{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T04:23:51Z","timestamp":1775881431047,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,4,2]],"date-time":"2021-04-02T00:00:00Z","timestamp":1617321600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shenzhen International Collaborative Research Project under Grant","award":["GJHZ20180929151604875"],"award-info":[{"award-number":["GJHZ20180929151604875"]}]},{"name":"National Natural Science Foundation of Guangdong Province, China","award":["No. 2018B030306026"],"award-info":[{"award-number":["No. 2018B030306026"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["No. 2018YFC1508200"],"award-info":[{"award-number":["No. 2018YFC1508200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In unmanned aerial vehicle based urban observation and monitoring, the performance of computer vision algorithms is inevitably limited by the low illumination and light pollution caused degradation, therefore, the application image enhancement is a considerable prerequisite for the performance of subsequent image processing algorithms. Therefore, we proposed a deep learning and generative adversarial network based model for UAV low illumination image enhancement, named LighterGAN. The design of LighterGAN refers to the CycleGAN model with two improvements\u2014attention mechanism and semantic consistency loss\u2014having been proposed to the original structure. Additionally, an unpaired dataset that was captured by urban UAV aerial photography has been used to train this unsupervised learning model. Furthermore, in order to explore the advantages of the improvements, both the performance in the illumination enhancement task and the generalization ability improvement of LighterGAN were proven in the comparative experiments combining subjective and objective evaluations. In the experiments with five cutting edge image enhancement algorithms, in the test set, LighterGAN achieved the best results in both visual perception and PIQE (perception based image quality evaluator, a MATLAB build-in function, the lower the score, the higher the image quality) score of enhanced images, scores were 4.91 and 11.75 respectively, better than EnlightenGAN the state-of-the-art. In the enhancement of low illumination sub-dataset Y (containing 2000 images), LighterGAN also achieved the lowest PIQE score of 12.37, 2.85 points lower than second place. Moreover, compared with the CycleGAN, the improvement of generalization ability was also demonstrated. In the test set generated images, LighterGAN was 6.66 percent higher than CycleGAN in subjective authenticity assessment and 3.84 lower in PIQE score, meanwhile, in the whole dataset generated images, the PIQE score of LighterGAN is 11.67, 4.86 lower than CycleGAN.<\/jats:p>","DOI":"10.3390\/rs13071371","type":"journal-article","created":{"date-parts":[[2021,4,2]],"date-time":"2021-04-02T10:34:09Z","timestamp":1617359649000},"page":"1371","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["LighterGAN: An Illumination Enhancement Method for Urban UAV Imagery"],"prefix":"10.3390","volume":"13","author":[{"given":"Junshu","family":"Wang","sequence":"first","affiliation":[{"name":"College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Yang","sequence":"additional","affiliation":[{"name":"Pearl River Water Resources Research Institute of PRWRC, Guangzhou 510611, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Accounting, Guangdong University of Foreign Studies, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxing","family":"Han","sequence":"additional","affiliation":[{"name":"Matrix Mathematical Imaging Center, Research Institute of Tsinghua University in Shenzhen, Shenzhen 518057, China"},{"name":"Lingnan Guangdong Laboratory of Modern Agriculture, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,2]]},"reference":[{"key":"ref_1","unstructured":"Noor, N.M., Abdullah, A., and Hashim, M. (2018, January 24\u201325). Remote sensing UAV\/drones and its applications for urban areas: A review. Proceedings of the IOP Conference Series: Earth and Environmental Science, 9th IGRSM International Conference and Exhibition on Geospatial & Remote Sensing (IGRSM), Kuala Lumpur, Malaysia."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1016\/j.scs.2018.01.053","article-title":"Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities","volume":"38","author":"Silva","year":"2018","journal-title":"Sustain. Cities Soc."