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Increasing the Geometrical and Interpretation Quality of Unmanned Aerial Vehicle Photogrammetry Products Using Super-Resolution Algorithms

Abstrakt

Unmanned aerial vehicles (UAVs) have now become very popular in photogrammetric and remote-sensing applications. Every day, these vehicles are used in new applications, new terrains, and new tasks, facing new problems. One of these problems is connected with flight altitude and the determined ground sample distance in a specific area, especially within cities and industrial and construction areas. The problem is that a safe flight altitude and camera parameters do not meet the required or demanded ground sampling distance or the geometrical and texture quality. In the cases where the flight level cannot be reduced and there is no technical ability to change the UAV camera or lens, the author proposes the use of a super-resolution algorithm for enhancing images acquired by UAVs and, consequently, increase the geometrical and interpretation quality of the final photogrammetric product. The main study objective was to utilize super-resolution (SR) algorithms to improve the geometric and interpretative quality of the final photogrammetric product, assess its impact on the accuracy of the photogrammetric processing and on the traditional digital photogrammetry workflow. The research concept assumes a comparative analysis of photogrammetric products obtained on the basis of data collected from small, commercial UAVs and products obtained from the same data but additionally processed by the super-resolution algorithm. As the study concludes, the photogrammetric products that are created as a result of the algorithms' operation on high-altitude images show a comparable quality to the reference products from low altitudes and, in some cases, even improve their quality.

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Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
Remote Sensing nr 12, strony 1 - 21,
ISSN: 2072-4292
Język:
angielski
Rok wydania:
2020
Opis bibliograficzny:
Burdziakowski P.: Increasing the Geometrical and Interpretation Quality of Unmanned Aerial Vehicle Photogrammetry Products Using Super-Resolution Algorithms// Remote Sensing -Vol. 12,iss. 5 (2020), s.1-21
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/rs12050810
Bibliografia: test
  1. Nex, F. UAV-g 2019: Unmanned Aerial Vehicles in Geomatics. Drones 2019, 3, 73. otwiera się w nowej karcie
  2. Meng, L.; Peng, Z.; Zhou, J.; Zhang, J.; Lu, Z.; Baumann, A.; Du, Y. Real-Time Detection of Ground Objects Based on Unmanned Aerial Vehicle Remote Sensing with Deep Learning: Application in Excavator Detection for Pipeline Safety. Remote Sens. 2020, 12, 182. otwiera się w nowej karcie
  3. Wierzbicki, D.; Kedzierski, M.; Fryskowska, A.; Jasinski, J. Quality Assessment of the Bidirectional Reflectance Distribution Function for NIR Imagery Sequences from UAV. Remote Sensing 2018, 10, 1348. otwiera się w nowej karcie
  4. Kedzierski, M.; Wierzbicki, D.; Sekrecka, A.; Fryskowska, A.; Walczykowski, P.; Siewert, J. Influence of Lower Atmosphere on the Radiometric Quality of Unmanned Aerial Vehicle Imagery. Remote Sens. 2019, 11, 1214. otwiera się w nowej karcie
  5. Wierzbicki, D.; Kedzierski, M.; Sekrecka, A. A Method for Dehazing Images Obtained from Low Altitudes during High-Pressure Fronts. Remote Sens. 2019, 12, 25. otwiera się w nowej karcie
  6. Zanutta, A.; Lambertini, A.; Vittuari, L. UAV Photogrammetry and Ground Surveys as a Mapping Tool for Quickly Monitoring Shoreline and Beach Changes. Journal of Marine Science and Engineering 2020, 8, 52. otwiera się w nowej karcie
  7. Šašak, J.; Gallay, M.; Kaňuk, J.; Hofierka, J.; Minár, J. Combined Use of Terrestrial Laser Scanning and UAV Photogrammetry in Mapping Alpine Terrain. Remote Sens. 2019, 11, 2154. otwiera się w nowej karcie
  8. Zongjian, L.I.N. Others UAV for mapping-low altitude photogrammetric survey. Int. Arch. Photogramm. Remote Sens. 2008, 37, 1183-1186.
