Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 22950133

Binocular high dynamic range imaging system based on digital micromirror device

Sun, Honghai·Wang, Yanjie·Yang, Hang·Wu, Pei
View original source ↗Export metadata ↓

Abstract

In the applications of scientific imaging and space exploration, the dynamic range of imaging systems is usually required to reach more than 120 dB. In order to observe a highly dynamic scene in real time, we designed an imaging system based on a digital micromirror device (DMD) that is used as a spatial light modulator. First, we designed a binocular highly dynamic light-adjusting system based on a DMD according to the DMD’s optical structure. Second, in order to realize the registration between the micromirrors of a DMD and pixels of the two cameras, a pixel-matching algorithm was developed. Finally, we introduce a novel light-adjusting algorithm that can recover the highly dynamic data of the dynamic scene. Experiments showed that the deviation between the DMD and the two cameras is reduced to 0.48 pixels after correction, and that bright and dark targets in a high-dynamic-range scene can both be displayed simultaneously in one image with high quality after light adjustment. The dynamic range of the system is theoretically 209 dB, which meets the requirements of high-dynamic-range observation.

Explore related subjects

42 ENGINEERINGALGORITHMSCAMERASCORRECTIONSDESIGNEQUIPMENTIMAGESVISIBLE RADIATION

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sun, Honghai, Wang, Yanjie, Yang, Hang, Wu, Pei. 2019-09-15. Binocular high dynamic range imaging system based on digital micromirror device. https://doi.org/10.1007/s11082-019-2014-6

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