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Found 23 papers, 5 papers with code
Neural Volumetric Reconstruction for Coherent Synthetic Aperture Sonar
However, image formation is typically under-constrained due to a limited number of measurements and bandlimited hardware, which limits the capabilities of existing reconstruction methods.
Eulerian Phase-based Motion Magnification for High-Fidelity Vital Sign Estimation with Radar in Clinical Settings
Efficient and accurate detection of subtle motion generated from small objects in noisy environments, as needed for vital sign monitoring, is challenging, but can be substantially improved with magnification.
Towards Live 3D Reconstruction from Wearable Video: An Evaluation of V-SLAM, NeRF, and Videogrammetry Techniques
3D reconstruction algorithms should utilize the low cost and pervasiveness of video camera sensors, from both overhead and soldier-level perspectives.
Robust Vocal Quality Feature Embeddings for Dysphonic Voice Detection
In this paper, we propose a deep learning framework for generating acoustic feature embeddings sensitive to vocal quality and robust across different corpora.
SINR: Deconvolving Circular SAS Images Using Implicit Neural Representations
In theory, deconvolution overcomes bandwidth limitations by reversing the PSF-induced blur and recovering the scene's scattering distribution.
Adaptive Subsampling for ROI-based Visual Tracking: Algorithms and FPGA Implementation
Our adaptive subsampling algorithms comprise an object detector and an ROI predictor (Kalman filter) which operate in conjunction to optimize the energy efficiency of the vision pipeline with the end task being object tracking.
Implicit Neural Representations for Deconvolving SAS Images
This work is an important first step towards applying neural networks for SAS image deconvolution.
Deep Camera Obscura: An Image Restoration Pipeline for Lensless Pinhole Photography
The lensless pinhole camera is perhaps the earliest and simplest form of an imaging system using only a pinhole-sized aperture in place of a lens.
Dynamic CT Reconstruction from Limited Views with Implicit Neural Representations and Parametric Motion Fields
However, if the scene is moving too fast, then the sampling occurs along a limited view and is difficult to reconstruct due to spatiotemporal ambiguities.
Restoring degraded speech via a modified diffusion model
We replace the mel-spectrum upsampler in DiffWave with a deep CNN upsampler, which is trained to alter the degraded speech mel-spectrum to match that of the original speech.
Unsupervised Non-Rigid Image Distortion Removal via Grid Deformation
In this paper, we present a novel unsupervised network to recover the latent distortion-free image.
Differentiable Programming for Hyperspectral Unmixing using a Physics-based Dispersion Model
The dispersion model is introduced to simulate realistic spectral variation, and an efficient method to fit the parameters is presented.
Coupling Rendering and Generative Adversarial Networks for Artificial SAS Image Generation
Acquisition of Synthetic Aperture Sonar (SAS) datasets is bottlenecked by the costly deployment of SAS imaging systems, and even when data acquisition is possible, the data is often skewed towards containing barren seafloor rather than objects of interest.
Adaptive Lighting for Data-Driven Non-Line-of-Sight 3D Localization and Object Identification
We achieve an average identification of 87. 1% object identification for four classes of objects, and average localization of the NLOS object's centroid with a mean-squared error (MSE) of 1. 97 cm in the occluded region for real data taken from a hardware prototype.
Non-Parametric Priors For Generative Adversarial Networks
The advent of generative adversarial networks (GAN) has enabled new capabilities in synthesis, interpolation, and data augmentation heretofore considered very challenging.
CS-VQA: Visual Question Answering with Compressively Sensed Images
Visual Question Answering (VQA) is a complex semantic task requiring both natural language processing and visual recognition.
EVA$^2$: Exploiting Temporal Redundancy in Live Computer Vision
Hardware support for deep convolutional neural networks (CNNs) is critical to advanced computer vision in mobile and embedded devices.
Compressive Light Field Reconstructions using Deep Learning
We test our network reconstructions on synthetic light fields, simulated coded measurements of real light fields captured from a Lytro Illum camera, and real coded images from a custom CMOS diffractive light field camera.
Reconfiguring the Imaging Pipeline for Computer Vision
We propose a new image sensor design that can compensate for skipping these stages.
Deep Learning with Energy-efficient Binary Gradient Cameras
Power consumption is a critical factor for the deployment of embedded computer vision systems.
ASP Vision: Optically Computing the First Layer of Convolutional Neural Networks using Angle Sensitive Pixels
Deep learning using convolutional neural networks (CNNs) is quickly becoming the state-of-the-art for challenging computer vision applications.
Depth Fields: Extending Light Field Techniques to Time-of-Flight Imaging
In this paper, we explore the strengths and weaknesses of combining light field and time-of-flight imaging, particularly the feasibility of an on-chip implementation as a single hybrid depth sensor.
Frequency Domain TOF: Encoding Object Depth in Modulation Frequency
Time of flight cameras may emerge as the 3-D sensor of choice.
