Special Topic: Deep Space Optoelectronic Measurement and Intelligent Awareness Technology

A review of airborne multi-aperture panoramic image compositing

  • Fujie WU ,
  • Bowen WANG ,
  • Jingya QI ,
  • Mingzhi CAO ,
  • Yingjun SANG ,
  • Sheng LI ,
  • Yuzhen ZHANG ,
  • Qian CHEN ,
  • Chao ZUO
Expand
  • 1.Smart Computational Imaging Laboratory,School of Electronic and Optical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
    2.Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense,Nanjing 210094,China
    3.Space Optoelectronic Measurement and Perception Lab,Beijing Institute of Control Engineering,Beijing 100190,China

Received date: 2024-04-10

  Revised date: 2024-06-21

  Accepted date: 2024-07-01

  Online published: 2024-07-12

Supported by

National Natural Science Foundation of China(U21B2033);Optoelectronic Measurement and Intelligent Perception Zhongguancun Open Lab., and Space Optoelectronic Measurement and Perception Lab., Beijing Institute of Control Engineering(LabSOMP-2022-05);National Key Research and Development Program of China(2022YFA1205002);Leading Technology of Jiangsu Basic Research Plan(BK20192003);Key National Industrial Technology Cooperation Foundation of Jiangsu Province(BZ2022039)

Abstract

The airborne multi-aperture panoramic image compositing technology generates panoramic images with high resolution and rich details by stitching images from multiple sub-aperture or sensors, and plays an important role in many key fields such as national defense and security, agriculture and forestry, and digital surveillance. This paper introduces the development background of airborne multi-aperture panoramic image compositing technology, expounds the basic concepts and steps of panoramic image compositing, sorts out the classification and development of its core technologies--image registration technology and image fusion technology, and summarizes the characteristics and limitations of current mainstream methods. Finally, based on an analysis of the current development status of airborne multi-aperture panoramic image compositing technology, the bottleneck problems of the technology are revealed. The future research directions and possible technical ways to solve these problems are also proposed to provide useful enlightenment for the technological progress and application expansion in this field.

Cite this article

Fujie WU , Bowen WANG , Jingya QI , Mingzhi CAO , Yingjun SANG , Sheng LI , Yuzhen ZHANG , Qian CHEN , Chao ZUO . A review of airborne multi-aperture panoramic image compositing[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2025 , 46(3) : 630505 -630505 . DOI: 10.7527/S1000-6893.2024.30505

