Linear inverse problems in imaging

Inverse Problem Theory and Methods for Model Parameter Estimation

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Regularization of Inverse Problems

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Generalized autocalibrating partially parallel acquisitions (GRAPPA)

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Extrapolation, Interpolation, and Smoothing of Stationary Time Series

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A generalized inverse for matrices

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The approximation of one matrix by another of lower rank

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Digital image processing

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Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter

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Rank-Deficient and Discrete Ill-Posed Problems

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Digital Image Processing

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The NMR phased array

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Simultaneous acquisition of spatial harmonics (SMASH): Fast imaging with radiofrequency coil arrays

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An Introduction to Multivariate Statistical Analysis

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Advances in sensitivity encoding with arbitrary k ‐space trajectories

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Introduction to Linear Regression Analysis

Gray, J. B. (2002) · Technometrics · doi:10.1198/tech.2002.s714 · Cited by 946 · see citing works

Practical Approximate Solutions to Linear Operator Equations When the Data are Noisy

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Regression and the Moore-Penrose Pseudoinverse

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High-Speed Spiral-Scan Echo Planar NMR Imaging-I

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A Standard Default Color Space for the Internet - sRGB

Stokes, M. (2005) · Cited by 275 · see citing works

Parallel imaging reconstruction using automatic regularization

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MRI measurement of the temporal evolution of relative CMRO2 during rat forepaw stimulation

Mandeville, J. B., Marota, J. J. A., Ayata, C., Moskowitz, M. A., Weisskoff, R. M., & Rosen, B. R. (1999) · Magnetic Resonance in Medicine · doi:10.1002/(sici)1522-2594(199911)42:5 · Cited by 220 · see citing works

Self‐calibrating parallel imaging with automatic coil sensitivity extraction

McKenzie, C. A., Yeh, E. N., Ohliger, M. A., Price, M. D., & Sodickson, D. K. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10087 · Cited by 188 · see citing works

Fast imaging using subencoding data sets from multiple detectors

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Imaging time reduction through multiple receiver coil data acquisition and image reconstruction

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Spectral estimation techniques for the spectral calibration of a color image scanner

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Spectrophotometric Image Analysis of Fine Art Paintings

Maı̂tre, H., Schmitt, F., Crettez, J., Wu, Y., & Hardeberg, J. Y. (1996) · Color and Imaging Conference · doi:10.2352/cic.1996.4.1.art00014 · Cited by 67 · see citing works

A tour of accelerated parallel MR imaging from a linear systems perspective

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Multispectral Color System with an Encoding Format Compatible with the Conventional Tristimulus Model

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Scanner linearity

Farrell, J., & Wandell, B. A. (1993) · Journal of Electronic Imaging · doi:10.1117/12.148219 · Cited by 11 · see citing works

Multispectral Imaging and Its Applications in Textile Industry and Related Fields.

Herzog, P. G., & Hill, B. (2003) · PICS · Cited by 6 · see citing works

Optimal Filters for the Reconstruction and Discrimination of Reflectance Curves

Mahy, M., Wambacq, P., Eycken, L. V., & Oosterlinck, A. (1994) · Color and Imaging Conference · doi:10.2352/cic.1994.2.1.art00039 · Cited by 6 · see citing works

Robust Super-Resolution Volume Reconstruction From Slice Acquisitions: Application to Fetal Brain MRI

Gholipour, A., Estroff, J. A., & Warfield, S. K. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2051680 · Cited by 351 · see citing works

A Plug-and-Play Priors Approach for Solving Nonlinear Imaging Inverse Problems

Kamilov, U. S., Mansour, H., & Wohlberg, B. E. (2017) · IEEE Signal Processing Letters · doi:10.1109/lsp.2017.2763583 · Cited by 259 · see citing works

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

Fascista, A. (2022) · Sensors · doi:10.3390/s22051824 · Cited by 259 · see citing works

Realistic Analytical Phantoms for Parallel Magnetic Resonance Imaging

Guerquin-Kern, M., Lejeune, L., Pruessmann, K. P., & Unser, M. A. (2011) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2174158 · Cited by 212 · see citing works

Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement

Tsagkatakis, G., Aidini, A., Fotiadou, K., Giannopoulos, M., Pentari, A., & Tsakalides, P. (2019) · Sensors · doi:10.3390/s19183929 · Cited by 144 · see citing works

Vector approximate message passing for the generalized linear model

Schniter, P., Rangan, S., & Fletcher, A. K. (2016) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/acssc.2016.7869633 · Cited by 142 · see citing works

Efficient and accurate inversion of multiple scattering with deep learning

Sun, Y., Xia, Z., & Kamilov, U. S. (2018) · Optics Express · doi:10.1364/oe.26.014678 · Cited by 135 · see citing works

A wavelet-based regularized reconstruction algorithm for SENSE parallel MRI with applications to neuroimaging☆

Chaâri, L., Pesquet, J.-C., Benazza‐Benyahia, A., & Ciuciu, P. (2010) · Medical Image Analysis · doi:10.1016/j.media.2010.08.001 · Cited by 108 · see citing works

Three-dimensional microscopy and sectional image reconstruction using optical scanning holography

Lam, E. Y. M., Zhang, X., Vo, H. N., Poon, T., & Indebetouw, G. (2009) · Applied Optics · doi:10.1364/ao.48.00h113 · Cited by 85 · see citing works

On Measuring and Controlling the Spectral Bias of the Deep Image Prior

Shi, Z., Mettes, P., Maji, S., & Snoek, C. G. M. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-021-01572-7 · Cited by 76 · see citing works

Multimodal Deep Unfolding for Guided Image Super-Resolution

Marivani, I., Tsiligianni, E., Cornelis, B., & Deligiannis, N. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3014729 · Cited by 62 · see citing works

Plug-and-Play Synthetic Aperture Radar Image Formation Using Deep Priors

Alver, M. B., Saleem, A., & Çetin, M. (2020) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2020.3047473 · Cited by 61 · see citing works

A Parallel Proximal Algorithm for Anisotropic Total Variation Minimization

Kamilov, U. S. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2629449 · Cited by 61 · see citing works

A Comprehensive Review of Blind Deconvolution Techniques for Image Deblurring

Satish, P., Srikantaswamy, M., & Ramaswamy, N. (2020) · Traitement du signal · doi:10.18280/ts.370321 · Cited by 59 · see citing works

SIMBA: Scalable Inversion in Optical Tomography Using Deep Denoising Priors

Wu, Z., Sun, Y., Matlock, A. C., Liu, J., Tian, L., & Kamilov, U. S. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.2999820 · Cited by 52 · see citing works

A New Sparse Representation Framework for Reconstruction of an Isotropic High Spatial Resolution MR Volume From Orthogonal Anisotropic Resolution Scans

Jia, Y., Gholipour, A., He, Z., & Warfield, S. K. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2656907 · Cited by 49 · see citing works

Fixed-Point and Objective Convergence of Plug-and-Play Algorithms

Nair, P., Gavaskar, R. G., & Chaudhury, K. N. (2021) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2021.3066053 · Cited by 49 · see citing works

Deep learning based remote sensing technique for environmental parameter retrieval and data fusion from physical models

Vemuri, R. K., Reddy, P. C. S., Kumar, B. S. P., Jayavadivel, R., Sharma, S. K., & Ponnusamy, S. (2021) · Arabian Journal of Geosciences · doi:10.1007/s12517-021-07577-3 · Cited by 47 · see citing works

Spectra estimation from raw camera responses based on adaptive local-weighted linear regression

Liang, J., Xiao, K., Pointer, M. R., Wan, X., & Li, C. (2019) · Optics Express · doi:10.1364/oe.27.005165 · Cited by 47 · see citing works

Using weighted pseudo‐inverse method for reconstruction of reflectance spectra and analyzing the dataset in terms of normality

