt Wohlberg, Charles A. Bouman
We present the TRINIDI algorithm which is based on a two-step process in which we first estimate the neutron flux and background counts, and then reconstruct the areal densities of each isotope and pixel.
no code implementations • 21 Nov 2022 • Paul Goyes, Edwin Vargas, Claudia Correa, Yu Sun, Ulugbek Kamilov, Brendt Wohlberg, Henry Arguello
Physical and budget constraints often result in irregular sampling, which complicates accurate subsurface imaging.
no code implementations • 31 Mar 2022 • Ulugbek S. Kamilov, Charles A. Bouman, Gregery T. Buzzard, Brendt Wohlberg
Plug-and-Play Priors (PnP) is one of the most widely-used frameworks for solving computational imaging problems through the integration of physical models and learned models.
no code implementations • 28 Nov 2021 • Qiuchen Zhai, Brendt Wohlberg, Gregery T. Buzzard, Charles A. Bouman
Ptychography is a computational imaging technique using multiple, overlapping, coherently illuminated snapshots to achieve nanometer resolution by solving a nonlinear phase-field recovery problem.
no code implementations • 6 Oct 2021 • Thilo Balke, Alexander M. Long, Sven C. Vogel, Brendt Wohlberg, Charles A. Bouman
Energy resolved neutron imaging (ERNI) is an advanced neutron radiography technique capable of non-destructively extracting spatial isotopic information within a given material.
1 code implementation • NeurIPS 2021 • Jiaming Liu, M. Salman Asif, Brendt Wohlberg, Ulugbek S. Kamilov
The plug-and-play priors (PnP) and regularization by denoising (RED) methods have become widely used for solving inverse problems by leveraging pre-trained deep denoisers as image priors.
no code implementations • 25 May 2021 • Shihang Feng, Xitong Zhang, Brendt Wohlberg, Neill Symons, Youzuo Lin
Via both numerical and expert evaluation, we conclude that our models can produce high-quality 2D/3D seismic imaging data at a reasonable cost, offering the possibility of real-time monitoring or even near-future forecasting of the CO$_2$ storage reservoir.
no code implementations • 19 Apr 2021 • Aydin Buluc, Tamara G. Kolda, Stefan M. Wild, Mihai Anitescu, Anthony DeGennaro, John Jakeman, Chandrika Kamath, Ramakrishnan Kannan, Miles E. Lopes, Per-Gunnar Martinsson, Kary Myers, Jelani Nelson, Juan M. Restrepo, C. Seshadhri, Draguna Vrabie, Brendt Wohlberg, Stephen J. Wright, Chao Yang, Peter Zwart
Randomized algorithms have propelled advances in artificial intelligence and represent a foundational research area in advancing AI for Science.
no code implementations • 25 Mar 2021 • Qili Zeng, Shihang Feng, Brendt Wohlberg, Youzuo Lin
Seismic full-waveform inversion (FWI) techniques aim to find a high-resolution subsurface geophysical model provided with waveform data.
1 code implementation • 9 Feb 2021 • Yu Sun, Jiaming Liu, Mingyang Xie, Brendt Wohlberg, Ulugbek S. Kamilov
We propose Coordinate-based Internal Learning (CoIL) as a new deep-learning (DL) methodology for the continuous representation of measurements.
1 code implementation • 22 Jan 2021 • Jiaming Liu, Yu Sun, Weijie Gan, Xiaojian Xu, Brendt Wohlberg, Ulugbek S. Kamilov
Deep unfolding networks have recently gained popularity in the context of solving imaging inverse problems.
no code implementations • 26 Nov 2020 • Mingyang Xie, Yu Sun, Jiaming Liu, Brendt Wohlberg, Ulugbek S. Kamilov
Cal-RED extends the traditional RED methodology to imaging problems that require the calibration of the measurement operator.
no code implementations • ICLR 2021 • Yu Sun, Jiaming Liu, Yiran Sun, Brendt Wohlberg, Ulugbek S. Kamilov
Regularization by denoising (RED) is a recently developed framework for solving inverse problems by integrating advanced denoisers as image priors.
no code implementations • 3 Sep 2020 • Renán Rojas-Gómez, Jihyun Yang, Youzuo Lin, James Theiler, Brendt Wohlberg
Seismic full-waveform inversion (FWI) is a nonlinear computational imaging technique that can provide detailed estimates of subsurface geophysical properties.