References

1 李晟, 王博文, 管海涛, 等. 远场合成孔径计算光学成像技术: 文献综述与最新进展[J]. 光电工程202350(10): 230090.
  LI S, WANG B W, GUAN H T, et al. Far-field computational optical imaging techniques based on synthetic aperture: a review[J]. Opto-Electronic Engineering202350(10): 230090 (in Chinese).
2 左超, 陈钱. 分辨率、超分辨率与空间带宽积拓展: 从计算光学成像角度的一些思考[J]. 中国光学(中英文)202215(6): 1105-1166.
  ZUO C, CHEN Q. Resolution, super-resolution and spatial bandwidth product expansion: Some thoughts from the perspective of computational optical imaging[J]. Chinese Optics202215(6): 1105-1166 (in Chinese).
3 左超, 陈钱. 计算光学成像: 何来,何处,何去,何从?[J]. 红外与激光工程202251(2): 3788/IRLA20220110.
  ZUO C, CHEN Q. Computational optical imaging: An overview[J]. Infrared and Laser Engineering202251(2): 3788/IRLA20220110 (in Chinese).
4 LI Z Q, ISLER V. Large scale image mosaic construction for agricultural applications[J]. IEEE Robotics and Automation Letters20161(1): 295-302.
5 GUI Z C, LI H F. Automated defect detection and visualization for the robotic airport runway inspection[J]. IEEE Access20208: 76100-76107.
6 DAVID JENKINS M, BUGGY T, MORISON G. An imaging system for visual inspection and structural condition monitoring of railway tunnels[C]∥2017 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS). Piscataway: IEEE Press, 2017: 1-6.
7 LI M, LI D, FAN D. A study on automatic UAV image mosaic method for paroxysmal disaster[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences2012, XXXIX-B6: 123-128.
8 IVEZI? ?, CONNOLLY A J, JURI? M. Everything we’d like to do with LSST data, but we don’t know (yet) how[J]. Proceedings of the International Astronomical Union201612(S325): 93-102.
9 NEILL D, ANGELI G, CLAVER C, et al. Overview of the LSST active optics system[J]. Proceedings of SPIE20149150: 143-158.
10 YUAN X Y, FANG L, DAI Q H, et al. Multiscale gigapixel video: A cross resolution image matching and warping approach[C]∥2017 IEEE International Conference on Computational Photography (ICCP). Piscataway: IEEE Press, 2017: 1-9.
11 YUAN X Y, JI M Q, WU J M, et al. A modular hierarchical array camera[J]. Light, Science & Applications202110(1): 37.
12 LLULL P, BANGE L, PHILLIPS Z, et al. Characterization of the AWARE 40 wide-field-of-view visible imager[J]. Optica20152(12): 1086.
13 刘飞, 刘佳维, 邵晓鹏. 高集成度小型化共心多尺度光学系统设计[J]. 光学 精密工程202028(6): 1275-1282.
  LIU F, LIU J W, SHAO X P. Design of high integration and miniaturization concentric multiscale optical system[J]. Optics and Precision Engineering202028(6): 1275-1282 (in Chinese).
14 方璐, 戴琼海. 计算光场成像[J]. 光学学报202040(1): 0111001.
  FANG L, DAI Q H. Computational light field imaging[J]. Acta Optica Sinica202040(1): 0111001 (in Chinese).
15 邵晓鹏, 刘飞, 李伟, 等. 计算成像技术及应用最新进展[J]. 激光与光电子学进展202057(2): 11-55.
  SHAO X P, LIU F, LI W, et al. Latest progress in computational imaging technology and application[J]. Laser & Optoelectronics Progress202057(2): 11-55 (in Chinese).
16 周亮, 刘凯, 刘朝晖, 等. 光学复用大视场成像研究[J]. 光子学报202049(9): 132-138.
  ZHOU L, LIU K, LIU Z H, et al. Optically multiplexed imaging for increased field of view[J]. Acta Photonica Sinica202049(9): 132-138 (in Chinese).
17 WANG B W, LI S, CHEN Q, et al. Learning-based single-shot long-range synthetic aperture Fourier ptychographic imaging with a camera array[J]. Optics Letters202348(2): 263-266.
18 YANG F, WU J C, GAO Y H, et al. A four-aperture super-resolution camera based on adaptive regularization parameter tuning[J]. Optics and Lasers in Engineering2023165: 107562.
19 HOLLOWAY J, ASIF M S, SHARMA M K, et al. Toward long-distance subdiffraction imaging using coherent camera arrays[J]. IEEE Transactions on Computational Imaging20162(3): 251-265.
20 李树涛, 李聪妤, 康旭东. 多源遥感图像融合发展现状与未来展望[J]. 遥感学报25(1): 148-166.
  LI S T, LI C Y, KANG X D. Development status and future prospects of multi-source remote sensing image fusion[J]. National Remote Sensing Bulletin25(1): 148-166 (in Chinese).
21 李赛, 尹球, 胡勇, 等. 基于SPHP的推扫式高光谱航空影像拼接[J]. 红外与毫米波学报202140(1): 64-73.
  LI S, YIN Q, HU Y, et al. A push-sweep hyperspectral aerial image Mosaic method based on SPHP[J]. Journal of Infrared and Millimeter Waves202140(1): 64-73 (in Chinese).