  9. Fan, X.; Nie, G.; Gao, N.; Deng, Y.; An, J.; Li, H. Building extraction from UAV remote sensing data based on photogrammetry method. In Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, Texas, USA, 23-28 July 2017; pp. 3317-3320. otwiera się w nowej karcie
  10. Pei, H.; Wan, P.; Li, C.; Feng, H.; Yang, G.; Xu, B.; Niu, Q. Accuracy analysis of UAV remote sensing imagery mosaicking based on structure-from-motion. In Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, Texas, USA, 23-28 July 2017; pp. 5904-5907. otwiera się w nowej karcie
  11. Gao, N.; Zhao, J.; Song, D.; Chu, J.; Cao, K.; Zha, X.; Du, X. High-Precision and Light-Small Oblique Photogrammetry UAV Landscape Restoration Monitoring. In Proceedings of the 2018 Ninth International Conference on Intelligent Control and Information Processing (ICICIP), Wanzhou, China, 9-11 November 2018; pp. 301-304. otwiera się w nowej karcie
  12. Samad, A.M.; Kamarulzaman, N.; Hamdani, M.A.; Mastor, T.A.; Hashim, K.A. The potential of Unmanned Aerial Vehicle (UAV) for civilian and mapping application. In Proceedings of the 2013 IEEE 3rd International Conference on System Engineering and Technology, Shah Alam, Malaysia, 19-20 August 2013; pp. 313-318. otwiera się w nowej karcie
  13. Ismael, R.Q.; Henari, Q.Z. Accuracy Assessment of UAV photogrammetry for Large Scale Topographic Mapping. In Proceedings of the 2019 International Engineering Conference (IEC), Erbil, KRG, Iraq, 23-24 April 2019; pp. 1-5. otwiera się w nowej karcie
  14. Tariq, A.; Osama, S.M.; Gillani, A. Development of a Low Cost and Light Weight UAV for Photogrammetry and Precision Land Mapping Using Aerial Imagery. In Proceedings of the 2016 International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan, 19-21 December 2016; pp. 360-364. otwiera się w nowej karcie
  15. Segales, A.; Gregor, R.; Rodas, J.; Gregor, D.; Toledo, S. Implementation of a low cost UAV for photogrammetry measurement applications. In Proceedings of the 2016 International Conference on Unmanned Aircraft Systems (ICUAS), Key Bridge Marriott Arlington, VA, USA, 7-10 June 2016; pp. 926- 932. otwiera się w nowej karcie
  16. Song, Y.; Wang, J.; Shan, B. An Effective Leaf Area Index Estimation Method for Wheat from UAV-Based Point Cloud Data. In Proceedings of the IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 28 July-2 August 2019; pp. 1801-1804. otwiera się w nowej karcie
  17. Mansoori, S.A.; Al-Ruzouq, R.; Dogom, D.A.; al Shamsi, M.; Mazzm, A.A.; Aburaed, N. Photogrammetric Techniques and UAV for Drainage Pattern and Overflow Assessment in Mountainous Terrains- Hatta/UAE. In Proceedings of the IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 28 July-2 August 2019; pp. 951-954. otwiera się w nowej karcie
  18. Fernández, T.; Pérez, J.L.; Cardenal, J.; Gómez, J.M.; Colomo, C.; Delgado, J. Analysis of Landslide Evolution Affecting Olive Groves Using UAV and Photogrammetric Techniques. Remote Sens. 2016, 8, 837. otwiera się w nowej karcie
  19. Nevalainen, O.; Honkavaara, E.; Tuominen, S.; Viljanen, N.; Hakala, T.; Yu, X.; Hyyppä, J.; Saari, H.; Pölönen, I.; Imai, N.N.; et al. Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging. Remote Sens. 2017, 9, 185. otwiera się w nowej karcie
  20. Feng, Q.; Liu, J.; Gong, J. UAV remote sensing for urban vegetation mapping using random forest and texture analysis. Remote Sens. 2015, 7, 1074-1094. otwiera się w nowej karcie
  21. Zhang, Y.; Wu, H.; Yang, W. Forests Growth Monitoring Based on Tree Canopy 3D Reconstruction Using UAV Aerial Photogrammetry. Forests 2019, 10, 1052. otwiera się w nowej karcie
  22. Torresan, C.; Berton, A.; Carotenuto, F.; Gennaro, S.F. di; otwiera się w nowej karcie
  23. Gioli, B.; Matese, A.; Miglietta, F.; Vagnoli, C.; Zaldei, A.; Wallace, L. Forestry applications of UAVs in Europe: a review. International Journal of Remote Sensing 2017, 38, 2427-2447.
