Skip to content
Branch: master
Find file History
tfxue Update README.md
Add a link to SIGGRAPH Asia presentation.
Latest commit ad70bf5 Oct 30, 2019
Permalink
Type Name Latest commit message Commit time
..
Failed to load latest commit information.
README.md Update README.md Oct 30, 2019
_config.yml Migrate config._yml to docs folder Oct 23, 2019
night-sight-teaser.png Migrate teaser figure to docs folder Oct 23, 2019

README.md

SIGGRAPH Asia 2019

Authors: Orly Liba, Kiran Murthy, Yun-Ta Tsai, Timothy Brooks, Tianfan Xue, Nikhil Karnad, Qiurui He, Jonathan T. Barron, Dillon Sharlet, Ryan Geiss, Samuel W. Hasinoff, Yael Pritch, Marc Levoy

figure1

Abstract

Taking photographs in low light using a mobile phone is challenging and rarely produces pleasing photographs.

Aside from the physical limits imposed by read noise and photon shot noise, these cameras are typically handheld, have small apertures and sensors, use mass-produced analog electronics that cannot easily be cooled, and are commonly used to photograph subjects that move, like children and pets. In this paper we describe a system for capturing clean, sharp, colorful photographs in light as low as 0.3 lux, where human vision becomes monochrome and indistinct.

To permit handheld photography without flash illumination, we capture, align, and combine multiple frames. Our system employs motion metering, which uses an estimate of motion magnitudes (whether due to handshake or moving objects) to identify the number of frames and the per-frame exposure times that together minimize both noise and motion blur in a captured burst.

We combine these frames using robust alignment and merging techniques that have been specialized for high-noise imagery.

To ensure accurate colors in such low light, we employ a learning-based auto white balancing algorithm. To prevent the photographs from looking like they were shot in daylight, we use tone mapping techniques inspired by illusionistic painting: increasing contrast, crushing shadows to black, and surrounding the scene with darkness. All of these processes are performed using the limited computational resources of a mobile device.

Our system can be used by novice photographers to produce shareable pictures in a few seconds based on a single shutter press, even in environments so dim that humans cannot see clearly.

Downloads

Results on HDR+ dataset: link

arXiv: https://arxiv.org/abs/1910.11336

Presentation in SIGGRAPH Asia 2019: link

You can’t perform that action at this time.