Babaei, V., Amirshahi, S. H., & Agahian, F. (2010) · Color Research & Application · doi:10.1002/col.20613 · Cited by 44 · see citing works

Sparse Stochastic Processes and Discretization of Linear Inverse Problems

Bostan, E., Kamilov, U. S., Nilchian, M., & Unser, M. A. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2255305 · Cited by 38 · see citing works

On Plug-and-Play Regularization Using Linear Denoisers

Gavaskar, R. G., Athalye, C. D., & Chaudhury, K. N. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3075092 · Cited by 37 · see citing works

An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial NetworksPreprint

Anirudh, R., Thiagarajan, J. J., Kailkhura, B., & Bremer, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.07281 · Cited by 37 · see citing works

A Neural-Network-Based Convex Regularizer for Inverse Problems

Goujon, A., Neumayer, S., Bohra, P., Ducotterd, S., & Unser, M. A. (2023) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2023.3306100 · Cited by 36 · see citing works

From Fienup’s phase retrieval techniques to regularized inversion for in-line holography: tutorial

Momey, F., Denis, L., Olivier, T., & Fournier, C. (2019) · Journal of the Optical Society of America A · doi:10.1364/josaa.36.000d62 · Cited by 36 · see citing works

Studying That Smile

Ribés, A., Pillay, R., Schmitt, F., & Lahanier, C. (2008) · IEEE Signal Processing Magazine · doi:10.1109/msp.2008.923091 · Cited by 35 · see citing works

Learning Weakly Convex Regularizers for Convergent Image-Reconstruction Algorithms

Goujon, A., Neumayer, S., & Unser, M. A. (2024) · SIAM Journal on Imaging Sciences · doi:10.1137/23m1565243 · Cited by 34 · see citing works

Physics-Driven Inverse Problems Made Tractable With Cosparse Regularization

Kitić, S., Albera, L., Nancy Bertin, B., & Gribonval, R. (2015) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2015.2480045 · Cited by 34 · see citing works

Snapshot multidimensional photography through active optical mapping

Park, J., Feng, X., Liang, R., & Gao, L. S. (2020) · Nature Communications · doi:10.1038/s41467-020-19418-0 · Cited by 34 · see citing works

Improving temporal resolution of pulmonary perfusion imaging in rats using the partially separable functions model

Brinegar, C., Schmitter, S. S., Mistry, N. N., Johnson, G. A., & Liang, Z. (2010) · Magnetic Resonance in Medicine · doi:10.1002/mrm.22500 · Cited by 34 · see citing works

A 3-D Sparse SAR Imaging Method Based on Plug-and-Play

Wang, Y., He, Z., Zhan, X., Zeng, Q., & Hu, Y. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3221934 · Cited by 32 · see citing works

Topographic localization of brain activation in diffuse optical imaging using spherical wavelets

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Relevance Vector Machine Image Reconstruction Algorithm for Electrical Capacitance Tomography With Explicit Uncertainty Estimates

Acero, D. O., Marashdeh, Q. M., & Teixeira, F. L. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.2966990 · Cited by 31 · see citing works

Spectral imaging using consumer-level devices and kernel-based regression

Heikkinen, V., Cámara, C., Hirvonen, T., & Penttinen, N. (2016) · Journal of the Optical Society of America A · doi:10.1364/josaa.33.001095 · Cited by 30 · see citing works

On the application of singular value decomposition and Tikhonov regularization to ill-posed problems in hyperbolic passive location

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Spectral Reflectance Estimation Using Gaussian Processes and Combination Kernels

Heikkinen, V. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2820839 · Cited by 29 · see citing works

Real-time cardiac MRI using prior spatial-spectral information

Brinegar, C., Zhang, H., Wu, Y., Foley, L. M., Hitchens, T. K., Ye, Q., Pocci, D., Lam, F., Ho, C., & Liang, Z. (2009) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2009.5333482 · Cited by 28 · see citing works

Deep spectral reflectance and illuminant estimation from self-interreflections

Deeb, R., van de Weijer, J., Muselet, D., Hébert, M., & Trémeau, A. (2018) · Journal of the Optical Society of America A · doi:10.1364/josaa.36.000105 · Cited by 25 · see citing works