22 李俊杰, 姜涛, 傅俏燕. “高分一号” 卫星多光谱宽幅相机影像合成[J]. 航天返回与遥感202041(5): 95-101.
  LI J J, JIANG T, FU Q Y. Cloud-free image composite of GF-1 wide field of view camera[J]. Spacecraft Recovery & Remote Sensing202041(5): 95-101 (in Chinese).
23 易俐娜, 许筱, 张桂峰, 等. 轻小型无人机高光谱影像拼接研究[J]. 光谱学与光谱分析201939(6): 1885-1891.
  YI L N, XU X, ZHANG G F, et al. Light and small UAV hyperspectral image mosaicking[J]. Spectroscopy and Spectral Analysis201939(6): 1885-1891 (in Chinese).
24 WANG B W, ZOU Y, ZHANG L F, et al. Multimodal super-resolution reconstruction of infrared and visible images via deep learning[J]. Optics and Lasers in Engineering2022156: 107078.
25 CHANDEL R, GUPTA G. Image filtering algorithms and techniques: A review[J]. International Journal of Advanced Research in Computer Science and Software Engineering20133(10): 1-10.
26 BANG S, KIM H, KIM H. UAV-based automatic generation of high-resolution panorama at a construction site with a focus on preprocessing for image stitching[J]. Automation in Construction201784: 70-80.
27 VAIDYA O S, GANDHE S T. The study of preprocessing and postprocessing techniques of image stitching[C]∥2018 International Conference on Advances in Communication and Computing Technology (ICACCT). Piscataway: IEEE Press, 2018: 431-435.
28 周前飞, 刘晶红, 居波, 等. 面阵CCD航空相机斜视图像的几何校正[J]. 液晶与显示201530(3): 505-513.
  ZHOU Q F, LIU J H, JU B, et al. Geometric correction of oblique images for array CCD aerial cameras[J]. Chinese Journal of Liquid Crystals and Displays201530(3): 505-513 (in Chinese).
29 LIU B, XIAO Q, ZHANG Y H, et al. Intelligent recognition method of low-altitude squint optical ship target fused with simulation samples[J]. Remote Sensing202113(14): 2697.
30 BROWN L G. A survey of image registration techniques[J]. ACM Computing Surveys199224(4): 325-376.
31 ZITOVá B, FLUSSER J. Image registration methods: A survey[J]. Image and Vision Computing200321(11): 977-1000.
32 WANG Z J, ZIOU D, ARMENAKIS C, et al. A comparative analysis of image fusion methods[J]. IEEE Transactions on Geoscience and Remote Sensing200543(6): 1391-1402.
33 KAUR H, KOUNDAL D, KADYAN V. Image fusion techniques: A survey[J]. Archives of Computational Methods in Engineering202128(7): 4425-4447.
34 XIONG Z, ZHANG Y. A critical review of image registration methods[J]. International Journal of Image and Data Fusion20101(2): 137-158.
35 RAO Y R, PRATHAPANI N, NAGABHOOSHANAM E. Application of normalized cross correlation to image registration[J]. International Journal of Research in Engineering and Technology20143(5): 12-16.
36 KYBIC J, UNSER M. Fast parametric elastic image registration[J]. IEEE Transactions on Image Processing200312(11): 1427-1442.
37 BARNEA D I, SILVERMAN H F. A class of algorithms for fast digital image registration[J]. IEEE Transactions on Computers1972, C-21(2): 179-186.
38 KUGLIN C D, HINES D C. The phase correlation image alignment method[C]∥IEEE International Conference on Cybernetics and Society, 1975: 163-165.
39 DE CASTRO E, MORANDI C. Registration of translated and rotated images using finite Fourier transforms[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence1987, PAMI-9(5): 700-703.
40 杨必武, 郭晓松, 赵敬民, 等. 基于小波变换的视差图像全局几何配准新算法[J]. 光子学报200736(3): 574-576.
  YANG B W, GUO X S, ZHAO J M, et al. An algorithm on geometric global registration of parallax images based on wavelet transform[J]. Acta Photonica Sinica200736(3): 574-576 (in Chinese).
41 李培, 姜刚, 马千里, 等. 结合张量与互信息的混合模型多模态图像配准方法[J]. 测绘学报202150(7): 916-929.
  LI P, JIANG G, MA Q L, et al. A hybrid model combining tensor and mutual information for multi-modal image registration[J]. Acta Geodaetica et Cartographica Sinica202150(7): 916-929 (in Chinese).
42 凌志刚, 潘泉, 程咏梅, 等. 一种结合梯度方向互信息和多分辨混合优化的多模图像配准方法[J]. 光子学报201039(8): 1359-1366.
  LING Z G, PAN Q, CHENG Y M, et al. A multimodal image registration method combining gradient orientation mutual information with multi-resolution hybrid optimization algorithm[J]. Acta Photonica Sinica201039(8): 1359-1366 (in Chinese).