  24. Jizhou, W.; Zongjian, L.; Chengming, L. Reconstruction of buildings from a single UAV image. In Proceedings of the Proc. International Society for Photogrammetry and Remote Sensing Congress, Zurich, Switzerland, 6-12 September 2004; pp. 100-103.
  25. Saleri, R.; Cappellini, V.; Nony, N.; de Luca, L.; Pierrot-Deseilligny, M.; Bardiere, E.; Campi, M. UAV photogrammetry for archaeological survey: The Theaters area of Pompeii. In Proceedings of the 2013 otwiera się w nowej karcie
  26. Digital Heritage International Congress (DigitalHeritage), Marseille, France 28 October-1 November 2013; otwiera się w nowej karcie
  27. Tariq, A.; Gillani, S.M.O.A.; Qureshi, H.K.; Haneef, I. Heritage preservation using aerial imagery from light weight low cost Unmanned Aerial Vehicle (UAV). In Proceedings of the 2017 International Conference on Communication Technologies (ComTech), Guayaquil, Ecuador, 6-9 November 2017; pp. 201-205. otwiera się w nowej karcie
  28. Hashim, K.A.; Ahmad, A.; Samad, A.M.; NizamTahar, K.; Udin, W.S. Integration of low altitude aerial terrestrial photogrammetry data in 3D heritage building modeling. In Proceedings of the 2012 IEEE Control and System Graduate Research Colloquium, Shah Alam, Selangor, Malaysia, 16-17 July 2012; pp. 225-230. otwiera się w nowej karcie
  29. Frankenberger, J.R.; Huang, C.; Nouwakpo, K. Low-Altitude Digital Photogrammetry Technique to Assess Ephemeral Gully Erosion. In Proceedings of the IGARSS 2008-2008 IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA, 6-11 July 2008; Volume 4, pp. IV-117-IV-120. otwiera się w nowej karcie
  30. Mancini, F.; Castagnetti, C.; Rossi, P.; Dubbini, M.; Fazio, N.L.; Perrotti, M.; Lollino, P. An Integrated Procedure to Assess the Stability of Coastal Rocky Cliffs: From UAV Close-Range Photogrammetry to Geomechanical Finite Element Modeling. Remote Sens. 2017, 9, 1235. otwiera się w nowej karcie
  31. Simpson, J.E.; Wooster, M.J.; Smith, T.E.L.; Trivedi, M.; Vernimmen, R.R.E.; Dedi, R.; Shakti, M.; Dinata, Y. Tropical Peatland Burn Depth and Combustion Heterogeneity Assessed Using UAV Photogrammetry and Airborne LiDAR. Remote Sens. 2016, 8, 1000. otwiera się w nowej karcie
  32. Lu, C. Uav-Based photogrammetry for the application on geomorphic change-the case study of Penghu Kuibishan geopark, Taiwan. In Proceedings of the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22-27 July 2018; pp. 7840-7842. otwiera się w nowej karcie
  33. Özcan, O.; Akay, S.S. Modeling Morphodynamic Processes in Meandering Rivers with UAV-Based Measurements. In Proceedings of the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22-27 July 2018; pp. 7886-7889. otwiera się w nowej karcie
  34. Shi, Y.; Bai, M.; Li, Y.; Li, Y. Study on UAV Remote Sensing Technology in Irrigation District Informationization Construction and Application. In Proceedings of the 2018 10th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), Changsha, China, 10-11 February 2018; pp. 252-255. otwiera się w nowej karcie
  35. Zefri, Y.; Elkcttani, A.; Sebari, I.; Lamallam, S.A. Inspection of Photovoltaic Installations by Thermo-visual UAV Imagery Application Case: Morocco. In Proceedings of the 2017 International Renewable and Sustainable Energy Conference (IRSEC), Morocco, Tangier, 7-20 April 2017; pp. 1-6. otwiera się w nowej karcie