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, W., Wei, C., Yang, W., and Liu, J. (2018, January 15\u201319). GLADNet: Low-Light Enhancement Network with Global Awareness. Proceedings of the Automatic Face & Gesture Recognition (FG) & 2018 13th IEEE International Conference, Xi\u2019an, China.","DOI":"10.1109\/FG.2018.00118"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yang, H., Chen, P., Huang, C., Zhuang, Y., and Shiau, Y. (2011, January 16\u201318). Low complexity underwater image enhancement based on dark channel prior. Proceedings of the 2011 Second International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA), Shenzhen, China.","DOI":"10.1109\/IBICA.2011.9"},{"key":"ref_5","unstructured":"Ulyanov, D., Vedaldi, A., and Lempitsky, V. (2017). Deep Image Prior. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4364","DOI":"10.1109\/TIP.2019.2910412","article-title":"Low-light image enhancement via a deep hybrid network","volume":"28","author":"Ren","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_7","unstructured":"Lv, F., and Lu, F. (2019). Attention-guided low-light image enhancement. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"13737","DOI":"10.1109\/ACCESS.2019.2891957","article-title":"A pipeline neural network for low-light image enhancement","volume":"7","author":"Guo","year":"2019","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhu, J., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. Proceedings of the International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., and He, K. (2018, January 18\u201323). Non-local neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1109\/TIP.2016.2639450","article-title":"LIME: Low-Light Image Enhancement via Illumination Map Estimation","volume":"26","author":"Guo","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1111\/cgf.13833","article-title":"Dual illumination estimation for robust exposure correction","volume":"38","author":"Zhang","year":"2019","journal-title":"Comput. Graph. Forum"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Parthasarathy, S., and Sankaran, P. (2012, January 3\u20135). An automated multi Scale Retinex with Color Restoration for image enhancement. Proceedings of the 2012 National Conference on Communications (NCC), Kharagpur, India.","DOI":"10.1109\/NCC.2012.6176791"},{"key":"ref_14","unstructured":"Jiang, Y., Gong, X., Liu, D., Cheng, Y., Fang, C., Shen, X., Yang, J., Zhou, P., and Wang, Z. (2019). Enlightengan: Deep light enhancement without paired supervision. arXiv."},{"key":"ref_15","unstructured":"(2020, July 25). Perception Based Image Quality Evaluator (PIQE) No-Reference Image Quality Score. Available online: https:\/\/www.mathworks.com\/help\/images\/ref\/piqe.html."},{"key":"ref_16","unstructured":"Zhao, S., Liu, Z., Lin, J., Zhu, J.Y., and Han, S. (2019). Differentiable augmentation for data-efficient gan training. arXiv."},{"key":"ref_17","unstructured":"Kleinberg, R., Ligett, K., Piliouras, G., and Tardos, \u00c9. (2011, January 7\u20139). Beyond the Nash Equilibrium Barrier. Proceedings of the Symposium on Innovations in Computer Science (ICS), Beijing, China."},{"key":"ref_18","unstructured":"Mohammadi, P., Ebrahimi-Moghadam, A., and Shirani, S. (2014). Subjective and objective quality assessment of image: A survey. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hore, A., and Ziou, D. (2010, January 23\u201326). Image quality metrics: PSNR vs. SSIM. Proceedings of the 20th International Conference on Pattern Recognition (ICPR), Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.579"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ma, Y., Liu, Y., Cheng, J., Zheng, Y., Ghahremani, M., and Chen, H. (2020, January 4\u20138). Cycle Structure and Illumination Constrained GAN for Medical Image Enhancement. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Lima, Peru.","DOI":"10.1007\/978-3-030-59713-9_64"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ganesan, P., Xue, Z., Singh, S., Long, R., Ghoraani, B., and Antani, S. (2019, January 23\u201327). Performance Evaluation of a Generative Adversarial Network for Deblurring Mobile-phone Cervical Images. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8857124"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Pacot, M.P.B., and Marcos, N. (2020, January 8). Cloud Removal from Aerial Images Using Generative Adversarial Network with Simple Image Enhancement. Proceedings of the 2020 3rd International Conference on Image and Graphics Processing, Singapore.","DOI":"10.1145\/3383812.3383838"},{"key":"ref_23","unstructured":"(2020, January 20). LIVE Image Quality Assessment Database Release. Available online: https:\/\/live.ece.utexas.edu\/research\/quality."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1109\/TNS.2013.2257183","article-title":"Generalization evaluation of machine learning numerical observers for image quality assessment","volume":"60","author":"Kalayeh","year":"2013","journal-title":"IEEE Trans. Nucl. Sci."},{"key":"ref_25","unstructured":"Lasserre, J.A., Bishop, C.M., and Minka, T.P. (2006, January 17\u201322). Principled Hybrids of Generative and Discriminative Models. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York, NY, USA."