A Review on Image Reconstruction through MRI k-Space Data

Jhamb, T. K., Rejathalal, V., & Govindan, V. K. (2015) · International Journal of Image Graphics and Signal Processing · doi:10.5815/ijigsp.2015.07.06 · Cited by 24 · see citing works

Research on filter selection method for broadband spectral imaging system based on ancient murals

Liang, J., Wan, X., Liu, Q., Li, C., & Li, J. (2015) · Color Research & Application · doi:10.1002/col.22004 · Cited by 21 · see citing works

Time‐difference‐of‐arrival regularised location estimator for multilateration systems

Mantilla-Gaviria, I. A., Leonardi, M., Galati, G., & Tejedor, J. V. B. (2013) · IET Radar Sonar & Navigation · doi:10.1049/iet-rsn.2013.0151 · Cited by 20 · see citing works

Comparative studies of l_p-regularization-based reconstruction algorithms for bioluminescence tomography

Zhang, Q., Chen, X., Qu, X., Liang, J., & Tian, J. (2012) · Biomedical Optics Express · doi:10.1364/boe.3.002916 · Cited by 20 · see citing works

The Fukushima inverse problem

Martinez-Camara, M., Dokmanić, I., Ranieri, J., Scheibler, R., Vetterli, M., & Stohl, A. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6638477 · Cited by 18 · see citing works

Robust Si/Ge heterostructure metasurfaces as building blocks for wavelength-selective photodetectors

Schlipf, J., Berkmann, F., Yamamoto, Y., Reichenbach, M., Veleski, M., Kawaguchi, Y., Mörz, F., Tomm, J. W., Weißhaupt, D., & Fischer, I. A. (2023) · Applied Physics Letters · doi:10.1063/5.0134458 · Cited by 17 · see citing works

Interpretable Deep Learning for Multimodal Super-Resolution of Medical Images

Tsiligianni, E., Zerva, M. C., Marivani, I., Deligiannis, N., & Kondi, L. P. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87231-1_41 · Cited by 17 · see citing works

Imageless Shape Detection Using a Millimeter-Wave Dynamic Antenna Array and Noise Illumination

Chen, D., Vakalis, S., & Nanzer, J. A. (2021) · IEEE Transactions on Microwave Theory and Techniques · doi:10.1109/tmtt.2021.3132311 · Cited by 17 · see citing works

Reflectance spectra reconstruction from trichromatic camera based on kernel partial least square method

Xiao, G., Wan, X., Wang, L., & Liu, S. (2019) · Optics Express · doi:10.1364/oe.27.034921 · Cited by 16 · see citing works

Tomographic single pixel spatial frequency projection imaging

Stockton, P. A., Murray, G., Field, J. J., Squier, J. A., Pezeshki, A., & Bartels, R. A. (2022) · Optics Communications · doi:10.1016/j.optcom.2022.128401 · Cited by 16 · see citing works

Averaged Deep Denoisers for Image Regularization

Nair, P., & Chaudhury, K. N. (2024) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-024-01181-2 · Cited by 16 · see citing works

A tutorial on the Bayesian statistical approach to inverse problems

Waqar, F., Patel, S. K., & Simon, C. M. (2023) · APL Machine Learning · doi:10.1063/5.0154773 · Cited by 14 · see citing works

A Novel Plug-and-Play SAR Reconstruction Framework Using Deep Priors

Alver, M. B., Saleem, A., & Çetin, M. (2019) · IEEE Radar Conference (RadarConf) · doi:10.1109/radar.2019.8835598 · Cited by 14 · see citing works

A survey of deep learning-based microscopic cell image understanding

Huo, Y., Lu, Z., Deng, Z., Zhang, F., Xiong, J., Zhang, P., & Huang, H. (2025) · Displays · doi:10.1016/j.displa.2025.102968 · Cited by 12 · see citing works

