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Found 34 papers, 6 papers with code
PtychoDV: Vision Transformer-Based Deep Unrolling Network for Ptychographic Image Reconstruction
Ptychography is an imaging technique that captures multiple overlapping snapshots of a sample, illuminated coherently by a moving localized probe.
Learned Full Waveform Inversion Incorporating Task Information for Ultrasound Computed Tomography
Once trained, the CNN can perform real-time FWI image reconstruction from USCT waveform data.
Deep Equilibrium Learning of Explicit Regularizers for Imaging Inverse Problems
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.
TRINIDI: Time-of-Flight Resonance Imaging with Neutrons for Isotopic Density Inference
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.
Coordinate-Based Seismic Interpolation in Irregular Land Survey: A Deep Internal Learning Approach
Physical and budget constraints often result in irregular sampling, which complicates accurate subsurface imaging.
Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging
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.
Projected Multi-Agent Consensus Equilibrium for Ptychographic Image Reconstruction
Ptychography is a computational imaging technique using multiple, overlapping, coherently illuminated snapshots to achieve nanometer resolution by solving a nonlinear phase-field recovery problem.
Hyperspectral Neutron CT with Material Decomposition
Energy resolved neutron imaging (ERNI) is an advanced neutron radiography technique capable of non-destructively extracting spatial isotopic information within a given material.
Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition
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.
Connect the Dots: In Situ 4D Seismic Monitoring of CO2 Storage with Spatio-temporal CNNs
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.
Randomized Algorithms for Scientific Computing (RASC)
Randomized algorithms have propelled advances in artificial intelligence and represent a foundational research area in advancing AI for Science.
InversionNet3D: Efficient and Scalable Learning for 3D Full Waveform Inversion
Seismic full-waveform inversion (FWI) techniques aim to find a high-resolution subsurface geophysical model provided with waveform data.
CoIL: Coordinate-based Internal Learning for Imaging Inverse Problems
We propose Coordinate-based Internal Learning (CoIL) as a new deep-learning (DL) methodology for the continuous representation of measurements.
SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees
Deep unfolding networks have recently gained popularity in the context of solving imaging inverse problems.
Joint Reconstruction and Calibration using Regularization by Denoising
Cal-RED extends the traditional RED methodology to imaging problems that require the calibration of the measurement operator.
Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors
Regularization by denoising (RED) is a recently developed framework for solving inverse problems by integrating advanced denoisers as image priors.
Physics-Consistent Data-driven Waveform Inversion with Adaptive Data Augmentation
Seismic full-waveform inversion (FWI) is a nonlinear computational imaging technique that can provide detailed estimates of subsurface geophysical properties.
Scalable Plug-and-Play ADMM with Convergence Guarantees
Plug-and-play priors (PnP) is a broadly applicable methodology for solving inverse problems by exploiting statistical priors specified as denoisers.
Provable Convergence of Plug-and-Play Priors with MMSE denoisers
Plug-and-play priors (PnP) is a methodology for regularized image reconstruction that specifies the prior through an image denoiser.
Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning
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.
TGGLines: A Robust Topological Graph Guided Line Segment Detector for Low Quality Binary Images
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.
Two-layer Residual Sparsifying Transform Learning for Image Reconstruction
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.
Plug-In Stochastic Gradient Method
Plug-and-play priors (PnP) is a popular framework for regularized signal reconstruction by using advanced denoisers within an iterative algorithm.
Regularized Fourier Ptychography using an Online Plug-and-Play Algorithm
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.
Learning Multi-Layer Transform Models
Learned data models based on sparsity are widely used in signal processing and imaging applications.
An Online Plug-and-Play Algorithm for Regularized Image Reconstruction
The results in this paper have the potential to expand the applicability of the PnP framework to very large and redundant datasets.
Convolutional Dictionary Learning: A Comparative Review and New Algorithms
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.
First and Second Order Methods for Online Convolutional Dictionary Learning
Convolutional sparse representations are a form of sparse representation with a structured, translation invariant dictionary.
Convolutional Sparse Coding with Overlapping Group Norms
The most widely used form of convolutional sparse coding uses an $\ell_1$ regularization term.
Convolutional Sparse Coding: Boundary Handling Revisited
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.
Online Convolutional Dictionary Learning
While a number of different algorithms have recently been proposed for convolutional dictionary learning, this remains an expensive problem.
Convolutional Sparse Representations with Gradient Penalties
While convolutional sparse representations enjoy a number of useful properties, they have received limited attention for image reconstruction problems.
ADMM Penalty Parameter Selection by Residual Balancing
Appropriate selection of the penalty parameter is crucial to obtaining good performance from the Alternating Direction Method of Multipliers (ADMM).
Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation
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.
