a) Previously described result (b) Previously described result, gained (c) Our result Fig. 1. We present a system that uses a novel combination of motion-adaptive burst capture, robust temporal denoising, learning-based white balance, and tone mapping to create high quality photographs in low light on a handheld mobile device. Here we show a comparison of a photograph generated by the burst photography system described in (Hasino et al. 2016) and the system described in this paper, running on the same mobile camera. In this low-light se ing (about 0.4 lux), the previous system generates an underexposed result (a). Brightening the image (b) reveals significant noise, especially chroma noise, which results in loss of detail and an unpleasantly blotchy appearance. Additionally, the colors of the face appear too orange. Our pipeline (c) produces detailed images by selecting a longer exposure time due to the low scene motion (in this se ing, extended from 0.14 s to 0.33 s), robustly aligning and merging a larger number of frames (13 frames instead of 6), and reproducing colors reliably by training a model for predicting the white balance gains specifically in low light. Additionally, we apply local tone mapping that brightens the shadows without over-clipping highlights or sacrificing global contrast.Taking photographs in low light using a mobile phone is challenging and rarely produces pleasing results. 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 monochromatic and indistinct. To permit handheld photography without ash 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 are 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 shu er press, even in environments so dim that humans cannot see clearly.
This paper presents details of the SkySat-1 mission, which is the first microsatellite-class commercial earthobservation system to generate sub-meter resolution panchromatic imagery, in addition to sub-meter resolution 4-band pan-sharpened imagery. SkySat-1 was built and launched for an order of magnitude lower cost than similarly performing missions. The low-cost design enables the deployment of a large imaging constellation that can provide imagery with both high temporal resolution and high spatial resolution.One key enabler of the SkySat-1 mission was simplifying the spacecraft design and instead relying on groundbased image processing to achieve high-performance at the system level. The imaging instrument consists of a custom-designed high-quality optical telescope and commercially-available high frame rate CMOS image sensors. While each individually captured raw image frame shows moderate quality, ground-based image processing algorithms improve the raw data by combining data from multiple frames to boost image signal-to-noise ratio (SNR) and decrease the ground sample distance (GSD) in a process Skybox calls "digital TDI". Careful quality assessment and tuning of the spacecraft, payload, and algorithms was necessary to generate high-quality panchromatic, multispectral, and pan-sharpened imagery. Furthermore, the framing sensor configuration enabled the first commercial High-Definition full-frame rate panchromatic video to be captured from space, with approximately 1 meter ground sample distance.Details of the SkySat-1 imaging instrument and ground-based image processing system are presented, as well as an overview of the work involved with calibrating and validating the system. Examples of raw and processed imagery are shown, and the raw imagery is compared to pre-launch simulated imagery used to tune the image processing algorithms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.