  36. Tan, Y.; Li, Y. UAV Photogrammetry-Based 3D Road Distress Detection. ISPRS Int. J. Geo-Inf. 2019, 8, 409. otwiera się w nowej karcie
  37. Ro, K.; Oh, J.-S.; Dong, L. Lessons learned: Application of small uav for urban highway traffic monitoring. In Proceedings of the 45th AIAA aerospace sciences meeting and exhibit; 2007; p. 596. otwiera się w nowej karcie
  38. Semsch, E.; Jakob, M.; Pavlicek, D.; Pechoucek, M. Autonomous UAV surveillance in complex urban environments. In Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, Washington, DC, USA, 15-18 September 2009; Volume 2, pp. 82-85. otwiera się w nowej karcie
  39. Burdziakowski, P. Uav in todays photogrammetry-application areas and challenges. In Proceedings of the International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, Albena, Bulgaria, 30 June-9 July 2018. otwiera się w nowej karcie
  40. Al-falluji, R.A.A.; Youssif, A.A.-H.; Guirguis, S.K. Single Image Super Resolution Algorithms: A Survey and Evaluation. Int. J. Adv. Res. Comput. Eng. Technol. 2017, 6, 1445-1451. otwiera się w nowej karcie
  41. Ledig, C.; Theis, L.; Huszár, F.; Caballero, J.; Cunningham, A.; Acosta, A.; Aitken, A.; Tejani, A.; Totz, J.; Wang, Z.; et al. Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the Proceedings -30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017; Honolulu, HI, USA, 21-26 July 2017; pp. 4681-4690 otwiera się w nowej karcie
  42. Dănișor, C.; Fornaro, G.; Pauciullo, A.; Reale, D.; Datcu, M. Super-Resolution Multi-Look Detection in SAR Tomography. Remote Sens. 2018, 10, 1894. otwiera się w nowej karcie
  43. Jiang, K.; Wang, Z.; Yi, P.; Jiang, J.; Xiao, J.; Yao, Y. Deep Distillation Recursive Network for Remote Sensing Imagery Super-Resolution. Remote Sens. 2018, 10, 1700. otwiera się w nowej karcie
  44. Kwan, C. Remote Sensing Performance Enhancement in Hyperspectral Images. Sensors 2018, 18, 3598. otwiera się w nowej karcie
  45. Mei, S.; Yuan, X.; Ji, J.; Zhang, Y.; Wan, S.; Du, Q. Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network. Remote Sens. 2017, 9, 1139. otwiera się w nowej karcie
  46. Li, L.; Xu, T.; Chen, Y. Improved Urban Flooding Mapping from Remote Sensing Images Using Generalized Regression Neural Network-Based Super-Resolution Algorithm. Remote Sens. 2016, 8, 625. otwiera się w nowej karcie
  47. Hu, J.; Zhao, M.; Li, Y. Hyperspectral Image Super-Resolution by Deep Spatial-Spectral Exploitation. Remote Sens. 2019, 11(24), 2933. otwiera się w nowej karcie
  48. Demirel, H.; Anbarjafari, G. Discrete wavelet transform-based satellite image resolution enhancement. IEEE Trans. Geosci. Remote Sens. 2011, 49, 1997-2004. otwiera się w nowej karcie
  49. Ducournau, A.; Fablet, R. Deep learning for ocean remote sensing: An application of convolutional neural networks for super-resolution on satellite-derived SST data. In Proceedings of the 2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS), Cancun, Mexico, 4 December 2016; pp. 1-6. otwiera się w nowej karcie
  50. Tatem, A.J.; Lewis, H.G.; Atkinson, P.M.; Nixon, M.S. Super-resolution target identification from remotely sensed images using a Hopfield neural network. IEEE Trans. Geosci. Remote Sens. 2001, 39, 781-796. otwiera się w nowej karcie
  51. Harikrishna, O.; Maheshwari, A. Satellite image resolution enhancement using DWT technique. Int. J. Soft Comput. Eng. (IJSCE) 2012, 2(5), pp. 274-275.
