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Found 27 papers, 10 papers with code
Acoustic Neural 3D Reconstruction Under Pose Drift
We consider the problem of optimizing neural implicit surfaces for 3D reconstruction using acoustic images collected with drifting sensor poses.
Flash-Split: 2D Reflection Removal with Flash Cues and Latent Diffusion Separation
Transparent surfaces, such as glass, create complex reflections that obscure images and challenge downstream computer vision applications.
Repurposing Pre-trained Video Diffusion Models for Event-based Video Interpolation
In this work, we overcome the limited data challenge by adapting pre-trained video diffusion models trained on internet-scale datasets to EVFI.
Flash-Splat: 3D Reflection Removal with Flash Cues and Gaussian Splats
We introduce a simple yet effective approach for separating transmitted and reflected light.
Event3DGS: Event-Based 3D Gaussian Splatting for High-Speed Robot Egomotion
By combining differentiable rendering with explicit point-based scene representations, 3D Gaussian Splatting (3DGS) has demonstrated breakthrough 3D reconstruction capabilities.
WaveMo: Learning Wavefront Modulations to See Through Scattering
Imaging through scattering media is a fundamental and pervasive challenge in fields ranging from medical diagnostics to astronomy.
Adaptive LPD Radar Waveform Design with Generative Deep Learning
Our waveforms are designed to follow a distribution that is indistinguishable from the ambient radio frequency (RF) background -- while still being effective at ranging and sensing.
Bagged Deep Image Prior for Recovering Images in the Presence of Speckle Noise
We investigate both the theoretical and algorithmic aspects of likelihood-based methods for recovering a complex-valued signal from multiple sets of measurements, referred to as looks, affected by speckle (multiplicative) noise.
Estimating Epistemic and Aleatoric Uncertainty with a Single Model
Estimating and disentangling epistemic uncertainty, uncertainty that is reducible with more training data, and aleatoric uncertainty, uncertainty that is inherent to the task at hand, is critically important when applying machine learning to high-stakes applications such as medical imaging and weather forecasting.
AONeuS: A Neural Rendering Framework for Acoustic-Optical Sensor Fusion
Underwater perception and 3D surface reconstruction are challenging problems with broad applications in construction, security, marine archaeology, and environmental monitoring.
TiDy-PSFs: Computational Imaging with Time-Averaged Dynamic Point-Spread-Functions
We then demonstrate, in simulation, that time-averaged dynamic (TiDy) phase masks can offer substantially improved monocular depth estimation and extended depth-of-field imaging performance.
SUD$^2$: Supervision by Denoising Diffusion Models for Image Reconstruction
Many imaging inverse problems$\unicode{x2014}$such as image-dependent in-painting and dehazing$\unicode{x2014}$are challenging because their forward models are unknown or depend on unknown latent parameters.
Weakly-Supervised Semantic Segmentation of Ships Using Thermal Imagery
The United States coastline spans 95, 471 miles; a distance that cannot be effectively patrolled or secured by manual human effort alone.
Weakly-supervised Learning
Weakly supervised segmentation
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MetaDIP: Accelerating Deep Image Prior with Meta Learning
Deep image prior (DIP) is a recently proposed technique for solving imaging inverse problems by fitting the reconstructed images to the output of an untrained convolutional neural network.
Denoising Generalized Expectation-Consistent Approximation for MR Image Recovery
To solve inverse problems, plug-and-play (PnP) methods replace the proximal step in a convex optimization algorithm with a call to an application-specific denoiser, often implemented using a deep neural network (DNN).
TurbuGAN: An Adversarial Learning Approach to Spatially-Varying Multiframe Blind Deconvolution with Applications to Imaging Through Turbulence
We present a self-supervised and self-calibrating multi-shot approach to imaging through atmospheric turbulence, called TurbuGAN.
Supervision by Denoising for Medical Image Segmentation
SUD unifies stochastic averaging and spatial denoising techniques under a spatio-temporal denoising framework and alternates denoising and model weight update steps in an optimization framework for semi-supervision.
D-VDAMP: Denoising-based Approximate Message Passing for Compressive MRI
Plug and play (P&P) algorithms iteratively apply highly optimized image denoisers to impose priors and solve computational image reconstruction problems, to great effect.
SUREMap: Predicting Uncertainty in CNN-based Image Reconstruction Using Stein's Unbiased Risk Estimate
Convolutional neural networks (CNN) have emerged as a powerful tool for solving computational imaging reconstruction problems.
Deep Learning Techniques for Inverse Problems in Imaging
Recent work in machine learning shows that deep neural networks can be used to solve a wide variety of inverse problems arising in computational imaging.
Deep S$^3$PR: Simultaneous Source Separation and Phase Retrieval Using Deep Generative Models
This paper introduces and solves the simultaneous source separation and phase retrieval (S$^3$PR) problem.
Keyhole Imaging: Non-Line-of-Sight Imaging and Tracking of Moving Objects Along a Single Optical Path
Non-line-of-sight (NLOS) imaging and tracking is an emerging technology that allows the shape or position of objects around corners or behind diffusers to be recovered from transient, time-of-flight measurements.
Deep Optics for Single-shot High-dynamic-range Imaging
High-dynamic-range (HDR) imaging is crucial for many computer graphics and vision applications.
Unsupervised Learning with Stein's Unbiased Risk Estimator
We show that, in the context of image recovery, SURE and its generalizations can be used to train convolutional neural networks (CNNs) for a range of image denoising and recovery problems without any ground truth data.
prDeep: Robust Phase Retrieval with a Flexible Deep Network
Phase retrieval algorithms have become an important component in many modern computational imaging systems.
Learned D-AMP: Principled Neural Network based Compressive Image Recovery
The LDAMP network is easy to train, can be applied to a variety of different measurement matrices, and comes with a state-evolution heuristic that accurately predicts its performance.
From Denoising to Compressed Sensing
A key element in D-AMP is the use of an appropriate Onsager correction term in its iterations, which coerces the signal perturbation at each iteration to be very close to the white Gaussian noise that denoisers are typically designed to remove.
