.png){kind=icon}
返回
CoCoCs: co-optimized compressive imaging driven by high-level vision
.png){kind=icon}
OA
AI
DOI：10.1364/OE.468733.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Compressive imaging senses optically encoded high-dimensional scene data with far fewer measurements and then performs reconstruction via appropriate algorithms. In this paper, we present a novel noniterative end-to-end deep learning-based framework for compressive imaging, dubbed CoCoCs. In comparison to existing approaches, we extend the pipeline by co-optimizing the recovery algorithm with optical coding as well as cascaded high-level computer vision tasks to boost the quality of the reconstruction. We demonstrate the proposed framework on two typical compressive imaging systems, i.e., single pixel imaging and snapshot video compressive imaging. Extensive results, including conventional image quality criteria, mean opinion scores, and accuracy in image classification and motion recognition, confirm that CoCoCs can yield realistic images and videos, which are friendly to both human viewing and computer vision. We hope CoCoCs will give impetus to bridge the gap between compressive imagers and computer vision and the perception of human. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
Keyword:
QUALITY ASSESSMENT
ALGORITHMS
CAMERA
期刊
IF:
3.3
论文数:
6.1W
被引数:
14.3W
机构
引用论文
分享收藏Single-pixel imaging by means of Fourier spectrum acquisition傅里叶光谱采集的单像素成像.png)
NATURE COMMUNICATIONS
OAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏Video object detection from one single image through opto-electronic neural networkAPL PHOTONICSOAAI
APL PHOTONICSIF5.3
OAAI分享收藏Mean opinion score (MOS) revisited: methods and applications, limitations and alternativesMULTIMEDIA SYSTEMS
MULTIMEDIA SYSTEMSIF3.1
PREAI
分享收藏分享收藏
查看更多内容