Imaging Sensors and Systems最新文献_第5页

Planar microlenses for near infrared CMOS image sensors 近红外CMOS图像传感器用平面微透镜

Imaging Sensors and Systems Pub Date : 2020-01-26 DOI: 10.2352/issn.2470-1173.2020.7.iss-144

L. Dilhan, J. Vaillant, A. Ostrovsky, L. Masarotto, C. Pichard, R. Paquet

引用次数: 7

TunnelCAM- A HDR Spherical Camera Array for Structural Integrity Assessments of Dam Interiors TunnelCAM-用于大坝内部结构完整性评估的HDR球面相机阵列

Imaging Sensors and Systems Pub Date : 2020-01-26 DOI: 10.2352/issn.2470-1173.2020.7.iss-227

D. Meyer, E. Lo, Jonathan Klingspon, Anton Netchaev, Charles Ellison, F. Kuester

{"title":"TunnelCAM- A HDR Spherical Camera Array for Structural Integrity Assessments of Dam Interiors","authors":"D. Meyer, E. Lo, Jonathan Klingspon, Anton Netchaev, Charles Ellison, F. Kuester","doi":"10.2352/issn.2470-1173.2020.7.iss-227","DOIUrl":"https://doi.org/10.2352/issn.2470-1173.2020.7.iss-227","url":null,"abstract":"\u0000 The United States of America has an estimate of 84,000 dams of which approximately 15,500 are rated as high-risk as of 2016. Recurrent geological and structural health changes require dam assets to be subject to continuous structural monitoring, assessment and restoration. The objective\u0000 of the developed system is targeted at evaluating the feasibility for standardization in remote, digital inspections of the outflow works of such assets to replace human visual inspections.\u0000 \u0000 This work proposes both a mobile inspection platform and an image processing pipeline to reconstruct\u0000 3D models of the outflow tunnel and gates of dams for structural defect identification. We begin by presenting the imaging system with consideration to lighting conditions and acquisition strategies. We then propose and formulate global optimization constraints that optimize system poses and\u0000 geometric estimates of the environment. Following that, we present a RANSAC frame-work that fits geometric cylinder primitives for texture projection and geometric deviation, as well as an interactive annotation frame-work for 3D anomaly marking. Results of the system and processing are demonstrated\u0000 at the Blue Mountain Dam, Arkansas and the F.E. Walter Dam, Pennsylvania.\u0000","PeriodicalId":121190,"journal":{"name":"Imaging Sensors and Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125500550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Deep Image Demosaicing for Submicron Image Sensors 亚微米图像传感器的深度图像去马赛克

Imaging Sensors and Systems Pub Date : 2019-11-01 DOI: 10.2352/j.imagingsci.technol.2019.63.6.060410

I. Kim, Seongwook Song, Soonkeun Chang, Sukhwan Lim, Kai Guo

{"title":"Deep Image Demosaicing for Submicron Image Sensors","authors":"I. Kim, Seongwook Song, Soonkeun Chang, Sukhwan Lim, Kai Guo","doi":"10.2352/j.imagingsci.technol.2019.63.6.060410","DOIUrl":"https://doi.org/10.2352/j.imagingsci.technol.2019.63.6.060410","url":null,"abstract":"Abstract Latest trend in image sensor technology allowing submicron pixel size for high-end mobile devices comes at very high image resolutions and with irregularly sampled Quad Bayer color filter array (CFA). Sustaining image quality becomes a challenge for the image signal\u0000 processor (ISP), namely for demosaicing. Inspired by the success of deep learning approach to standard Bayer demosaicing, we aim to investigate how artifacts-prone Quad Bayer array can benefit from it. We found that deeper networks are capable to improve image quality and reduce artifacts;\u0000 however, deeper networks can be hardly deployed on mobile devices given very high image resolutions: 24MP, 36MP, 48MP. In this article, we propose an efficient end-to-end solution to bridge this gap—a duplex pyramid network (DPN). Deep hierarchical structure, residual learning, and linear\u0000 feature map depth growth allow very large receptive field, yielding better details restoration and artifacts reduction, while staying computationally efficient. Experiments show that the proposed network outperforms state of the art for standard and Quad Bayer demosaicing. For the challenging\u0000 Quad Bayer CFA, the proposed method reduces visual artifacts better than state-of-the-art deep networks including artifacts existing in conventional commercial solutions. While superior in image quality, it is 2‐25 times faster than state-of-the-art deep neural networks and therefore\u0000 feasible for deployment on mobile devices, paving the way for a new era of on-device deep ISPs.","PeriodicalId":121190,"journal":{"name":"Imaging Sensors and Systems","volume":"195 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125090989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