43 吴泽鹏, 郭玲玲, 朱明超, 等. 结合图像信息熵和特征点的图像配准方法[J]. 红外与激光工程201342(10): 2846-2852.
  WU Z P, GUO L L, ZHU M C, et al. Improved image registration using feature points combined with image entropy[J]. Infrared and Laser Engineering201342(10): 2846-2852 (in Chinese).
44 BAJCSY R, KOVA?I? S. Multiresolution elastic matching[J]. Computer Vision, Graphics, and Image Processing198946(1): 1-21.
45 CHRISTENSEN G E, RABBITT R D, MILLER M I. Deformable templates using large deformation kinematics[J]. IEEE Transactions on Image Processing19965(10): 1435-1447.
46 BEAUCHEMIN S S, BARRON J L. The computation of optical flow[J]. ACM Computing Surveys199527(3): 433-466.
47 MORAVEC H P. Obstacle avoidance and navigation in the real world by a seeing robot rover[D]. Stanford: Stanford University, 1980.
48 HARRIS C, STEPHENS M. A combined corner and edge detector[C]∥Proceedings of the Alvey Vision Conference, 1988.
49 SHI J B, TOMASI C. Good features to track[C]∥1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2002: 593-600.
50 SMITH S M, BRADY J M. SUSAN: A new approach to low level image processing[J]. International Journal of Computer Vision199723(1): 45-78.
51 LOWE D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision200460(2): 91-110.
52 BAY H, TUYTELAARS T, VAN GOOL L. SURF: Speeded up robust features[M]∥Computer Vision- ECCV 2006. Berlin, Heidelberg: Springer, 2006: 404-417.
53 RUBLEE E, RABAUD V, KONOLIGE K, et al. ORB: An efficient alternative to SIFT or SURF[C]∥2011 International Conference on Computer Vision. Piscataway: IEEE Press, 2011: 2564-2571.
54 ROSTEN E, DRUMMOND T. Machine learning for high-speed corner detection[M]∥Computer Vision- ECCV 2006. Berlin, Heidelberg: Springer, 2006: 430-443.
55 CALONDER M, LEPETIT V, STRECHA C, et al. BRIEF: Binary robust independent elementary features[M]∥Computer Vision-ECCV 2010. Berlin, Heidelberg: Springer, 2010: 778-792.
56 ZARAGOZA J, CHIN T J, BROWN M S, et al. As-projective-as-possible image stitching with moving DLT[C]∥2013 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2013: 2339-2346.
57 CHANG C H, SATO Y, CHUANG Y Y. Shape-preserving half-projective warps for image stitching[C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2014: 3254-3261.
58 LIN C C, PANKANTI S U, RAMAMURTHY K N, et al. Adaptive as-natural-as-possible image stitching[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2015: 1155-1163.
59 CHEN Y-S, CHUANG Y Y. Natural image stitching with the global similarity prior[M]∥Computer Vision-ECCV 2016. Cham: Springer International Publishing, 2016: 186-201.
60 SUN L, WANG S Q, XING J C. An improved Harris corner detection algorithm for low contrast image[C]∥The 26th Chinese Control and Decision Conference (2014 CCDC). Piscataway: IEEE Press, 2014: 3039-3043.
61 HAN C, YOU F C, WANG S M. An improved Harris corner detection algorithm based on adaptive gray threshold[C]∥2019 4th International Conference on Automatic Control and Mechatronic Engineering (ACME 2019). 2019: 1-5.
62 CUI J, XIE J B, LIU T, et al. Corners detection on finger vein images using the improved Harris algorithm[J]. Optik2014125(17): 4668-4671.
63 KE Y, SUKTHANKAR R. PCA-SIFT: A more distinctive representation for local image descriptors[C]∥Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2004.
64 MOREL J M, YU G S. ASIFT: A new framework for fully affine invariant image comparison[J]. SIAM Journal on Imaging Sciences20092(2): 438-469.
65 HOSSEIN-NEJAD Z, AGAHI H, MAHMOODZADEH A. Image matching based on the adaptive redundant keypoint elimination method in the SIFT algorithm[J]. Pattern Analysis and Applications202124(2): 669-683.
66 MA W P, WEN Z L, WU Y, et al. Remote sensing image registration with modified SIFT and enhanced feature matching[J]. IEEE Geoscience and Remote Sensing Letters201714(1): 3-7.
67 CHEN Y, SHANG L. Improved SIFT image registration algorithm on characteristic statistical distributions and consistency constraint[J]. Optik2016127(2): 900-911.
