Split-Aperture 2-in-1 Computational Cameras

Author:

Shi Zheng1ORCID,Chugunov Ilya1ORCID,Bijelic Mario1ORCID,Côté Geoffroi1ORCID,Yeom Jiwoon1ORCID,Fu Qiang2ORCID,Amata Hadi2ORCID,Heidrich Wolfgang2ORCID,Heide Felix1ORCID

Affiliation:

1. Princeton University, Princeton, United States of America

2. King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia

Abstract

While conventional cameras offer versatility for applications ranging from amateur photography to autonomous driving, computational cameras allow for domain-specific adaption. Cameras with co-designed optics and image processing algorithms enable high-dynamic-range image recovery, depth estimation, and hyperspectral imaging through optically encoding scene information that is otherwise undetected by conventional cameras. However, this optical encoding creates a challenging inverse reconstruction problem for conventional image recovery, and often lowers the overall photographic quality. Thus computational cameras with domain-specific optics have only been adopted in a few specialized applications where the captured information cannot be acquired in other ways. In this work, we investigate a method that combines two optical systems into one to tackle this challenge. We split the aperture of a conventional camera into two halves: one which applies an application-specific modulation to the incident light via a diffractive optical element to produce a coded image capture, and one which applies no modulation to produce a conventional image capture. Co-designing the phase modulation of the split aperture with a dual-pixel sensor allows us to simultaneously capture these coded and uncoded images without increasing physical or computational footprint. With an uncoded conventional image alongside the optically coded image in hand, we investigate image reconstruction methods that are conditioned on the conventional image, making it possible to eliminate artifacts and compute costs that existing methods struggle with. We assess the proposed method with 2-in-1 cameras for optical high-dynamic-range reconstruction, monocular depth estimation, and hyperspectral imaging, comparing favorably to all tested methods in all applications.

Publisher

Association for Computing Machinery (ACM)

Reference62 articles.

1. Defocus Deblurring Using Dual-Pixel Data

2. Abdullah Abuolaim, Radu Timofte, and Michael S Brown. 2021. NTIRE 2021 challenge for defocus deblurring using dual-pixel images: Methods and results. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 578--587.

3. Split Aperture Imaging for High Dynamic Range

4. Sparse Recovery of Hyperspectral Signal from Natural RGB Images

5. NTIRE 2020 Challenge on Spectral Reconstruction from an RGB Image

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3