Using images of partially visible chart for multi-camera system calibration 利用部分可见海图图像进行多相机系统标定

Imaging Sensors and Systems Pub Date : 1900-01-01 DOI: 10.2352/EI.2022.34.7.ISS-231

Radka Tezaur, G. Ali, Oscar Nestares

引用次数: 0

World's first 16: 4: 1 triple conversion gain sensor with all-pixel AF for 82.4dB single exposure HDR 世界上第一个16:4:1三重转换增益传感器，具有82.4dB单曝光HDR的全像素自动对焦

Imaging Sensors and Systems Pub Date : 1900-01-01 DOI: 10.2352/EI.2022.34.7.ISS-257

Changhyun Park, Ho-gyun Lee, E. Shim, Jungbin Yun, Kyungho Lee, Y. Jung, Sukki Yoon, Ilyun Jeong, JungChak Ahn, Duckhyun Chang

引用次数: 0

Accurate event simulation using high-speed video 使用高速视频进行准确的事件模拟

Imaging Sensors and Systems Pub Date : 1900-01-01 DOI: 10.2352/EI.2022.34.7.ISS-242

Xiaozheng Mou, K. Feng, Alex Yi, Steve Wang, Huan Chen, Xiaoqin Hu, M. Guo, Shoushun Chen, Andreas Suess

引用次数: 1

Design and analysis on low-power and low-noise single slope ADC for digital pixel sensors 数字像素传感器低功耗低噪声单斜率ADC的设计与分析

Imaging Sensors and Systems Pub Date : 1900-01-01 DOI: 10.2352/EI.2022.34.7.ISS-256

Hyun-Yong Jung, Myonglae Chu, M. Seo, Suksan Kim, Jiyoun Song, Sang-Gwon Lee, Sung-Jae Byun, Minkyung Kim, Daehee Bae, Junan Lee, Sung-Yong Kim, Jongyeon Lee, Jonghyun Go, Jaekyu Lee, Changrok Moon, Hyoung-Sub Kim

引用次数: 1

The study and analysis of using CMY color filter arrays for 0.8 um CMOS image sensors CMY彩色滤光片阵列用于0.8 um CMOS图像传感器的研究与分析

Imaging Sensors and Systems Pub Date : 1900-01-01 DOI: 10.2352/EI.2022.34.7.ISS-201

Po-Hsiang Wang, An-Li Kuo, Ta-Yung Ni, Hao-Wei Liu, Yu-Chi Chang, Ching-Chiang Wu, Ken Wu

引用次数: 1

DePhaseNet: A deep convolutional network using phase differentiated layers and frequency based custom loss for RGBW image sensor demosaicing DePhaseNet:一种使用相位差分层和基于频率的自定义损失的深度卷积网络，用于RGBW图像传感器去马赛克

Imaging Sensors and Systems Pub Date : 1900-01-01 DOI: 10.2352/EI.2022.34.7.ISS-200

I. Kim, Youngil Seo, Dongpan Lim, Jeongguk Lee, Wooseok Choi, S. Song

引用次数: 1

ESP32-CAM as a programmable camera research platform ESP32-CAM作为可编程摄像机的研究平台

Imaging Sensors and Systems Pub Date : 1900-01-01 DOI: 10.2352/EI.2022.34.7.ISS-232

H. Dietz, Dillon Abney, P. Eberhart, Nick Santini, W. Davis, Elisabeth Wilson, M. McKenzie

引用次数: 2