Plug-and-Play Regularization Using Linear Solvers

Nair, P., & Chaudhury, K. N. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3211473 · Cited by 11 · see citing works

20 µm resolution multipixel ghost imaging with high-energy x-rays

Sefi, O., Yehuda, A. B., Klein, Y., Sobol, Z., Bloch, S., Schwartz, H., Cohen, E., & Shwartz, S. (2024) · Optics Express · doi:10.1364/oe.524414 · Cited by 11 · see citing works

Unsupervised Vocal Dereverberation with Diffusion-Based Generative Models

Saito, K., Murata, N., Uesaka, T., Lai, C.-H., Takida, Y., Fukui, T., & Mitsufuji, Y. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095761 · Cited by 11 · see citing works

Spectral reflectance estimation from one RGB image using self-interreflections in a concave object

Deeb, R., Muselet, D., Hébert, M., & Trémeau, A. (2018) · Applied Optics · doi:10.1364/ao.57.004918 · Cited by 11 · see citing works

An improved nonlocal sparse regularization-based image deblurring via novel similarity criteria

Wang, N., Shi, W., Fan, C., & Zou, L. (2018) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881418783119 · Cited by 11 · see citing works

Modeling and Simulation of Polarimetric Hyperspectral Imaging Process

Zhang, J., Chen, J., Zou, B., & Zhang, Y. (2011) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2011.2172618 · Cited by 10 · see citing works

Research on the deep learning-based exposure invariant spectral reconstruction method

Liang, J., Xin, L., Zuo, Z., Zhou, J., Liu, A., Luo, H., & Hu, X. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.1031546 · Cited by 9 · see citing works

On the Contractivity of Plug-and-Play Operators

Athalye, C. D., Chaudhury, K. N., & Kumar, B. (2023) · IEEE Signal Processing Letters · doi:10.1109/lsp.2023.3323248 · Cited by 8 · see citing works

Advanced interacting sequential Monte Carlo sampling for inverse scattering

Giraud, F., Minvielle, P., & Moral, P. D. (2013) · Inverse Problems · doi:10.1088/0266-5611/29/9/095014 · Cited by 8 · see citing works

A new robust and efficient estimator for ill-conditioned linear inverse problems with outliers

Martinez-Camara, M., Muma, M., Zoubir, A. M., & Vetterli, M. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178606 · Cited by 8 · see citing works

Heavy-ball enhanced pseudo-inverse-based hard thresholding algorithms for sparse linear inverse problems

Wen, J., He, J., He, Z., & Liu, X. (2025) · Inverse Problems · doi:10.1088/1361-6420/ade180 · Cited by 7 · see citing works

Parameter Estimation in Sparse Inverse Problems Using Bernoulli-Gaussian Prior

Barbault, P., Kowalski, M., & Soussen, C. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746697 · Cited by 7 · see citing works

Tensor-Free Proximal Methods for Lifted Bilinear/Quadratic Inverse Problems with Applications to Phase Retrieval

Beinert, R., & Bredies, K. (2021) · Foundations of Computational Mathematics · doi:10.1007/s10208-020-09479-4 · Cited by 7 · see citing works

Analytical form of Shepp-Logan phantom for parallel MRI

Guerquin-Kern, M., Karahanoğlu, F. I., Ville, D. V. D., Pruessmann, K. P., & Unser, M. A. (2010) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2010.5490365 · Cited by 7 · see citing works

Sparse inference using approximate message passing

Andersen, M. R. (2014) · Cited by 7 · see citing works

Simple method for volumetric thickness measurement using a color camera

Choi, G., Lee, Y., Lee, S.-W., Cho, Y., & Pahk, H.-J. (2018) · Applied Optics · doi:10.1364/ao.57.007550 · Cited by 7 · see citing works

Toward non-metameric reflectance recovery by emulating the spectral neighborhood using corresponding color information

Safdar, M., & Emmel, P. (2022) · Journal of the Optical Society of America A · doi:10.1364/josaa.451931 · Cited by 6 · see citing works

