Ricci-Notation Tensor Framework for Model-based Approaches to Imaging (2312.04018v3)
Abstract: Model-based approaches to imaging, like specialized image enhancements in astronomy, facilitate explanations of relationships between observed inputs and computed outputs. These models may be expressed with extended matrix-vector (EMV) algebra, especially when they involve only scalars, vectors, and matrices, and with n-mode or index notations, when they involve multidimensional arrays, also called numeric tensors or, simply, tensors. While this paper features an example, inspired by exoplanet imaging, that employs tensors to reveal (inverse) 2D fast Fourier transforms in an image enhancement model, the work is actually about the tensor algebra and software, or tensor frameworks, available for model-based imaging. The paper proposes a Ricci-notation tensor (RT) framework, comprising a dual-variant index notation, with Einstein summation convention, and codesigned object-oriented software, called the RTToolbox for MATLAB. Extensions to Ricci notation offer novel representations for entrywise, pagewise, and broadcasting operations popular in EMV frameworks for imaging. Complementing the EMV algebra computable with MATLAB, the RTToolbox demonstrates programmatic and computational efficiency via careful design of numeric tensor and dual-variant index classes. Compared to its closest competitor, also a numeric tensor framework that uses index notation, the RT framework enables superior ways to model imaging problems and, thereby, to develop solutions.
- M. Abadi, P. Barham, J. Chen, et al., “TensorFlow: A system for large-scale machine learning,” arXiv:1605.08695v2 [cs.DC], 1–18 (2016).
- N. Vasilache, O. Zinenko, T. Theodoridis, et al., “Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions,” arXiv:1802.04730v3 [cs.PL], 1–37 (2018).
- T. Mai, E. Lam, and C. Lee, “Deep Unrolled Low-Rank Tensor Completion for High Dynamic Range Imaging,” IEEE Transactions on Image Processing 31, 5774–5787 (2022).
- P. Wang, T. Cao, X. Li, et al., “Multi-focus image fusion based on gradient tensor HOSVD,” Journal of Electronic Imaging 32(2), 023028 1–18 (2023).
- C. Prévost, R. Borsoi, K. Usevich, et al., “Hyperspectral Super-resolution Accounting for Spectral Variability: Coupled Tensor LL1-Based Recovery and Blind Unmixing of the Unknown Super-resolution Image,” SIAM Journal on Imaging Sciences 15(1), 110–138 (2022).
- X. Liu, C. Hao, Z. Su, et al., “Image inpainting by low-rank tensor decomposition and multidirectional search,” Journal of Electronic Imaging 30(5), 053010 1–21 (2021).
- J. Bengua, H. Phien, H. Tuan, et al., “Efficient Tensor Completion for Color Image and Video Recovery: Low-Rank Tensor Train,” IEEE Transactions on Image Processing 26(5), 2466–2479 (2017).
- E. Newman and M. Kilmer, “Nonnegative Tensor Patch Dictionary Approaches for Image Compression and Deblurring Applications,” SIAM Journal on Imaging Sciences 13(3), 1084–1112 (2020).
- S. Lefkimmiatis and S. Osher, “Nonlocal Structure Tensor Functionals for Image Regularization,” IEEE Transactions on Computational Imaging 1(1), 16–29 (2015).
- M. Rezghi, “A Novel Fast Tensor-Based Preconditioner for Image Restoration,” IEEE Transactions on Image Processing 26(9), 4499–4508 (2017).
- M. Marquez, H. Rueda-Chacon, and H. Arguello, “Compressive Spectral Light Field Image Reconstruction via Online Tensor Representation,” IEEE Transactions on Image Processing 29, 3558–3568 (2020).
- W. Qiu, J. Zhou, and Q. Fu, “Tensor Representation for Three-Dimensional Radar Target Imaging With Sparsely Sampled Data,” IEEE Transactions on Computational Imaging 6, 263–275 (2020).
- Y. Li, J. Zhang, G. Sun, et al., “Hyperspectral image compressive reconstruction with low-rank tensor constraint,” Journal of Electronic Imaging 29(2), 023009 1–13 (2020).
- H. Skibbe and M. Reisert, “Spherical Tensor Algebra: A Toolkit for 3D Image Processing,” Journal of Mathematical Imaging and Vision 58(3), 349–381 (2017).
- W. Zhou, L. Shi, Z. Chen, et al., “Tensor Oriented No-Reference Light Field Image Quality Assessment,” IEEE Transactions on Image Processing 29, 4070–4084 (2020).
- E. Solomonik, D. Matthews, J. Hammond, et al., “A massively parallel tensor contraction framework for coupled-cluster computations,” Journal of Parallel and Distributed Computing 74(12), 3176–3190 (2014).