  52. Li, F.; Jia, X.; Fraser, D. Universal HMT based super resolution for remote sensing images. In Proceedings of the 2008 15th IEEE International Conference on Image Processing, San Diego, CA, USA, 12-15 October 2008; pp. 333-336. otwiera się w nowej karcie
  53. Thornton, M.W.; Atkinson, P.M.; Holland, D.A. Sub-pixel mapping of rural land cover objects from fine spatial resolution satellite sensor imagery using super-resolution pixel-swapping. International Journal of Remote Sens. 2006, 27, 473-491. otwiera się w nowej karcie
  54. Plenge, E.; Poot, D.H.J.; Bernsen, M.; Kotek, G.; Houston, G.; Wielopolski, P.; van der Weerd, L.; Niessen, W.J.; Meijering, E. Super-resolution methods in MRI: Can they improve the trade-off between resolution, signal-to-noise ratio, and acquisition time? Magn. Reson. Med. 2012, 68, 1983-1993. otwiera się w nowej karcie
  55. Trinh, D.-H.; Luong, M.; Dibos, F.; Rocchisani, J.-M.; Pham, C.-D.; Nguyen, T.Q. Novel example-based method for super-resolution and denoising of medical images. IEEE Trans. Image Process. 2014, 23, 1882- 1895.
  56. OˈReilly, M.A.; Hynynen, K. A super-resolution ultrasound method for brain vascular mapping. Med Phys. 2013, 40, 110701. otwiera się w nowej karcie
  57. Greenspan, H. Super-resolution in medical imaging. Comput. J. 2008, 52, 43-63. otwiera się w nowej karcie
  58. Huang, B.; Bates, M.; Zhuang, X. Super-resolution fluorescence microscopy. Annu. Rev. Biochem. 2009, 78, 993-1016. otwiera się w nowej karcie
  59. Huang, B.; Wang, W.; Bates, M.; Zhuang, X. Three-dimensional super-resolution imaging by stochastic optical reconstruction microscopy. Science 2008, 319, 810-813. otwiera się w nowej karcie
  60. Schermelleh, L.; Heintzmann, R.; Leonhardt, H. A guide to super-resolution fluorescence microscopy. J. Cell Biol. 2010, 190, 165-175. otwiera się w nowej karcie
  61. Nieves, D.J.; Gaus, K.; Baker, M.A.B. DNA-Based Super-Resolution Microscopy: DNA-PAINT. Genes 2018, 9, 621. otwiera się w nowej karcie
  62. Dong, H.; Supratak, A.; Mai, L.; Liu, F.; Oehmichen, A.; Yu, S.; Guo, Y. TensorLayer: A Versatile Library for Efficient Deep Learning Development. ACM Multimedia 2017, 10 1210-1217. otwiera się w nowej karcie
  63. Kawulok, M.; Benecki, P.; Piechaczek, S.; Hrynczenko, K.; Kostrzewa, D.; Nalepa, J. Deep Learning for Multiple-Image Super-Resolution. IEEE Geoscience and Remote Sensing Letters 2019, pp. 1-5. otwiera się w nowej karcie
  64. Yuan, Q.; Zhang, L.; Shen, H.; Li, P. Adaptive multiple-frame image super-resolution based on U-curve. IEEE Trans. Image Process. 2010, 19, 3157-3170.