Spectral reflectance estimation based on two-step k-nearest neighbors locally weighted linear regression

Wei, L., Xu, W., Weng, Z., Sun, Y., & Lin, Y. (2022) · Optical Engineering · doi:10.1117/1.oe.61.6.063102 · Cited by 6 · see citing works

A Plug-and-Play Deep Denoiser Prior Model for Accelerated MRI Reconstruction

Karaoğlu, H. H., & Ekşioğlu, E. M. (2022) · International Conference on Telecommunications and Signal Processing (TSP) · doi:10.1109/tsp55681.2022.9851376 · Cited by 6 · see citing works

Inexact Half-Quadratic Optimization for Linear Inverse Problems

Robini, M. C., Yang, F., & Zhu, Y. (2018) · SIAM Journal on Imaging Sciences · doi:10.1137/17m114635x · Cited by 6 · see citing works

Characterization of Metal Artifacts in X‐Ray Computed Tomography

Park, H. S., Choi, J. K., & Seo, J. K. (2017) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.21680 · Cited by 5 · see citing works

Remote Real Time Monitoring for Underground Contamination in Mongolia Using Electrical Impedance Tomography

Munkh-Erdene, T., Lee, E., Zhou, L., Lee, K., & Seo, J. K. (2015) · Journal of Nondestructive Evaluation · doi:10.1007/s10921-015-0325-5 · Cited by 5 · see citing works

Stability of data-dependent ridge-regularization for inverse problems

Neumayer, S., & Altekrüger, F. (2025) · Inverse Problems · doi:10.1088/1361-6420/adda74 · Cited by 4 · see citing works

Sparsity Driven Latent Space Sampling for Generative Prior Based Compressive Sensing

Killedar, V., Pokala, P. K., & Seelamantula, C. S. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413451 · Cited by 4 · see citing works

Infrared Image Stripe Noise Removal by Solving First-And Second-Order Total Variation Inverse Problem

Hamadouche, S. A., Boutemedjet, A., & Bouaraba, A. (2023) · International Conference on Electronics, Energy and Measurement (IC2EM) · doi:10.1109/ic2em59347.2023.10419751 · Cited by 4 · see citing works

Horizon Detection in Seismic Data: An Application of Linked Feature Detection from Multiple Time Series

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From the Sensor to Color Images

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PMA-Diffusion: A physics-guided mask-aware diffusion framework for traffic state estimation from sparse observations

Liu, L., Jin, Z., & Choi, S. (2026) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2026.105801 · Cited by 3 · see citing works

Performance enhancement of a scanning electron microscope using a deep convolutional neural network

Panchal, S. V., Datar, S. S., & Gopinathan, U. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac4a19 · Cited by 3 · see citing works

Regularized Least Squares Estimating Sensitivity for Self-calibrating Parallel Imaging

Liu, X., Ye, X., Zhang, S., & Liu, F. (2011) · Journal of Computers · doi:10.4304/jcp.6.5.857-864 · Cited by 3 · see citing works

Orthogonal basis expansion based atrial activity reconstruction for atrial fibrillation electrocardiogram analysis

Kodituwakku, S., Abhayapala, T. D., & Kennedy, R. A. (2009) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2009.5332608 · Cited by 3 · see citing works

Deep Proximal Gradient Method for Learned Convex Regularizers

Berk, A., Ma, Y., Boufounos, P. T., Wang, P. P., & Mansour, H. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094632 · Cited by 3 · see citing works

An Image-Denoising Framework Using ℓq Norm-Based Higher Order Variation and Fractional Variation with Overlapping Group Sparsity

Zhang, X., Cai, G., Li, M., & Bi, S. (2023) · Fractal and Fractional · doi:10.3390/fractalfract7080573 · Cited by 3 · see citing works

Improved Spectral Density Measurement from Estimated Reflectance Data with Kernel Ridge Regression

Eckhard, T., Klammer, M., Valero, E. M., & Hernández‐Andrés, J. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-07998-1_10 · Cited by 3 · see citing works

