High-Resolution, Lightweight Remote Sensing via Harmonic Diffractive Optical Imaging Systems and Deep Denoiser Prior Image Restoration

被引:3
作者
Zhong, Shuo [1 ,2 ,3 ]
Zhao, Xijun [2 ]
Liu, Dun [2 ]
Su, Haibing [2 ]
Xie, Zongliang [2 ]
Fan, Bin [2 ]
机构
[1] Chinese Acad Sci, Natl Key Lab Opt Field Manipulat Sci & Technol, Chengdu 610209, Sichuan, Peoples R China
[2] Chinese Acad Sci, Inst Opt & Elect, Chengdu 610209, Sichuan, Peoples R China
[3] Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China
来源
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2024年 / 62卷
基金
中国国家自然科学基金;
关键词
Optical imaging; Image restoration; Optical diffraction; Optical sensors; Imaging; Optical refraction; Remote sensing; Depth denoiser; harmonic diffractive optical imaging systems; image restoration; remote sensing; REGULARIZATION; DESIGN;
D O I
10.1109/TGRS.2024.3394154
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
The weight of traditional space optical imaging systems often increases nearly cubically with the increase in aperture size. To overcome this limitation, this study proposes the use of a lightweight harmonic diffractive optical imaging system combined with deep denoiser prior image restoration, to achieve high-resolution, lightweight remote sensing. This research first designed a novel 150-order harmonic diffractive optical element (H-DOE) featuring a 40-mm aperture size and a 320-mm focal length, which is equipped with seven annular zones covering a broad spectral band (500-800 nm). Its slim structure significantly reduces weight and volume, thereby lowering its launch costs. Furthermore, to address the blurring issues encountered in H-DOE imaging tasks and attain enhanced image resolution, this study incorporates an advanced image restoration technique. This technique employs a deep denoiser as a prior module, which is embedded into a model-based image restoration optimization framework. The newly trained deep denoiser utilizes a U-Net architecture integrating a transformer and residual structures and is adept at handling complex noise during the optical imaging process. The experimental results demonstrate that the performance of the proposed image restoration strategy based on a deep denoiser surpasses that of the existing technologies, elevating the resolvable frequency of the modulation transfer function (MTF) of an H-DOE imaging system from 40.58 to 98.55 lp/mm, an enhancement of 142.9%. This significant image quality improvement showcases its vast potential for use in future high-resolution, lightweight remote sensing applications.
引用
收藏
页码:1 / 17
页数:17
相关论文
共 70 条
[1]   NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study [J].
Agustsson, Eirikur ;
Timofte, Radu .
2017 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW), 2017, :1122-1131
[2]   FalconSAT-7: A membrane space solar telescope [J].
Andersen, Geoff ;
Asmolova, Olha ;
McHarg, Matthew G. ;
Quiller, Trey ;
Maldonado, Carlos .
SPACE TELESCOPES AND INSTRUMENTATION 2016: OPTICAL, INFRARED, AND MILLIMETER WAVE, 2016, 9904
[3]  
Atcheson P.D., 2012, P SPAC TEL INSTR 201, P729
[4]   MOIRE - Ground Demonstration of a Large Aperture Diffractive Transmissive Telescope [J].
Atcheson, Paul ;
Domber, Jeanette ;
Whiteaker, Kevin ;
Britten, Jerald A. ;
Dixit, Shamasundar N. ;
Farmer, Brandon .
SPACE TELESCOPES AND INSTRUMENTATION 2014: OPTICAL, INFRARED, AND MILLIMETER WAVE, 2014, 9143
[5]  
Ba J L., LAYER NORMALIZATION
[6]   Very High-Resolution Remote Sensing: Challenges and Opportunities [J].
Benediktsson, Jon Atli ;
Chanussot, Jocelyn ;
Moon, Wooil M. .
PROCEEDINGS OF THE IEEE, 2012, 100 (06) :1907-1910
[7]   Plug-and-Play ADMM for Image Restoration: Fixed-Point Convergence and Applications [J].
Chan, Stanley H. ;
Wang, Xiran ;
Elgendy, Omar A. .
IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING, 2017, 3 (01) :84-98
[8]   Remote Sensing Image Change Detection With Transformers [J].
Chen, Hao ;
Qi, Zipeng ;
Shi, Zhenwei .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60
[9]   Enhanced Sparse Model for Blind Deblurring [J].
Chen, Liang ;
Fang, Faming ;
Lei, Shen ;
Li, Fang ;
Zhang, Guixu .
COMPUTER VISION - ECCV 2020, PT XXV, 2020, 12370 :631-646
[10]   Blind Image Deblurring with Local Maximum Gradient Prior [J].
Chen, Liang ;
Fang, Faming ;
Wang, Tingting ;
Zhang, Guixu .
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), 2019, :1742-1750