68 PATEL M I, THAKAR V K, SHAH S K. Image registration of satellite images with varying illumination level using HOG descriptor based SURF[J]. Procedia Computer Science201693: 382-388.
69 SHENG H Y, WEI S M, YU X L, et al. Research on binocular visual system of robotic arm based on improved SURF algorithm[J]. IEEE Sensors Journal202020(20): 11849-11855.
70 XIE Y G, WANG Q, CHANG Y X, et al. Fast target recognition based on improved ORB feature[J]. Applied Sciences202212(2): 786.
71 SUN H, WANG P, ZHANG D, et al. An improved ORB algorithm based on optimized feature point extraction[C]∥2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE). IEEE, 2020: 389-394.
72 ZHANG Z Y, WANG L X, ZHENG W F, et al. Endoscope image mosaic based on pyramid ORB[J]. Biomedical Signal Processing and Control202271: 103261.
73 ZHANG W N. Combination of SIFT and canny edge detection for registration between SAR and optical images[J]. IEEE Geoscience and Remote Sensing Letters202019: 4007205.
74 LIU W X, CHIN T J. Correspondence insertion for as-projective-as-possible image stitching[DB/OL]. arXiv preprint: 1608.07997, 2016.
75 LI N, XU Y F, WANG C. Quasi-homography warps in image stitching[J]. IEEE Transactions on Multimedia201820(6): 1365-1375.
76 LIAO T L, LI N. Single-perspective warps in natural image stitching[J]. IEEE Transactions on Image Processing2019.
77 JIA Q, LI Z J, FAN X, et al. Leveraging line-point consistence to preserve structures for wide parallax image stitching[C]∥2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2021: 12181-12190.
78 GAO J, LI Y, CHIN T J, et al. Seam-driven image stitching[C]∥Eurographics 2013 (Short Papers). Girona: The Eurographics Association, 2013: 45-48.
79 LIN K M, JIANG N J, CHEONG L F, et al. SEAGULL: Seam-guided local alignment for parallax-tolerant image stitching[M]∥Computer Vision-ECCV 2016. Cham: Springer International Publishing, 2016: 370-385.
80 LI N, LIAO T, WANG C. Perception-based energy functions in seam-cutting[J]. arXiv preprint: 1701.06141, 2017.
81 LIAO T L, CHEN J, XU Y F. Quality evaluation-based iterative seam estimation for image stitching[J]. Signal, Image and Video Processing201913(6): 1199-1206.
82 YUAN Y T, FANG F M, ZHANG G X. Superpixel-based seamless image stitching for UAV images[J]. IEEE Transactions on Geoscience and Remote Sensing202159(2): 1565-1576.
83 CHEN X, YU M, SONG Y. Optimized seam-driven image stitching method based on scene depth information[J]. Electronics202211(12): 1876.
84 LI S T, KANG X D, FANG L Y, et al. Pixel-level image fusion: A survey of the state of the art[J]. Information Fusion201733: 100-112.
85 SUN J G, HAN Q L, KOU L, et al. Multi-focus image fusion algorithm based on Laplacian Pyramids[J]. Journal of the Optical Society of America A, Optics, Image Science, and Vision201835(3): 480-490.
86 AMOLINS K, ZHANG Y, DARE P. Wavelet based image fusion techniques: An introduction, review and comparison[J]. ISPRS Journal of Photogrammetry and Remote Sensing200762(4): 249-263.
87 NENCINI F, GARZELLI A, BARONTI S, et al. Remote sensing image fusion using the curvelet transform[J]. Information Fusion20078(2): 143-156.
88 ZHANG Q, LIU Y, BLUM R S, et al. Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images: A review[J]. Information Fusion201840: 57-75.
89 SAHU D, PARSAI M P. Different image fusion techniques-a critical review[J]. International Journal of Modern Engineering Research (IJMER)20122(5): 4298-4301.
90 YANG Y, WAN W G, HUANG S Y, et al. Remote sensing image fusion based on adaptive IHS and multiscale guided filter[J]. IEEE Access20164: 4573-4582.
91 DESALE R P, VERMA S V. Study and analysis of PCA, DCT & DWT based image fusion techniques[C]∥2013 International Conference on Signal Processing, Image Processing & Pattern Recognition. Piscataway: IEEE Press, 2013: 66-69.
92 MITIANOUDIS N, STATHAKI T. Pixel-based and region-based image fusion schemes using ICA bases[J]. Information Fusion20078(2): 131-142.
93 TANG L, ZHAO Z G. Multiresolution image fusion based on the wavelet-based contourlet transform[C]∥2007 10th International Conference on Information Fusion. Piscataway: IEEE Press, 2007: 1-6.
