Search Results for author:
Found 52 papers, 16 papers with code
3PointTM: Faster Measurement of High-Dimensional Transmission Matrices
A transmission matrix (TM) describes the linear relationship between input and output phasor fields when a coherent wave passes through a scattering medium.
FreeCam3D: Snapshot Structured Light 3D with Freely-Moving Cameras
The projected pattern can be observed in part or full by any camera, to reconstruct both the 3D map of the scene and the camera pose in the projector coordinates.
Metric-guided Image Reconstruction Bounds via Conformal Prediction
We apply our method to sparse-view CT for downstream radiotherapy planning and show 1) that metric-guided bounds have valid coverage for downstream metrics while conventional pixel-wise bounds do not and 2) anatomical differences of upper/lower bounds between metric-guided and pixel-wise methods.
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.
DecentNeRFs: Decentralized Neural Radiance Fields from Crowdsourced Images
It learns photorealistic scene representations by decomposing users' 3D views into personal and global NeRFs and a novel optimally weighted aggregation of only the latter.
Passive Snapshot Coded Aperture Dual-Pixel RGB-D Imaging
Our resulting CADS imaging system demonstrates improvement of >1. 5dB PSNR in all-in-focus (AIF) estimates and 5-6% in depth estimation quality over naive DP sensing for a wide range of aperture settings.
ConVRT: Consistent Video Restoration Through Turbulence with Test-time Optimization of Neural Video Representations
tmospheric turbulence presents a significant challenge in long-range imaging.
Event-based Motion-Robust Accurate Shape Estimation for Mixed Reflectance Scenes
In this paper, we present a novel event-based structured light system that enables fast 3D imaging of mixed reflectance scenes with high accuracy.
ISLAND: Informing Brightness and Surface Temperature Through a Land Cover-based Interpolator
Our approach uses thermal infrared images from Landsat 8 (at 30 m resolution with 16-day revisit cycles) and the NLCD land cover dataset.
CT Reconstruction from Few Planar X-rays with Application towards Low-resource Radiotherapy
In this work, we propose a method to generate CT volumes from few (<5) planar X-ray observations using a prior data distribution, and perform the first evaluation of such a reconstruction algorithm for a clinical application: radiotherapy planning.
NeRT: Implicit Neural Representations for General Unsupervised Turbulence Mitigation
The atmospheric and water turbulence mitigation problems have emerged as challenging inverse problems in computer vision and optics communities over the years.
Role of Transients in Two-Bounce Non-Line-of-Sight Imaging
Specifically, we study how ToF information can reduce the number of measurements and spatial resolution needed for shape reconstruction.
Thermal Spread Functions (TSF): Physics-guided Material Classification
Our key observation is that the rate of heating and cooling of an object depends on the unique intrinsic properties of the material, namely the emissivity and diffusivity.
WIRE: Wavelet Implicit Neural Representations
Implicit neural representations (INRs) have recently advanced numerous vision-related areas.
ORCa: Glossy Objects As Radiance-Field Cameras
By converting these objects into cameras, we can unlock exciting applications, including imaging beyond the camera's field-of-view and from seemingly impossible vantage points, e. g. from reflections on the human eye.
Foveated Thermal Computational Imaging in the Wild Using All-Silicon Meta-Optics
A computational backend then utilizes a deep image prior to separate the resultant multiplexed image or video into a foveated image consisting of a high-resolution center and a lower-resolution large field of view context.
ORCa: Glossy Objects as Radiance Field Cameras
By converting these objects into cameras, we can unlock exciting applications, including imaging beyond the camera's field-of-view and from seemingly impossible vantage points, e. g. from reflections on the human eye.
Learning Phase Mask for Privacy-Preserving Passive Depth Estimation
With over a billion sold each year, cameras are not only becoming ubiquitous and omnipresent, but are driving progress in a wide range of applications such as augmented/virtual reality, robotics, surveillance, security, autonomous navigation and many others.
PS$^2$F: Polarized Spiral Point Spread Function for Single-Shot 3D Sensing
A special property of the phase mask used for generating the DHPSF is that a separation of the phase mask into two halves leads to a spatial separation of the two lobes.
DeepTensor: Low-Rank Tensor Decomposition with Deep Network Priors
DeepTensor is a computationally efficient framework for low-rank decomposition of matrices and tensors using deep generative networks.
PANDORA: Polarization-Aided Neural Decomposition Of Radiance
Reconstructing an object's geometry and appearance from multiple images, also known as inverse rendering, is a fundamental problem in computer graphics and vision.
MINER: Multiscale Implicit Neural Representations
We introduce a new neural signal model designed for efficient high-resolution representation of large-scale signals.
Thermal Image Processing via Physics-Inspired Deep Networks
We introduce DeepIR, a new thermal image processing framework that combines physically accurate sensor modeling with deep network-based image representation.
CodedStereo: Learned Phase Masks for Large Depth-of-field Stereo
Conventional stereo suffers from a fundamental trade-off between imaging volume and signal-to-noise ratio (SNR) -- due to the conflicting impact of aperture size on both these variables.
High Resolution, Deep Imaging Using Confocal Time-of-flight Diffuse Optical Tomography
Diffuse optical tomography (DOT) is one of the most powerful techniques for imaging deep within tissue -- well beyond the conventional $\sim$10-15 mean scattering lengths tolerated by ballistic imaging techniques such as confocal and two-photon microscopy.
SACoD: Sensor Algorithm Co-Design Towards Efficient CNN-powered Intelligent PhlatCam
PhlatCam, with its form factor potentially reduced by orders of magnitude, has emerged as a promising solution to the first aforementioned challenge, while the second one remains a bottleneck.