- N. Singh, Z. Zhang, X. Wu, et al., “Distributed-memory tensor completion for generalized loss functions in python using new sparse tensor kernels,” Journal of Parallel and Distributed Computing 169, 269–285 (2022).
- M. Valiev, E. Bylaska, N. Govind, et al., “NWChem: A comprehensive and scalable open-source solution for large scale molecular simulations,” Computer Physics Communications 181(9), 1477–1489 (2010).
- K. Kowalski, R. Bair, N. Bauman, et al., “From NWChem to NWChemEx: Evolving with the Computational Chemistry Landscape,” Chemical Reviews 121(8), 4962–4998 (2021).
- G. Baumgartner, A. Auer, D. Bernholdt, et al., “Synthesis of High-Performance Parallel Programs for a Class of Ab Initio Quantum Chemistry Models,” Proceedings of the IEEE 93(2), 276–292 (2005).
- E. Mutlu, A. Panyala, N. Gawande, et al., “TAMM: Tensor Algebra for Many-body Methods,” arXiv:2201.01257v3 [cs.DC], 1–13 (2023).
- J. Kossaifi, Y. Panagakis, A. Anandkumar, et al., “TensorLy: Tensor Learning in Python,” Journal of Machine Learning Research 20, 925–930 (2019).
- Y. Panagakis, J. Kossaifi, G. Chrysos, et al., “Tensor Methods in Computer Vision and Deep Learning,” Proceedings of the IEEE 109(5), 863–890 (2021).
- P. Cumpson, N. Sano, I. Fletcher, et al., “Multivariate analysis of extremely large ToFSIMS imaging datasets by a rapid PCA method,” Surface and Interface Analysis 47(10), 986–993 (2015).
- C. Bedia, A. Sierra, and R. Tauler, “Application of chemometric methods to the analysis of multimodal chemical images of biological tissues,” Analytical and Bioanalytical Chemistry 412, 5179–5190 (2020).
- A. Harrison and D. Joseph, “Numeric tensor framework: Exploiting and extending Einstein notation,” Journal of Computational Science 16, 128–139 (2016).
- B. Bader and T. Kolda, “Algorithm 862: MATLAB Tensor Classes for Fast Algorithm Prototyping,” ACM Transactions on Mathematical Software 32(4), 635–653 (2006).
- T. Kolda and B. Bader, “Tensor Decompositions and Applications,” SIAM Review 51(3), 455–500 (2009).
- L. Sorber, M. Van Barel, and L. De Lathauwer, “Structured Data Fusion,” IEEE Journal of Selected Topics in Signal Processing 9(4), 586–600 (2015).
- N. Vervliet, O. Debals, L. Sorber, et al., “Breaking the Curse of Dimensionality Using Decompositions of Incomplete Tensors: Tensor-based scientific computing in big data analysis,” IEEE Signal Processing Magazine 31(5), 71–79 (2014).
- I. Oseledets, “Tensor-Train Decomposition,” SIAM Journal on Scientific Computing 33(5), 2295–2317 (2011).
- D. Joseph, “Ricci-Notation Tensor Toolbox (RTToolbox).” MATLAB Central File Exchange (2023).
- The MathWorks, “Create and Share Toolboxes.” MATLAB R2022a Documentation (2022).
- The MathWorks, “Page-wise matrix multiplication (pagemtimes).” MATLAB R2020b Documentation (2020).
- D. Sirbu, N. Kasdin, and R. Vanderbei, “Monochromatic verification of high-contrast imaging with an occulter,” Optics Express 21(26), 32234–32253 (2013).
- D. Sirbu, S. Thomas, R. Belikov, et al., “Demonstration of broadband contrast at 1.2 λ𝜆\lambdaitalic_λ/D𝐷Ditalic_D and greater for the EXCEDE starlight suppression system,” Journal of Astronomical Telescopes, Instruments, and Systems 2(2), 025002 1–14 (2016).
- D. Sirbu, S. Thomas, R. Belikov, et al., “Techniques for High-contrast Imaging in Multi-star Systems. II. Multi-star Wavefront Control,” The Astrophysical Journal 849(2), 1–13 (2017).
- W. Schulz, “Digging the Dark Hole: Coronagraphy and the Quest to Find Other Worlds,” Photonics Focus 4(3), 22–27 (2023).
- Wikipedia contributors, “Airy disk — Wikipedia, The Free Encyclopedia.” https://en.wikipedia.org/w/index.php?title=Airy_disk (2022). [Online; accessed 21-July-2022].