Model-Based Multiscale Gigapixel Image Formation Pipeline on GPU

被引:1
|
作者
Gong, Qian [1 ]
Vera, Esteban [2 ]
Golish, Dathon R. [4 ]
Feller, Steven D. [3 ]
Brady, David J. [1 ]
Gehm, Michael E. [1 ]
机构
[1] Duke Univ, Dept Elect & Comp Engn, Durham, NC 27708 USA
[2] Pontificia Univ Catolica Valparaiso, Escuela Ingn Elect, Valparaiso 2362804, Chile
[3] Aqueti Inc, Durham, NC 27707 USA
[4] Univ Arizona, Lunar & Planetary Lab, Tucson, AZ 85721 USA
关键词
Gigapixel imaging; image formation; MapReduce; GPU;
D O I
10.1109/TCI.2016.2612942
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
We present an efficient and flexible GPU implementation of a highly-parallelizable and scalable image formation pipeline for gigapixel images based on the MapReduce framework. The presented implementation was developed to operate with the AWARE multiscale gigapixel cameras, but it is also able to efficiently form gigapixel images from any source. The AWARE cameras are compact camera arrays that simultaneously collect images that span a wide field-of-view to generate high-resolution and high dynamic range panoramic images and video. The proposed GPU implementation exploits the mutiscale nature of the AWARE image acquisition, not only enabling the fast composition of gigapixel-scale panoramas, but also the rapid formation of images of arbitrary portions of the field-of-view at current display-scale resolutions at video rates.
引用
收藏
页码:493 / 502
页数:10
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