94 LIU Y, LIU S P, WANG Z F. A general framework for image fusion based on multi-scale transform and sparse representation[J]. Information Fusion201524: 147-164.
95 LIU Y, CHEN X, WARD R K, et al. Medical image fusion via convolutional sparsity based morphological component analysis[J]. IEEE Signal Processing Letters201926(3): 485-489.
96 TU T M, SU S C, SHYU H C, et al. A new look at IHS-like image fusion methods[J]. Information Fusion20012(3): 177-186.
97 RAHMANI S, STRAIT M, MERKURJEV D, et al. An adaptive IHS pan-sharpening method[J]. IEEE Geoscience and Remote Sensing Letters20107(4): 746-750.
98 GHAHREMANI M, GHASSEMIAN H. Nonlinear IHS: A promising method for pan-sharpening[J]. IEEE Geoscience and Remote Sensing Letters201613(11): 1606-1610.
99 BURT P, ADELSON E. The Laplacian pyramid as a compact image code[J]. IEEE Transactions on Communications198331(4): 532-540.
100 TOET A. Image fusion by a ratio of low-pass pyramid[J]. Pattern Recognition Letters19899(4): 245-253.
101 SIMONCELLI E P, FREEMAN W T. The steerable pyramid: A flexible architecture for multi-scale derivative computation[C]∥Proceedings International Conference on Image Processing. Piscataway: IEEE Press, 1995: 444-447.
102 MALLAT S G. A theory for multiresolution signal decomposition: The wavelet representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence198911(7): 674-693.
103 YANG Y. A novel DWT based multi-focus image fusion method[J]. Procedia Engineering201124: 177-181.
104 CAO W, LI B C, ZHANG Y. A remote sensing image fusion method based on PCA transform and wavelet packet transform[C]∥International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003. Piscataway: IEEE Press, 2003: 976-981.
105 SELESNICK I W, BARANIUK R G, KINGSBURY N C. The dual-tree complex wavelet transform[J]. IEEE Signal Processing Magazine200522(6): 123-151.
106 GILLES J. Empirical wavelet transform[J]. IEEE Transactions on Signal Processing201361(16): 3999-4010.
107 CANDèS E J, DONOHO D L. Ridgelets: A key to higher-dimensional intermittency?[J]. Philosophical Transactions of the Royal Society of London Series A: Mathematical, Physical and Engineering Sciences1999357(1760): 2495-2509.
108 STARCK J L, CANDèS E J, DONOHO D L. The curvelet transform for image denoising[J]. IEEE Transactions on Image Processing200211(6): 670-684.
109 DO M N, VETTERLI M. The contourlet transform: an efficient directional multiresolution image representation[J]. IEEE Transactions on Image Processing200514(12): 2091-2106.
110 CUNHA A L DA, ZHOU J, DO M N. The nonsubsampled contourlet transform: theory, design, and applications[J]. IEEE Transactions on Image Processing200615(10): 3089-3101.
111 蒋婷婷, 王敬东, 李鹏. 基于Curvelet变换和小波变换相结合的图像融合算法研究[J]. 光电子技术201030(2): 111-116.
  JIANG T T, WANG J D, LI P. Research on multifocus image fusion algorithm by combining Curvelet transform and wavelet transform[J]. Optoelectronic Technology201030(2): 111-116 (in Chinese).
112 ARIF M, WANG G J. Fast curvelet transform through genetic algorithm for multimodal medical image fusion[J]. Soft Computing202024(3): 1815-1836.
113 ZHANG H, XU H, TIAN X, et al. Image fusion meets deep learning: A survey and perspective[J]. Information Fusion202176: 323-336.
114 KARIM S, TONG G, LI J Y, et al. Current advances and future perspectives of image fusion: A comprehensive review[J]. Information Fusion202390: 185-217.
115 AZARANG A, MANOOCHEHRI H E, KEHTAR?NAVAZ N. Convolutional autoencoder-based multispectral image fusion[J]. IEEE Access20197: 35673-35683.
116 PRABHAKAR K R, SRIKAR V S, BABU R V. DeepFuse: A deep unsupervised approach for exposure fusion with extreme exposure image pairs[C]∥2017 IEEE International Conference on Computer Vision (ICCV). Piscataway: IEEE Press, 2017: 4724-4732.
117 LI H, WU X J. DenseFuse: A fusion approach to infrared and visible images[J]. IEEE Transactions on Image Processing201928(5): 2614-2623.
118 LIU Y, CHEN X, CHENG J, et al. A medical image fusion method based on convolutional neural networks[C]∥2017 20th International Conference on Information Fusion (Fusion). Piscataway: IEEE Press, 2017: 1-7.
