Search Results for author:
Found 28 papers, 6 papers with code
Neural Kaleidoscopic Space Sculpting
Full-surround 3D reconstruction is critical for many applications, such as augmented and virtual reality.
Single-Photon Structured Light
We present a novel structured light technique that uses Single Photon Avalanche Diode (SPAD) arrays to enable 3D scanning at high-frame rates and low-light levels.
Programmable Spectral Filter Arrays using Phase Spatial Light Modulator
Spatially varying spectral modulation can be implemented using a liquid crystal spatial light modulator (SLM) since it provides an array of liquid crystal cells, each of which can be purposed to act as a programmable spectral filter array.
A Simple Framework for 3D Lensless Imaging with Programmable Masks
Existing methods for lensless imaging can recover the depth and intensity of the scene, but they require solving computationally-expensive inverse problems.
Towards Occlusion-Aware Multifocal Displays
Multifocal displays, one of the classic approaches to satisfy the accommodation cue, place virtual content at multiple focal planes, each at a di erent depth.
Photosequencing of Motion Blur using Short and Long Exposures
Adopting a computational photography approach, we propose to capture two short exposure images, along with the original blurred long exposure image to aid in the aforementioned challenges.
Programmable Spectrometry -- Per-pixel Classification of Materials using Learned Spectral Filters
We use this camera to optically implement the spectral filtering of the scene's hyperspectral image with the bank of spectral profiles needed to perform per-pixel material classification.
Learning to Separate Multiple Illuminants in a Single Image
We present a method to separate a single image captured under two illuminants, with different spectra, into the two images corresponding to the appearance of the scene under each individual illuminant.
Programmable Triangulation Light Curtains
A vehicle on a road or a robot in the field does not need a full-featured 3D depth sensor to detect potential collisions or monitor its blind spot.
Towards Multifocal Displays with Dense Focal Stacks
We present a virtual reality display that is capable of generating a dense collection of depth/focal planes.
KRISM --- Krylov Subspace-based Optical Computing of Hyperspectral Images
We present an adaptive imaging technique that optically computes a low-rank approximation of a scene's hyperspectral image, conceptualized as a matrix.
One Network to Solve Them All -- Solving Linear Inverse Problems Using Deep Projection Models
While deep learning methods have achieved state-of-the-art performance in many challenging inverse problems like image inpainting and super-resolution, they invariably involve problem-specific training of the networks.
Reflectance Capture Using Univariate Sampling of BRDFs
A collocated setup provides only a 1-D "univariate" sampling of the 4-D BRDF.
The Geometry of First-Returning Photons for Non-Line-Of-Sight Imaging
In this paper, we propose a new approach for NLOS imaging by studying the properties of first-returning photons from three-bounce light paths.
Illuminant Spectra-based Source Separation Using Flash Photography
Real-world lighting often consists of multiple illuminants with different spectra.
One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection Models
On the other hand, traditional methods using signal priors can be used in all linear inverse problems but often have worse performance on challenging tasks.
Random Features for Sparse Signal Classification
Random features is an approach for kernel-based inference on large datasets.
Shape and Spatially-Varying Reflectance Estimation From Virtual Exemplars
At the core of our techniques is the assumption that the BRDF at each pixel lies in the non-negative span of a known BRDF dictionary. This assumption enables a per-pixel surface normal and BRDF estimation framework that is computationally tractable and requires no initialization in spite of the underlying problem being non-convex.
Photometric Stereo With Small Angular Variations
This paper studies the effect of small angular variations in illumination directions to photometric stereo.
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.
A Dictionary-based Approach for Estimating Shape and Spatially-Varying Reflectance
We present a technique for estimating the shape and reflectance of an object in terms of its surface normals and spatially-varying BRDF.
LiSens --- A Scalable Architecture for Video Compressive Sensing
The measurement rate of cameras that take spatially multiplexed measurements by using spatial light modulators (SLM) is often limited by the switching speed of the SLMs.
Adaptive-Rate Sparse Signal Reconstruction With Application in Compressive Background Subtraction
We propose and analyze an online algorithm for reconstructing a sequence of signals from a limited number of linear measurements.
Video Compressive Sensing for Spatial Multiplexing Cameras using Motion-Flow Models
In this paper, we propose the CS multi-scale video (CS-MUVI) sensing and recovery framework for high-quality video acquisition and recovery using SMCs.
Video Compressive Sensing for Dynamic MRI
We apply this framework to accelerate the acquisition process of dynamic MRI and show it achieves the best reconstruction accuracy with the least computational time compared with existing algorithms in the literature.
Greedy Feature Selection for Subspace Clustering
To learn a union of subspaces from a collection of data, sets of signals in the collection that belong to the same subspace must be identified in order to obtain accurate estimates of the subspace structures present in the data.
Compressive Acquisition of Dynamic Scenes
Compressive sensing (CS) is a new approach for the acquisition and recovery of sparse signals and images that enables sampling rates significantly below the classical Nyquist rate.
SpaRCS: Recovering low-rank and sparse matrices from compressive measurements
We consider the problem of recovering a matrix $\mathbf{M}$ that is the sum of a low-rank matrix $\mathbf{L}$ and a sparse matrix $\mathbf{S}$ from a small set of linear measurements of the form $\mathbf{y} = \mathcal{A}(\mathbf{M}) = \mathcal{A}({\bf L}+{\bf S})$.
