Jinli Suo;Weihang Zhang;Jin Gong;Xin Yuan;David J. Brady;Qionghai Dai
{"title":"Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision","authors":"Jinli Suo;Weihang Zhang;Jin Gong;Xin Yuan;David J. Brady;Qionghai Dai","doi":"10.1109/JPROC.2023.3338272","DOIUrl":null,"url":null,"abstract":"Signal capture is at the forefront of perceiving and understanding the environment; thus, imaging plays a pivotal role in mobile vision. Recent unprecedented progress in artificial intelligence (AI) has shown great potential in the development of advanced mobile platforms with new imaging devices. Traditional imaging systems based on the “capturing images first and processing afterward” mechanism cannot meet this explosive demand. On the other hand, computational imaging (CI) systems are designed to capture high-dimensional data in an encoded manner to provide more information for mobile vision systems. Thanks to AI, CI can now be used in real-life systems by integrating deep learning algorithms into the mobile vision platform to achieve a closed loop of intelligent acquisition, processing, and decision-making, thus leading to the next revolution of mobile vision. Starting from the history of mobile vision using digital cameras, this work first introduces the advancement of CI in diverse applications and then conducts a comprehensive review of current research topics combining CI and AI. Although new-generation mobile platforms, represented by smart mobile phones, have deeply integrated CI and AI for better image acquisition and processing, most mobile vision platforms, such as self-driving cars and drones only loosely connect CI and AI, and are calling for a closer integration. Motivated by this fact, at the end of this work, we propose some potential technologies and disciplines that aid the deep integration of CI and AI and shed light on new directions in the future generation of mobile vision platforms.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"111 12","pages":"1607-1639"},"PeriodicalIF":23.2000,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the IEEE","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10355958/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Signal capture is at the forefront of perceiving and understanding the environment; thus, imaging plays a pivotal role in mobile vision. Recent unprecedented progress in artificial intelligence (AI) has shown great potential in the development of advanced mobile platforms with new imaging devices. Traditional imaging systems based on the “capturing images first and processing afterward” mechanism cannot meet this explosive demand. On the other hand, computational imaging (CI) systems are designed to capture high-dimensional data in an encoded manner to provide more information for mobile vision systems. Thanks to AI, CI can now be used in real-life systems by integrating deep learning algorithms into the mobile vision platform to achieve a closed loop of intelligent acquisition, processing, and decision-making, thus leading to the next revolution of mobile vision. Starting from the history of mobile vision using digital cameras, this work first introduces the advancement of CI in diverse applications and then conducts a comprehensive review of current research topics combining CI and AI. Although new-generation mobile platforms, represented by smart mobile phones, have deeply integrated CI and AI for better image acquisition and processing, most mobile vision platforms, such as self-driving cars and drones only loosely connect CI and AI, and are calling for a closer integration. Motivated by this fact, at the end of this work, we propose some potential technologies and disciplines that aid the deep integration of CI and AI and shed light on new directions in the future generation of mobile vision platforms.
信号捕捉是感知和理解环境的最前沿;因此,成像在移动视觉中起着举足轻重的作用。最近,人工智能(AI)取得了前所未有的进展,这为开发配备新型成像设备的先进移动平台提供了巨大的潜力。基于 "先捕捉图像、后处理 "机制的传统成像系统无法满足这一爆炸性需求。另一方面,计算成像(CI)系统旨在以编码方式捕捉高维数据,为移动视觉系统提供更多信息。得益于人工智能的发展,CI 现在可以通过将深度学习算法集成到移动视觉平台中,实现智能采集、处理和决策的闭环,从而应用于现实系统中,从而引发移动视觉的下一次革命。本著作从使用数码相机的移动视觉的历史出发,首先介绍了 CI 在各种应用中的进展,然后对当前 CI 与 AI 结合的研究课题进行了全面回顾。尽管以智能手机为代表的新一代移动平台已将 CI 与 AI 深度结合,以实现更好的图像采集和处理,但大多数移动视觉平台(如自动驾驶汽车和无人机)只是将 CI 与 AI 松散地联系在一起,因此需要更紧密的结合。在这一事实的推动下,我们在本作品的最后提出了一些有助于 CI 和 AI 深度融合的潜在技术和学科,并阐明了未来新一代移动视觉平台的新方向。
期刊介绍:
Proceedings of the IEEE is the leading journal to provide in-depth review, survey, and tutorial coverage of the technical developments in electronics, electrical and computer engineering, and computer science. Consistently ranked as one of the top journals by Impact Factor, Article Influence Score and more, the journal serves as a trusted resource for engineers around the world.