119 ZHANG H, XU H, XIAO Y, et al. Rethinking the image fusion: A fast unified image fusion network based on proportional maintenance of gradient and intensity[J]. Proceedings of the AAAI Conference on Artificial Intelligence202034(7): 12797-12804.
120 MA J Y, YU W, CHEN C, et al. Pan-GAN: An unsupervised pan-sharpening method for remote sensing image fusion[J]. Information Fusion202062: 110-120.
121 XU H, MA J Y, ZHANG X P. MEF-GAN: Multi-exposure image fusion via generative adversarial networks[J]. IEEE Transactions on Image Processing202029: 7203-7216.
122 RAO Y J, WU D, HAN M N, et al. AT-GAN: A generative adversarial network with attention and transition for infrared and visible image fusion[J]. Information Fusion202392: 336-349.
123 MA J Y, YU W, LIANG P W, et al. FusionGAN: A generative adversarial network for infrared and visible image fusion[J]. Information Fusion201948: 11-26.
124 MA J Y, XU H, JIANG J J, et al. DDcGAN: A dual-discriminator conditional generative adversarial network for multi-resolution image fusion[J]. IEEE Transactions on Image Processing2020.
125 朱雯青, 张宁, 李争, 等. 基于多任务卷积神经网络的红外与可见光多分辨率图像融合[J]. 光谱学与光谱分析202343(1): 289-296.
  ZHU W Q, ZHANG N, LI Z, et al. A multi-task convolutional neural network for infrared and visible multi-resolution image fusion[J]. Spectroscopy and Spectral Analysis202343(1): 289-296 (in Chinese).
126 WANG J X, XI X L, LI D M, et al. GRPAFusion: A gradient residual and pyramid attention-based multiscale network for multimodal image fusion[J]. Entropy202325(1): 169.
127 LIU D, YANG F B, WEI H,et al. Remote sensing image fusion method based on discrete wavelet and multiscale morphological transform in the IHS color space[J]. Journal of Applied Remote Sensing202014(1): 016518.
128 SHARMA M. A review : Image fusion techniques and applications[J]. International Journal of Computer Science and Information Technologies20167(3): 1082-1085.
129 LU N, WU Y P, ZHENG H B, et al. An assessment of multi-view spectral information from UAV-based color-infrared images for improved estimation of nitrogen nutrition status in winter wheat[J]. Precision Agriculture202223(5): 1653-1674.
130 TRINIDAD M C, MARTIN-BRUALLA R, KAINZ F, et al. Multi-view image fusion[C]∥2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2019: 4101-4110.
131 WANG D, CUI X R, CHEN X, et al. Multi-view 3D reconstruction with transformers[C]∥2021 IEEE/CVF International Conference on Computer Vision (ICCV). Piscataway: IEEE Press, 2021: 5702-5711.
132 SONG D, ZHANG Z Q, LI W H, et al. Judgment of benign and early malignant colorectal tumors from ultrasound images with deep multi-view fusion[J]. Computer Methods and Programs in Biomedicine2022215: 106634.
133 AMIN-NAJI M, AGHAGOLZADEH A, EZOJI M. Ensemble of CNN for multi-focus image fusion[J]. Information Fusion201951: 201-214.
134 QIN X M, BAN Y X, WU P, et al. Improved image fusion method based on sparse decomposition[J]. Electronics202211(15): 2321.
135 LIU Y, WANG L, LI H F, et al. Multi-focus image fusion with deep residual learning and focus property detection[J]. Information Fusion202286: 1-16.
136 MUCHONEY D, HAACK B. Change detection for monitoring forest defoliation[J]. Photogrammetric Engineering and Remote Sensing200760: 1243-1251.
137 EZIMAND K, CHAHARDOLI M, AZADBAKHT M, et al. Spatiotemporal analysis of land surface temperature using multi-temporal and multi-sensor image fusion techniques[J]. Sustainable Cities and Society202164: 102508.
138 SAUR G, KRüGER W. Change detection in UAV video mosaics combining a feature based approach and extended image differencing[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences2016, XLI-B7: 557-562.
139 WILBURN B, JOSHI N, VAISH V, et al. High-speed videography using a dense camera array[C]∥Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2004.
140 WILBURN B, JOSHI N, VAISH V, et al. High performance imaging using large camera arrays[J]. ACM Transactions on Graphics200524(3): 765-776.
Outlines

/