The Benefit of Distraction: Denoising Camera-Based Physiological Measurements Using Inverse Attention
The core idea is that the signal of interest is stronger in some pixels ("foreground"), and by selectively focusing computation on these pixels, networks can extract subtle information buried in noise and other sources of corruption.
SASSI -- Super-Pixelated Adaptive Spatio-Spectral Imaging
Hence, a scene-adaptive spatial sampling of an hyperspectral scene, guided by its super-pixel segmented image, is capable of obtaining high-quality reconstructions.
How to Train Neural Networks for Flare Removal
When a camera is pointed at a strong light source, the resulting photograph may contain lens flare artifacts.
FlatNet: Towards Photorealistic Scene Reconstruction from Lensless Measurements
Lensless imaging has emerged as a potential solution towards realizing ultra-miniature cameras by eschewing the bulky lens in a traditional camera.
The Benefit of Distraction: Denoising Remote Vitals Measurements using Inverse Attention
A convolutional attention network is used to learn which regions of a video contain the physiological signal and generate a preliminary estimate.
Fine-grained Classification using Heterogeneous Web Data and Auxiliary Categories
In the extreme case, given a set of test categories without any well-labeled training data, the majority of existing works can be grouped into the following two research directions: 1) crawl noisy labeled web data for the test categories as training data, which is dubbed as webly supervised learning; 2) transfer the knowledge from auxiliary categories with well-labeled training data to the test categories, which corresponds to zero-shot learning setting.
Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions
The current trend of pushing CNNs deeper with convolutions has created a pressing demand to achieve higher compression gains on CNNs where convolutions dominate the computation and parameter amount (e. g., GoogLeNet, ResNet and Wide ResNet).
Deep $k$-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions
The current trend of pushing CNNs deeper with convolutions has created a pressing demand to achieve higher compression gains on CNNs where convolutions dominate the computation and parameter amount (e. g., GoogLeNet, ResNet and Wide ResNet).
Webly Supervised Learning Meets Zero-Shot Learning: A Hybrid Approach for Fine-Grained Classification
The drawbacks of the above two directions motivate us to design a new framework which can jointly leverage both web data and auxiliary labeled categories to predict the test categories that are not associated with any well-labeled training images.
Fast Retinomorphic Event Stream for Video Recognition and Reinforcement Learning
Good temporal representations are crucial for video understanding, and the state-of-the-art video recognition framework is based on two-stream networks.
Learning from Noisy Web Data with Category-level Supervision
As tons of photos are being uploaded to public websites (e. g., Flickr, Bing, and Google) every day, learning from web data has become an increasingly popular research direction because of freely available web resources, which is also referred to as webly supervised learning.
prDeep: Robust Phase Retrieval with a Flexible Deep Network
Phase retrieval algorithms have become an important component in many modern computational imaging systems.
Reblur2Deblur: Deblurring Videos via Self-Supervised Learning
Motion blur is a fundamental problem in computer vision as it impacts image quality and hinders inference.
Zero-Shot Learning via Category-Specific Visual-Semantic Mapping
Zero-Shot Learning (ZSL) aims to classify a test instance from an unseen category based on the training instances from seen categories, in which the gap between seen categories and unseen categories is generally bridged via visual-semantic mapping between the low-level visual feature space and the intermediate semantic space.
Online Reweighted Least Squares Algorithm for Sparse Recovery and Application to Short-Wave Infrared Imaging
The proposed online algorithm is useful in a setting where one seeks to design a progressive decoding strategy to reconstruct a sparse signal from linear measurements so that one does not have to wait until all measurements are acquired.
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.
Spatial Phase-Sweep: Increasing temporal resolution of transient imaging using a light source array
Transient imaging or light-in-flight techniques capture the propagation of an ultra-short pulse of light through a scene, which in effect captures the optical impulse response of the scene.
Depth Selective Camera: A Direct, On-Chip, Programmable Technique for Depth Selectivity in Photography
Time of flight (ToF) cameras use a temporally modulated light source and measure correlation between the reflected light and a sensor modulation pattern, in order to infer scene depth.
Toward Long Distance, Sub-diffraction Imaging Using Coherent Camera Arrays
Recent advances in ptychography have demonstrated that one can image beyond the diffraction limit of the objective lens in a microscope.
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.
FlatCam: Thin, Bare-Sensor Cameras using Coded Aperture and Computation
FlatCam is a thin form-factor lensless camera that consists of a coded mask placed on top of a bare, conventional sensor array.
TabletGaze: Unconstrained Appearance-based Gaze Estimation in Mobile Tablets
We study gaze estimation on tablets, our key design goal is uncalibrated gaze estimation using the front-facing camera during natural use of tablets, where the posture and method of holding the tablet is not constrained.
FPA-CS: Focal Plane Array-based Compressive Imaging in Short-wave Infrared
Unfortunately, the measurement rate of a SPC is insufficient to enable imaging at high spatial and temporal resolutions.
DistancePPG: Robust non-contact vital signs monitoring using a camera
The gains in SNR of camera-based PPG estimated using distancePPG translate into reduction of the error in vital sign estimation, and thus expand the scope of camera-based vital sign monitoring to potentially challenging scenarios.
Fast Sublinear Sparse Representation using Shallow Tree Matching Pursuit
Sparse approximations using highly over-complete dictionaries is a state-of-the-art tool for many imaging applications including denoising, super-resolution, compressive sensing, light-field analysis, and object recognition.
A Framework for the Analysis of Computational Imaging Systems with Practical Applications
Unfortunately, a detailed analysis of CI has proven to be a challenging problem because performance depends equally on three components: (1) the optical multiplexing, (2) the noise characteristics of the sensor, and (3) the reconstruction algorithm.