no code implementations • 5 Jun 2020 • Yu Sun, Zihui Wu, Xiaojian Xu, Brendt Wohlberg, Ulugbek S. Kamilov
Plug-and-play priors (PnP) is a broadly applicable methodology for solving inverse problems by exploiting statistical priors specified as denoisers.
no code implementations • 15 May 2020 • Xiaojian Xu, Yu Sun, Jiaming Liu, Brendt Wohlberg, Ulugbek S. Kamilov
Plug-and-play priors (PnP) is a methodology for regularized image reconstruction that specifies the prior through an image denoiser.
no code implementations • 22 Apr 2020 • Manish Bhattarai, Diane Oyen, Juan Castorena, Liping Yang, Brendt Wohlberg
We then use our small set of manually labeled patent diagram images via transfer learning to adapt the image search from sketches of natural images to diagrams.
no code implementations • 27 Feb 2020 • Ming Gong, Liping Yang, Catherine Potts, Vijayan K. Asari, Diane Oyen, Brendt Wohlberg
Line segment detection is an essential task in computer vision and image analysis, as it is the critical foundation for advanced tasks such as shape modeling and road lane line detection for autonomous driving.
no code implementations • 1 Jun 2019 • Xuehang Zheng, Saiprasad Ravishankar, Yong Long, Marc Louis Klasky, Brendt Wohlberg
Signal models based on sparsity, low-rank and other properties have been exploited for image reconstruction from limited and corrupted data in medical imaging and other computational imaging applications.
no code implementations • 8 Nov 2018 • Yu Sun, Brendt Wohlberg, Ulugbek S. Kamilov
Plug-and-play priors (PnP) is a popular framework for regularized signal reconstruction by using advanced denoisers within an iterative algorithm.
no code implementations • 31 Oct 2018 • Yu Sun, Shiqi Xu, Yunzhe Li, Lei Tian, Brendt Wohlberg, Ulugbek S. Kamilov
The plug-and-play priors (PnP) framework has been recently shown to achieve state-of-the-art results in regularized image reconstruction by leveraging a sophisticated denoiser within an iterative algorithm.
no code implementations • 19 Oct 2018 • Saiprasad Ravishankar, Brendt Wohlberg
Learned data models based on sparsity are widely used in signal processing and imaging applications.
1 code implementation • 12 Sep 2018 • Yu Sun, Brendt Wohlberg, Ulugbek S. Kamilov
The results in this paper have the potential to expand the applicability of the PnP framework to very large and redundant datasets.
no code implementations • 9 Sep 2017 • Cristina Garcia-Cardona, Brendt Wohlberg
Convolutional sparse representations are a form of sparse representation with a dictionary that has a structure that is equivalent to convolution with a set of linear filters.
no code implementations • 31 Aug 2017 • Jialin Liu, Cristina Garcia-Cardona, Brendt Wohlberg, Wotao Yin
Convolutional sparse representations are a form of sparse representation with a structured, translation invariant dictionary.
no code implementations • 29 Aug 2017 • Brendt Wohlberg
The most widely used form of convolutional sparse coding uses an $\ell_1$ regularization term.
no code implementations • 20 Jul 2017 • Brendt Wohlberg, Paul Rodriguez
Two different approaches have recently been proposed for boundary handling in convolutional sparse representations, avoiding potential boundary artifacts arising from the circular boundary conditions implied by the use of frequency domain solution methods by introducing a spatial mask into the convolutional sparse coding problem.
no code implementations • 29 Jun 2017 • Jialin Liu, Cristina Garcia-Cardona, Brendt Wohlberg, Wotao Yin
While a number of different algorithms have recently been proposed for convolutional dictionary learning, this remains an expensive problem.
no code implementations • 12 May 2017 • Brendt Wohlberg
While convolutional sparse representations enjoy a number of useful properties, they have received limited attention for image reconstruction problems.
no code implementations • 20 Apr 2017 • Brendt Wohlberg
Appropriate selection of the penalty parameter is crucial to obtaining good performance from the Alternating Direction Method of Multipliers (ADMM).
no code implementations • 23 Dec 2015 • Suhas Sreehari, S. V. Venkatakrishnan, Brendt Wohlberg, Lawrence F. Drummy, Jeffrey P. Simmons, Charles A. Bouman
The power of the P&P approach is that it allows a wide array of modern denoising algorithms to be used as a "prior model" for tomography and image interpolation.