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20152022
most citedFlatNet: Towards Photorealistic Scene Reconstruction from Lensless Measurements

98 citations · 127 across the 10 of their papers we have counts for

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eess.IV2021

Thermal Image Processing via Physics-Inspired Deep Networks

Vishwanath Saragadam, Akshat Dave, Ashok Veeraraghavan +1

We introduce DeepIR, a new thermal image processing framework that combines physically accurate sensor modeling with deep network-based image representation. Our key enabling obser…

eess.IV2021

High Resolution, Deep Imaging Using Confocal Time-of-flight Diffuse Optical Tomography

Yongyi Zhao, Ankit Raghuram, Hyun K. Kim +3

Light scattering by tissue severely limits how deep beneath the surface one can image, and the spatial resolution one can obtain from these images. Diffuse optical tomography (DOT)…

eess.IV2020

SASSI -- Super-Pixelated Adaptive Spatio-Spectral Imaging

Vishwanath Saragadam, Michael DeZeeuw, Richard Baraniuk +2

We introduce a novel video-rate hyperspectral imager with high spatial, and temporal resolutions. Our key hypothesis is that spectral profiles of pixels in a super-pixel of an over…

eess.IV2020

How to Train Neural Networks for Flare Removal

Yicheng Wu, Qiurui He, Tianfan Xue +4

When a camera is pointed at a strong light source, the resulting photograph may contain lens flare artifacts. Flares appear in a wide variety of patterns (halos, streaks, color ble…

eess.IV202098 cited

FlatNet: Towards Photorealistic Scene Reconstruction from Lensless Measurements

Salman S. Khan, Varun Sundar, Vivek Boominathan +2

Lensless imaging has emerged as a potential solution towards realizing ultra-miniature cameras by eschewing the bulky lens in a traditional camera. Without a focusing lens, the len…

eess.IV2020

The Benefit of Distraction: Denoising Remote Vitals Measurements using Inverse Attention

Ewa Nowara, Daniel McDuff, Ashok Veeraraghavan

Attention is a powerful concept in computer vision. End-to-end networks that learn to focus selectively on regions of an image or video often perform strongly. However, other image…