High-Contrast Reflection Tomography with Total-Variation Constraints
- URL: http://arxiv.org/abs/2005.02903v2
- Date: Mon, 14 Dec 2020 14:44:29 GMT
- Title: High-Contrast Reflection Tomography with Total-Variation Constraints
- Authors: Ajinkya Kadu and Hassan Mansour and Petros T. Boufounos
- Abstract summary: Inverse scattering is the process of estimating the spatial distribution of the scattering potential of an object by measuring the scattered wavefields around it.
We propose a constrained incremental frequency inversion framework that requires no side information from a background model of the object.
- Score: 26.873262435961333
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Inverse scattering is the process of estimating the spatial distribution of
the scattering potential of an object by measuring the scattered wavefields
around it. In this paper, we consider reflection tomography of high contrast
objects that commonly occurs in ground-penetrating radar, exploration
geophysics, terahertz imaging, ultrasound, and electron microscopy. Unlike
conventional transmission tomography, the reflection regime is severely
ill-posed since the measured wavefields contain far less spatial frequency
information of the target object. We propose a constrained incremental
frequency inversion framework that requires no side information from a
background model of the object. Our framework solves a sequence of regularized
least-squares subproblems that ensure consistency with the measured scattered
wavefield while imposing total-variation and non-negativity constraints. We
propose a proximal Quasi-Newton method to solve the resulting subproblem and
devise an automatic parameter selection routine to determine the constraint of
each subproblem. We validate the performance of our approach on synthetic
low-resolution phantoms and with a mismatched forward model test on a
high-resolution phantom.
Related papers
- QMambaBSR: Burst Image Super-Resolution with Query State Space Model [55.56075874424194]
Burst super-resolution aims to reconstruct high-resolution images with higher quality and richer details by fusing the sub-pixel information from multiple burst low-resolution frames.
In BusrtSR, the key challenge lies in extracting the base frame's content complementary sub-pixel details while simultaneously suppressing high-frequency noise disturbance.
We introduce a novel Query Mamba Burst Super-Resolution (QMambaBSR) network, which incorporates a Query State Space Model (QSSM) and Adaptive Up-sampling module (AdaUp)
arXiv Detail & Related papers (2024-08-16T11:15:29Z) - FlowDepth: Decoupling Optical Flow for Self-Supervised Monocular Depth Estimation [8.78717459496649]
We propose FlowDepth, where a Dynamic Motion Flow Module (DMFM) decouples the optical flow by a mechanism-based approach and warps the dynamic regions thus solving the mismatch problem.
For the unfairness of photometric errors caused by high-freq and low-texture regions, we use Depth-Cue-Aware Blur (DCABlur) and Cost-Volume sparsity loss respectively at the input and the loss level to solve the problem.
arXiv Detail & Related papers (2024-03-28T10:31:23Z) - Weighted Spectral Filters for Kernel Interpolation on Spheres: Estimates
of Prediction Accuracy for Noisy Data [21.67168506689593]
We introduce a weighted spectral filter approach to reduce the condition number of the kernel matrix and then stabilize kernel.
Using a recently developed integral operator approach for spherical data analysis, we theoretically demonstrate that the proposed weighted spectral filter approach succeeds in breaking through the bottleneck of kernel.
We provide optimal approximation rates of the new method to show that our approach does not compromise the predicting accuracy.
arXiv Detail & Related papers (2024-01-16T13:46:10Z) - A Variational Perspective on Solving Inverse Problems with Diffusion
Models [101.831766524264]
Inverse tasks can be formulated as inferring a posterior distribution over data.
This is however challenging in diffusion models since the nonlinear and iterative nature of the diffusion process renders the posterior intractable.
We propose a variational approach that by design seeks to approximate the true posterior distribution.
arXiv Detail & Related papers (2023-05-07T23:00:47Z) - Passive superresolution imaging of incoherent objects [63.942632088208505]
Method consists of measuring the field's spatial mode components in the image plane in the overcomplete basis of Hermite-Gaussian modes and their superpositions.
Deep neural network is used to reconstruct the object from these measurements.
arXiv Detail & Related papers (2023-04-19T15:53:09Z) - Microseismic source imaging using physics-informed neural networks with
hard constraints [4.07926531936425]
We propose a direct microseismic imaging framework based on physics-informed neural networks (PINNs)
We use the PINNs to represent a multi-frequency wavefield and then apply inverse Fourier transform to extract the source image.
We further apply our method to hydraulic fracturing monitoring field data, and demonstrate that our method can correctly image the source with fewer artifacts.
arXiv Detail & Related papers (2023-04-09T21:10:39Z) - Frequency-Aware Self-Supervised Monocular Depth Estimation [41.97188738587212]
We present two versatile methods to enhance self-supervised monocular depth estimation models.
The high generalizability of our methods is achieved by solving the fundamental and ubiquitous problems in photometric loss function.
We are the first to propose blurring images to improve depth estimators with an interpretable analysis.
arXiv Detail & Related papers (2022-10-11T14:30:26Z) - Orthogonal Matrix Retrieval with Spatial Consensus for 3D Unknown-View
Tomography [58.60249163402822]
Unknown-view tomography (UVT) reconstructs a 3D density map from its 2D projections at unknown, random orientations.
The proposed OMR is more robust and performs significantly better than the previous state-of-the-art OMR approach.
arXiv Detail & Related papers (2022-07-06T21:40:59Z) - Super-resolution GANs of randomly-seeded fields [68.8204255655161]
We propose a novel super-resolution generative adversarial network (GAN) framework to estimate field quantities from random sparse sensors.
The algorithm exploits random sampling to provide incomplete views of the high-resolution underlying distributions.
The proposed technique is tested on synthetic databases of fluid flow simulations, ocean surface temperature distributions measurements, and particle image velocimetry data.
arXiv Detail & Related papers (2022-02-23T18:57:53Z) - Uncalibrated Neural Inverse Rendering for Photometric Stereo of General
Surfaces [103.08512487830669]
This paper presents an uncalibrated deep neural network framework for the photometric stereo problem.
Existing neural network-based methods either require exact light directions or ground-truth surface normals of the object or both.
We propose an uncalibrated neural inverse rendering approach to this problem.
arXiv Detail & Related papers (2020-12-12T10:33:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.