no code implementations • 16 Jan 2025 • Alper Kayabasi, Anil Kumar Vadathya, Guha Balakrishnan, Vishwanath Saragadam
At the core of our approach is the observation that INRs can be considered as a learnable dictionary, with the shapes of the basis functions governed by the weights of the INR, and their locations governed by the biases.
no code implementations • CVPR 2024 • Sarah Friday, Yunzi Shi, Yaswanth Cherivirala, Vishwanath Saragadam, Adithya Pediredla
Capturing the four measurements in sequence renders the system slow invariably causing inaccuracies in depth estimates due to motion in the scene or the camera.
no code implementations • 1 Oct 2023 • T. Mitchell Roddenberry, Vishwanath Saragadam, Maarten V. de Hoop, Richard G. Baraniuk
Implicit neural representations (INRs) have arisen as useful methods for representing signals on Euclidean domains.
1 code implementation • CVPR 2023 • Aniket Dashpute, Vishwanath Saragadam, Emma Alexander, Florian Willomitzer, Aggelos Katsaggelos, Ashok Veeraraghavan, Oliver Cossairt
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.
2 code implementations • CVPR 2023 • Vishwanath Saragadam, Daniel LeJeune, Jasper Tan, Guha Balakrishnan, Ashok Veeraraghavan, Richard G. Baraniuk
Implicit neural representations (INRs) have recently advanced numerous vision-related areas.
no code implementations • 13 Dec 2022 • Vishwanath Saragadam, Zheyi Han, Vivek Boominathan, Luocheng Huang, Shiyu Tan, Johannes E. Fröch, Karl F. Böhringer, Richard G. Baraniuk, Arka Majumdar, Ashok Veeraraghavan
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.
1 code implementation • 1 Nov 2022 • Lorenzo Luzi, Daniel LeJeune, Ali Siahkoohi, Sina AlEMohammad, Vishwanath Saragadam, Hossein Babaei, Naiming Liu, Zichao Wang, Richard G. Baraniuk
We study the interpolation capabilities of implicit neural representations (INRs) of images.
no code implementations • 3 Jul 2022 • Bhargav Ghanekar, Vishwanath Saragadam, Dushyant Mehra, Anna-Karin Gustavsson, Aswin Sankaranarayanan, Ashok Veeraraghavan
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.
no code implementations • 7 Apr 2022 • Vishwanath Saragadam, Randall Balestriero, Ashok Veeraraghavan, Richard G. Baraniuk
DeepTensor is a computationally efficient framework for low-rank decomposition of matrices and tensors using deep generative networks.
1 code implementation • 7 Feb 2022 • Vishwanath Saragadam, Jasper Tan, Guha Balakrishnan, Richard G. Baraniuk, Ashok Veeraraghavan
We introduce a new neural signal model designed for efficient high-resolution representation of large-scale signals.
no code implementations • 29 Sep 2021 • Vishwanath Saragadam, Vijay Rengarajan, Ryuichi Tadano, Tuo Zhuang, Hideki Oyaizu, Jun Murayama, Aswin C. Sankaranarayanan
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.
1 code implementation • 18 Aug 2021 • Vishwanath Saragadam, Akshat Dave, Ashok Veeraraghavan, Richard Baraniuk
We introduce DeepIR, a new thermal image processing framework that combines physically accurate sensor modeling with deep network-based image representation.
1 code implementation • 28 Dec 2020 • Vishwanath Saragadam, Michael DeZeeuw, Richard Baraniuk, Ashok Veeraraghavan, Aswin Sankaranarayanan
Hence, a scene-adaptive spatial sampling of an hyperspectral scene, guided by its super-pixel segmented image, is capable of obtaining high-quality reconstructions.
no code implementations • 16 Nov 2019 • Vishwanath Saragadam, Aswin Sankaranarayanan
We introduce and analyze the concept of space-spectrum uncertainty for certain commonly-used designs for spectrally programmable cameras.
no code implementations • ICCV 2019 • Vishwanath Saragadam, Jian Wang, Mohit Gupta, Shree Nayar
We propose Micro-baseline Structured Light (MSL), a novel 3D imaging approach designed for small form-factor devices such as cell-phones and miniature robots.
no code implementations • 13 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.
no code implementations • 26 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.
no code implementations • 16 Nov 2015 • Vishwanath Saragadam, Xin Li, Aswin Sankaranarayanan
Sparse representations using data dictionaries provide an efficient model particularly for signals that do not enjoy alternate analytic sparsifying transformations.