  65. Capel, D.; Zisserman, A. Super-resolution from multiple views using learnt image models. In Proceedings of the Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 2, 2. otwiera się w nowej karcie
  66. Lim, B.; Son, S.; Kim, H.; Nah, S.; Lee, K.M. Enhanced Deep Residual Networks for Single Image Super- Resolution. In Proceedings of the The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Honolulu, HI, USA, 21-26 July 2017. otwiera się w nowej karcie
  67. Liebel, L.; Körner, M. Single-image super resolution for multispectral remote sensing data using convolutional neural networks. ISPRS-Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2016, 41, 883-890. otwiera się w nowej karcie
  68. Shi, W.; Caballero, J.; Huszár, F.; Totz, J.; Aitken, A.P.; Bishop, R.; Rueckert, D.; Wang, Z. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA, 27- 30 June 2016; pp. 1874-1883. otwiera się w nowej karcie
  69. Zhang, Y.; Zheng, Z.; Luo, Y.; Zhang, Y.; Wu, J.; Peng, Z. A CNN-Based Subpixel Level DSM Generation Approach via Single Image Super-Resolution. Photogramm. Eng. Remote Sens. 2019, 85, 765-775. otwiera się w nowej karcie
  70. LLC, A. Agisoft Metashape User Manual Professional Edition, Version 1.5 Available online: https://www.agisoft.com/pdf/metashape-pro_1_5_en.pdf (accessed on 13 February 2020).
  71. Agisoft LLC Agisoft Available online: https://www.agisoft.com/ (accessed on 13 February 2020).
  72. Xu, F.; Muneyoshi, H. A Case Study of DJI, the Top Drone Maker in the World. Kindai Manag. Rev. 2017, 5, 97-104.
  73. Schroth, L. Drone Manufacturer Market Shares: DJI Leads the Way in the US Available online: https://www.droneii.com/drone-manufacturer-market-shares-dji-leads-the-way-in-the-us (accessed on 12 December 2019).
  74. Burdziakowski, P. A COMMERCIAL OF THE SHELF COMPONENTS FOR AN UNMANNED AIR VEHICLE PHOTOGRAMMETRY. In Proceedings of the 16th International Multidisciplinary Scientific GeoConference SGEM2016, Informatics, Geoinformatics and Remote Sensing, Albena, Bulgaria, 30 June-6 July 2016. otwiera się w nowej karcie
  75. Blaikie, R.J.; Melville, D.O.S.; Alkaisi, M.M. Super-resolution near-field lithography using planar silver lenses: A review of recent developments. Microelectron. Eng. 2006, 83, 723-729. otwiera się w nowej karcie
  76. Siu, W.-C.; Hung, K.-W. Review of image interpolation and super-resolution. In Proceedings of the Proceedings of The 2012 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, Hollywood, CA, USA, 3-6 December 2012; pp. 1-10.