A fast image reconstruction algorithm for continuous wave diffuse optical tomography

Tsai, J.-J., Chen, N.-J., Fang, W.-C., & Chen, J.-S. (2011) · IEEE/NIH Life Science Systems and Applications Workshop · doi:10.1109/lissa.2011.5754164 · Cited by 3 · see citing works

Efficient image reconstruction under sparsity constraints with application to MRI and bioluminescence tomography

Guerquin-Kern, M., Baritaux, J.-C., & Unser, M. A. (2011) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2011.5947669 · Cited by 3 · see citing works

Time-frequency analysis compensating missing data for Atrial Fibrillation ECG assessment

Kodituwakku, S., Kennedy, R. A., & Abhayapala, T. D. (2011) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2011.5946485 · Cited by 3 · see citing works

The regularized tau estimator: A robust and efficient solution to ill-posed linear inverse problemsPreprint

Martinez-Camara, M., Muma, M., Béjar, B., Zoubir, A. M., & Vetterli, M. (2016) · TUbilio (Technical University of Darmstadt) · doi:10.48550/arxiv.1606.00812 · Cited by 3 · see citing works

Joint Reconstruction and Calibration using Regularization by DenoisingPreprint

Xie, M., Sun, Y., Liu, J., Wohlberg, B. E., & Kamilov, U. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.13391 · Cited by 3 · see citing works

Conductivity image enhancement in MREIT using adaptively weighted spatial averaging filter

Oh, T. I., Kim, H. J., Jeong, W. C., Wi, H., Kwon, O. I., & Woo, E. J. (2014) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-13-87 · Cited by 2 · see citing works

Dual-angle, spectral reconstruction imaging method for the determination of dielectric thin-film thickness

Kruschwitz, J. D., & Berns, R. S. (2017) · Optics Letters · doi:10.1364/ol.42.003032 · Cited by 2 · see citing works

Convergent Plug-And-Play Using Contractive Denoisers

Nair, P., & Chaudhury, K. N. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447001 · Cited by 2 · see citing works

Destriping model for adaptive removal of arbitrary oriented stripes in remote sensing images

Hamadouche, S. A., Boutemedjet, A., & Bouaraba, A. (2024) · Physica Scripta · doi:10.1088/1402-4896/ad6fe4 · Cited by 2 · see citing works

Analysis and Synthesis Denoisers for Forward-Backward Plug-and-Play Algorithms

Kowalski, M., Malézieux, B., Moreau, T., & Repetti, A. (2026) · SIAM Journal on Imaging Sciences · doi:10.1137/24m1711194 · Cited by 2 · see citing works

Assessing the performance of a spectral reflectance estimation method based on a diffraction grating and a spectroradiometer

Javoršek, D., Jerman, T., Rat, B., & Hladnik, A. (2014) · Coloration Technology · doi:10.1111/cote.12095 · Cited by 2 · see citing works

Comparison of Two Digital Cameras based on Spectral Data Estimation Obtained with Two Methods

Javoršek, D., Javoršek, D., & Jerman, T. (2015) · Acta Polytechnica Hungarica · doi:10.12700/aph.12.7.2015.7.11 · Cited by 2 · see citing works

Efficient solution of linear inverse problems using an iterative linear neural network with a generalization training approach

Tieng, Q. M., Su, J., Vegh, V., & Reutens, D. C. (2021) · Journal of Physics Communications · doi:10.1088/2399-6528/abebcf · Cited by 2 · see citing works

Solving inverse problems with autoencoders on learnt graphs

Majumdar, A. (2021) · Signal Processing · doi:10.1016/j.sigpro.2021.108300 · Cited by 2 · see citing works

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Bertero, M., & Boccacci, P. (2020) · CRC Press eBooks · doi:10.1201/9780367806941-10

Sparse inverse problems for Fourier imaging : applications to Optical Interferometry and Diffusion Magnetic Resonance Imaging

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Linear Inversion in Fourier Space

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Image reconstruction without prior informationPreprint

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