no code implementations • 19 Nov 2023 • Ling Chen, Zhishen Huang, Yong Long, Saiprasad Ravishankar
In our experiments, we study combinations of supervised deep network reconstructors and MBIR solver with learned sparse representation-based priors or analytical priors.
no code implementations • 19 May 2022 • Ling Chen, Zhishen Huang, Yong Long, Saiprasad Ravishankar
Recent application of deep learning methods for image reconstruction provides a successful data-driven approach to addressing the challenges when reconstructing images with measurement undersampling or various types of noise.
no code implementations • 10 May 2022 • Il Yong Chun, Dongwon Park, Xuehang Zheng, Se Young Chun, Yong Long
Regression that predicts continuous quantity is a central part of applications using computational imaging and computer vision technologies.
no code implementations • 22 Mar 2022 • Xikai Yang, Zhishen Huang, Yong Long, Saiprasad Ravishankar
In this study, we propose a network-structured sparsifying transform learning approach for X-ray computed tomography (CT), which we refer to as multi-layer clustering-based residual sparsifying transform (MCST) learning.
no code implementations • 29 Sep 2021 • Il Yong Chun, Dongwon Park, Xuehang Zheng, Se Young Chun, Yong Long
Regression that predicts continuous quantity is a central part of applications using computational imaging and computer vision technologies.
no code implementations • 2 Dec 2020 • Zhipeng Li, Yong Long, Il Yong Chun
We propose a new INN architecture for DECT material decomposition.
no code implementations • 1 Nov 2020 • Xikai Yang, Yong Long, Saiprasad Ravishankar
Achieving high-quality reconstructions from low-dose computed tomography (LDCT) measurements is of much importance in clinical settings.
no code implementations • 10 Oct 2020 • Xikai Yang, Yong Long, Saiprasad Ravishankar
In this work, we develop a new image reconstruction approach based on a novel multi-layer model learned in an unsupervised manner by combining both sparse representations and deep models.
no code implementations • 6 Oct 2020 • Siqi Ye, Zhipeng Li, Michael T. McCann, Yong Long, Saiprasad Ravishankar
The proposed learning formulation combines both unsupervised learning-based priors (or even simple analytical priors) together with (supervised) deep network-based priors in a unified MBIR framework based on a fixed point iteration analysis.
no code implementations • 8 May 2020 • Xikai Yang, Xuehang Zheng, Yong Long, Saiprasad Ravishankar
Signal models based on sparse representation have received considerable attention in recent years.
no code implementations • 27 Feb 2020 • Siqi Ye, Yong Long, Il Yong Chun
We also investigated the spectral normalization technique that applies to image refining NN learning to satisfy the nonexpansive NN property; however, experimental results show that this does not improve the image reconstruction performance of Momentum-Net.
no code implementations • 26 Oct 2019 • Zhipeng Li, Siqi Ye, Yong Long, Saiprasad Ravishankar
Recent works have shown the promising reconstruction performance of methods such as PWLS-ULTRA that rely on clustering the underlying (reconstructed) image patches into a learned union of transforms.
no code implementations • 4 Aug 2019 • Il Yong Chun, Xuehang Zheng, Yong Long, Jeffrey A. Fessler
Numerical results with phantom data show that applying faster numerical solvers to model-based image reconstruction (MBIR) modules of BCD-Net leads to faster and more accurate BCD-Net; BCD-Net significantly improves the reconstruction accuracy, compared to the state-of-the-art MBIR method using learned transforms; BCD-Net achieves better image quality, compared to a state-of-the-art iterative NN architecture, ADMM-Net.
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 • 1 Jan 2019 • Zhipeng Li, Saiprasad Ravishankar, Yong Long, Jeffrey A. Fessler
Dual energy computed tomography (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability.
1 code implementation • 27 Aug 2018 • Siqi Ye, Saiprasad Ravishankar, Yong Long, Jeffrey A. Fessler
The SPULTRA algorithm has a similar computational cost per iteration as its recent counterpart PWLS-ULTRA that uses post-log measurements, and it provides better image reconstruction quality than PWLS-ULTRA, especially in low-dose scans.
Signal Processing Image and Video Processing Optimization and Control Medical Physics
no code implementations • 2 Nov 2017 • Xuehang Zheng, Il Yong Chun, Zhipeng Li, Yong Long, Jeffrey A. Fessler
Our results with the extended cardiac-torso (XCAT) phantom data and clinical chest data show that, for sparse-view 2D fan-beam CT and 3D axial cone-beam CT, PWLS-ST-$\ell_1$ improves the quality of reconstructed images compared to the CT reconstruction methods using edge-preserving regularizer and $\ell_2$ prior with learned ST.
no code implementations • 10 Jul 2017 • Xuehang Zheng, Zening Lu, Saiprasad Ravishankar, Yong Long, Jeffrey A. Fessler
A major challenge in computed tomography (CT) is to reduce X-ray dose to a low or even ultra-low level while maintaining the high quality of reconstructed images.
1 code implementation • 27 Mar 2017 • Xuehang Zheng, Saiprasad Ravishankar, Yong Long, Jeffrey A. Fessler
PWLS with regularization based on a union of learned transforms leads to better image reconstructions than using a single learned square transform.