Search Results for author: Aswin C. Sankaranarayanan

Found 26 papers, 6 papers with code

Programmable Spectral Filter Arrays for Hyperspectral Imaging

no code implementations29 Sep 2021 Aswin C. Sankaranarayanan, Vishwanath Saragadam, Vijay Rengarajan, Ryuichi Tadano, Tuo Zhuang, Hideki Oyaizu, Jun Murayama

Relying on this insight, we provide an optical schematic and an associated lab prototype for realizing the capability, as well as address the associated challenges at implementation using optical and computational innovations.

A Simple Framework for 3D Lensless Imaging with Programmable Masks

1 code implementation ICCV 2021 Yucheng Zheng, Yi Hua, Aswin C. Sankaranarayanan, M. Salman Asif

Existing methods for lensless imaging can recover the depth and intensity of the scene, but they require solving computationally-expensive inverse problems.

Depth Estimation

Towards Occlusion-Aware Multifocal Displays

no code implementations2 May 2020 Jen-Hao Rick Chang, Anat Levin, B. V. K. Vijaya Kumar, Aswin C. Sankaranarayanan

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

no code implementations11 Dec 2019 Vijay Rengarajan, Shuo Zhao, Ruiwen Zhen, John Glotzbach, Hamid Sheikh, Aswin C. Sankaranarayanan

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

no code implementations13 May 2019 Vishwanath Saragadam, Aswin C. Sankaranarayanan

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.

General Classification Material Classification

Learning to Separate Multiple Illuminants in a Single Image

no code implementations CVPR 2019 Zhuo Hui, Ayan Chakrabarti, Kalyan Sunkavalli, Aswin C. Sankaranarayanan

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

no code implementations ECCV 2018 Jian Wang, Joseph Bartels, William Whittaker, Aswin C. Sankaranarayanan, Srinivasa G. Narasimhan

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

no code implementations27 May 2018 Jen-Hao Rick Chang, B. V. K. Vijaya Kumar, Aswin C. Sankaranarayanan

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

no code implementations26 Jan 2018 Vishwanath Saragadam, Aswin C. Sankaranarayanan

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

1 code implementation ICCV 2017 J. H. Rick Chang, Chun-Liang Li, Barnabas Poczos, B. V. K. Vijaya Kumar, Aswin C. Sankaranarayanan

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.

Compressive Sensing Image Inpainting +1

The Geometry of First-Returning Photons for Non-Line-Of-Sight Imaging

no code implementations CVPR 2017 Chia-Yin Tsai, Kiriakos N. Kutulakos, Srinivasa G. Narasimhan, Aswin C. Sankaranarayanan

In this paper, we propose a new approach for NLOS imaging by studying the properties of first-returning photons from three-bounce light paths.

One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection Models

2 code implementations29 Mar 2017 J. H. Rick Chang, Chun-Liang Li, Barnabas Poczos, B. V. K. Vijaya Kumar, Aswin C. Sankaranarayanan

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.

Compressive Sensing Image Inpainting +1

Shape and Spatially-Varying Reflectance Estimation From Virtual Exemplars

1 code implementation16 Dec 2015 Zhuo Hui, Aswin C. Sankaranarayanan

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.

BRDF estimation

Photometric Stereo With Small Angular Variations

no code implementations ICCV 2015 Jian Wang, Yasuyuki Matsushita, Boxin Shi, Aswin C. Sankaranarayanan

This paper studies the effect of small angular variations in illumination directions to photometric stereo.

A Dictionary-based Approach for Estimating Shape and Spatially-Varying Reflectance

no code implementations14 Mar 2015 Zhuo Hui, Aswin C. Sankaranarayanan

We present a technique for estimating the shape and reflectance of an object in terms of its surface normals and spatially-varying BRDF.

BRDF estimation

LiSens --- A Scalable Architecture for Video Compressive Sensing

1 code implementation14 Mar 2015 Jian Wang, Mohit Gupta, Aswin C. Sankaranarayanan

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.

Compressive Sensing Video Compressive Sensing

Video Compressive Sensing for Spatial Multiplexing Cameras using Motion-Flow Models

no code implementations9 Mar 2015 Aswin C. Sankaranarayanan, Lina Xu, Christoph Studer, Yun Li, Kevin Kelly, Richard G. Baraniuk

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.

Compressive Sensing Optical Flow Estimation +1

Video Compressive Sensing for Dynamic MRI

no code implementations30 Jan 2014 Jianing V. Shi, Wotao Yin, Aswin C. Sankaranarayanan, Richard G. Baraniuk

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.

Compressive Sensing Video Compressive Sensing

Greedy Feature Selection for Subspace Clustering

no code implementations19 Mar 2013 Eva L. Dyer, Aswin C. Sankaranarayanan, Richard G. Baraniuk

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

no code implementations23 Jan 2012 Aswin C. Sankaranarayanan, Pavan K Turaga, Rama Chellappa, Richard G. Baraniuk

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.

Compressive Sensing

SpaRCS: Recovering low-rank and sparse matrices from compressive measurements

no code implementations NeurIPS 2011 Andrew E. Waters, Aswin C. Sankaranarayanan, Richard Baraniuk

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 $\mathbf{y} = \mathcal{A}(\mathbf{M}) = \mathcal{A}({\bf L}+{\bf S})$.

Compressive Sensing Matrix Completion +1

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