The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation
Open accessAbstract
Many material and biological samples in scientific imaging are characterized by nonlocal repeating structures. These are studied using scanning electron microscopy and electron tomography. Sparse sampling of individual pixels in a two-dimensional image acquisition geometry, or sparse sampling of projection images with large tilt increments in a tomography experiment, can enable high speed data acquisition and minimize sample damage caused by the electron beam. In this paper, we present an algorithm for electron tomographic reconstruction and sparse image interpolation that exploits the nonlocal redundancy in images. We adapt a framework, termed plug-and-play priors, to solve these imaging problems in a regularized inversion setting. The power of the plug-and-play approach is that it allows a wide array of modern denoising algorithms to be used as a “prior model” for tomography and image interpolation. We also present sufficient mathematical conditions that ensure convergence of the plug-and-play approach, and we use these insights to design a new nonlocal means denoising algorithm. Finally, we demonstrate that the algorithm produces higher quality reconstructions on both simulated and real electron microscope data, along with improved convergence properties compared to other methods.
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Cites
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A Non-Local Algorithm for Image Denoising
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A Review of Image Denoising Algorithms, with a New One
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A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration
Bioucas-Dias, J. M., & Figueiredo, M. A. T. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.909319 · Cited by 2,064 · cited 1×
“The solution to equation (1) may be computed using a variety of optimization methods, such as the alternating direction method of multipliers (ADMM), the fast iterative shrinkage thresholding algorithm (FISTA) [52], the two-step iterative shrinkage thresholding (TwIST) [53], or sparse reconstruction by separable appro…”
Method
Cited by
Learning Deep CNN Denoiser Prior for Image Restoration
Zhang, K., Zuo, W., Gu, S., & Zhang, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.300 · Cited by 2,116
Plug-and-Play ADMM for Image Restoration: Fixed-Point Convergence and Applications
Chan, S. H., Wang, X., & Elgendy, O. A. (2017) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2016.2629286 · Cited by 904
A Plug-and-Play Priors Approach for Solving Nonlinear Imaging Inverse Problems
Kamilov, U. S., Mansour, H., & Wohlberg, B. (2017) · IEEE Signal Processing Letters · doi:10.1109/lsp.2017.2763583 · Cited by 264
Self-citationLearning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems
Meinhardt, T., Moeller, M., Hazirbas, C., & Cremers, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.198 · Cited by 257
DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction
Liu, J., Anirudh, R., Thiagarajan, J. J., He, S., Mohan, K. A., Kamilov, U. S., & Kim, H. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00963 · Cited by 106
Metadata
- Authors
- Sreehari, S., Venkatakrishnan, S. V., Wohlberg, B., Buzzard, G. T., Drummy, L. F., Simmons, J. P., & Bouman, C. A.
- Venue
- IEEE Transactions on Computational Imaging
- Published
- 2016-12
- Volume
- 2
- Issue
- 4
- Pages
- 408-423
- ISSN
- 2333-9403 (ISSN-L)
- 2334-0118
- 2573-0436
- OpenAlex
- W2296055064
- Cited by
- 300
- References
- 50
- FWCI
- 14.38
- Type
- journal article
- Language
- en
- Open access
- green
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- Sreehari, S., Venkatakrishnan, S. V., Wohlberg, B., Buzzard, G. T., Drummy, L. F., Simmons, J. P., & Bouman, C. A. (2016). Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation. IEEE Transactions on Computational Imaging. https://doi.org/10.1109/tci.2016.2599778
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