  77. Yang, W.; Zhang, X.; Tian, Y.; Wang, W.; Xue, J.-H.; Liao, Q. Deep learning for single image super- resolution: A brief review. IEEE Trans. Multimed. 2019, 1, 99. otwiera się w nowej karcie
  78. Dong, C.; Loy, C.C.; Tang, X. Accelerating the Super-Resolution Convolutional Neural Network; European conference on computer vision ECCV 2016 Amsterdam, The Netherlands, October 11-14, 2016, Springer, p. 391-407. otwiera się w nowej karcie
  79. Kim, J.; Lee, J.K.; Lee, K.M. Accurate Image Super-Resolution Using Very Deep Convolutional Networks; IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27-30 June 2016, pp. 1646-1654. otwiera się w nowej karcie
  80. Li, Z.; Yang, J.; Liu, Z.; Yang, X.; Jeon, G.; Wu, W. Feedback Network for Image Super-Resolution. In Proceedings of the The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Xi'an, China, 8-11 November 2019. otwiera się w nowej karcie
  81. Tai, Y.; Yang, J.; Liu, X.; Xu, C. MemNet: A Persistent Memory Network for Image Restoration. In Proceedings of the International Conference on Computer Vision, Venice, Italy, 22-29 October 2017. otwiera się w nowej karcie
  82. Wang, X.; Yu, K.; Wu, S.; Gu, J.; Liu, Y.; Dong, C.; Qiao, Y.; Loy, C.C. ESRGAN: Enhanced super-resolution generative adversarial networks. In Proceedings of the The European Conference on Computer Vision Workshops (ECCVW), Munich, Germany, 8-14 September 2018. otwiera się w nowej karcie
  83. Zhang, K.; Zuo, W.; Gu, S.; Zhang, L. Learning Deep CNN Denoiser Prior for Image Restoration. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21- 28 July 2017; pp. 3929-3938. otwiera się w nowej karcie
  84. Zhang, K.; Zuo, W.; Zhang, L. Learning a single convolutional super-resolution network for multiple degradations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18-22 June 2018; pp. 3262-3271. otwiera się w nowej karcie
  85. Zhang, Y.; Li, K.; Li, K.; Wang, L.; Zhong, B.; Fu, Y. Image Super-Resolution Using Very Deep Residual Channel Attention Networks. In Proceedings of the ECCV, Munish, Germany, 8-14 September 2018. otwiera się w nowej karcie
  86. Zhang, Y.; Tian, Y.; Kong, Y.; Zhong, B.; Fu, Y. Residual Dense Network for Image Super-Resolution 2018. otwiera się w nowej karcie
  87. He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, WI, USA, 27-30 June 2016. otwiera się w nowej karcie
  88. Wang, Z.; Simoncelli, E.P.; Bovik, A.C. Multi-scale structural similarity for image quality assessment. In Proceedings of the Conference Record of the Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, 9-12 November 2003. otwiera się w nowej karcie
  89. Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; Bernstein, M.; et al. ImageNet Large Scale Visual Recognition Challenge. Int. J. Comput. Vis. 2015, 115, 211- 252. otwiera się w nowej karcie
  90. Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. In Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015-Conference Track Proceedings, San Diego, CA, USA, 7-9 May 2015.
  91. Agustsson, E.; Timofte, R. NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA, 21-26 July 2017. otwiera się w nowej karcie
  92. Mittal, A.; Moorthy, A.K.; Bovik, A.C. No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 2012, 21; pp. 4695 -4708 otwiera się w nowej karcie
  93. Mittal, A.; Soundararajan, R.; Bovik, A.C. Making a "completely blind" image quality analyzer. IEEE Signal Process. Lett. 2013, 21, pp. 209-212 otwiera się w nowej karcie
  94. Venkatanath, N.; Praneeth, D.; Maruthi Chandrasekhar, B.H.; Channappayya, S.S.; Medasani, S.S. Blind image quality evaluation using perception based features. In Proceedings of the 2015 21st National Conference on Communications, NCC 2015, Bombay, Indian, 27 February-1 March; 2015. otwiera się w nowej karcie
  95. Sheikh, H.R.; Wang, Z.; Cormack, L.; Bovik, A.C. LIVE Image Quality Assessment Database Release 2. Available online: https://live.ece.utexas.edu/research/quality/ (accessed on 12 December 2019).
  96. Fraser, B.T.; Congalton, R.G. Issues in Unmanned Aerial Systems (UAS) Data Collection of Complex Forest Environments. Remote Sens. 2018, 10, 908. otwiera się w nowej karcie
  97. Nourbakhshbeidokhti, S.; Kinoshita, A.M.; Chin, A.; Florsheim, J.L. A Workflow to Estimate Topographic and Volumetric Changes and Errors in Channel Sedimentation after Disturbance. Remote Sens. 2019, 11, 586. otwiera się w nowej karcie
Weryfikacja:
Politechnika Gdańska

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