},{"key":"ref_26","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial networks. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_27","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. (2017, January 4\u20139). Improved training of wasserstein gans. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_28","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1007\/s12194-019-00520-y","article-title":"Overview of image-to-image translation by use of deep neural networks: Denoising, super-resolution, modality conversion, and reconstruction in medical imaging","volume":"12","author":"Kaji","year":"2019","journal-title":"Radiol. Phys. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J., Zhou, T., and Efros, A.A. (2016). Image-to-image translation with conditional adversarial networks. arXiv.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_31","unstructured":"Mejjati, Y.A., Richardt, C., Tompkin, J., Cosker, D., and Kim, K.I. (2018, January 3\u20138). Unsupervised attention-guided image-to-image translation. Proceedings of the 32th Annual Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_32","first-page":"937","article-title":"Practical guidance for discharge and water quality data collection on small watershed","volume":"49","author":"Harmel","year":"2006","journal-title":"Trans. Am. Soc. Agric. Eng."},{"key":"ref_33","unstructured":"(2020, March 17). Unsplash. Available online: https:\/\/unsplash.com."},{"key":"ref_34","unstructured":"(2020, July 25). POND5. Available online: https:\/\/www.pond5.com."},{"key":"ref_35","unstructured":"(2020, July 20). ShutterStock. Available online: https:\/\/www.shutterstock.com\/."},{"key":"ref_36","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the 32nd International Conference on Machine Learning, ICML, Lille, France."},{"key":"ref_37","unstructured":"Ulyanov, D., Vedaldi, A., and Lempitsky, V. (2016). Instance normalization: The missing ingredient for fast stylization. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.rse.2005.09.008","article-title":"A change detection model based on neighborhood correlation image analysis and decision tree classification","volume":"99","author":"Im","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2014, January 24\u201327). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Kodirov, E., Xiang, T., and Gong, S. (2017, January 21\u201326). Semantic autoencoder for zero-shot learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.473"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Scherer, D., M\u00fcller, A., and Behnke, S. (2010, January 15\u201318). Evaluation of pooling operations in convolutional architectures for object recognition. Proceedings of the International Conference on Artificial Neural Networks, Thessaloniki, Greece.","DOI":"10.1007\/978-3-642-15825-4_10"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0893-6080(88)90014-7","article-title":"Neocognitron: A hierarchical neural network capable of visual pattern recognition","volume":"1","author":"Fukushima","year":"1988","journal-title":"Neural Netw."},{"key":"ref_43","unstructured":"Agarap, A.F. (2018). Deep learning using rectified linear units (relu). arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Tan, C., Sun, F., Kong, T., Fang, B., and Zhang, W. (2019, January 12\u201317). Attention-based Transfer Learning for Brain-computer Interface. Proceedings of the ICASSP 2019\u20132019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8683344"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Shen, T., Zhou, T., Long, G., Jiang, J., Wang, S., and Zhang, C. (2018, January 13\u201319). Reinforced Self-Attention Network: A Hybrid of Hard and Soft Attention for Sequence Modeling. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/604"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"121761","DOI":"10.1016\/j.biortech.2019.121761","article-title":"A fast and easy method for predicting agricultural waste compost maturity by image-based deep learning","volume":"290","author":"Xue","year":"2019","journal-title":"Bioresour. Technol."},{"key":"ref_47","unstructured":"Kingma, D.P., and Adam, B.A.J. (2014). A method for stochastic optimization. arXiv."},{"key":"ref_48","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). Tensorflow: A system for large-scale machines learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, Savannan, GA, USA."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1371\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:33:39Z","timestamp":1760362419000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1371"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,2]]},"references-count":48,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13071371"],"URL":"https:\/\/doi.org\/10.3390\/rs13071371","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,2]]}}}