{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T13:23:54Z","timestamp":1743081834021,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":22,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819967018"},{"type":"electronic","value":"9789819967025"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-981-99-6702-5_30","type":"book-chapter","created":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T18:02:42Z","timestamp":1700503362000},"page":"351-361","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dehazing of Satellite Images with Low Wavelength and High Distortion: A Comparative Analysis"],"prefix":"10.1007","author":[{"given":"Amrutha","family":"Sajeevan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"B. A.","family":"Sabarish","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,21]]},"reference":[{"key":"30_CR1","doi-asserted-by":"crossref","unstructured":"Alias, B., Karthika, R., Parameswaran, L.: Classification of high resolution remote sensing images using deep learning techniques. In: 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (2018)","DOI":"10.1109\/ICACCI.2018.8554605"},{"key":"30_CR2","doi-asserted-by":"crossref","unstructured":"Awwad, Z. et al.: Self-supervised deep learning for vehicle detection in high-resolution satellite imagery. In: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (2021)","DOI":"10.1109\/IGARSS47720.2021.9554580"},{"key":"30_CR3","doi-asserted-by":"publisher","first-page":"3926","DOI":"10.3390\/s21113926","volume":"21","author":"J Liu","year":"2021","unstructured":"Liu, J., Wang, S., Wang, X., Ju, M., Zhang, D.: A review of remote sensing image dehazing. Sensors 21, 3926 (2021)","journal-title":"Sensors"},{"key":"30_CR4","doi-asserted-by":"crossref","unstructured":"Takano, H. et al.: Visible light communication on LED-equipped drone and object-detecting camera for post-disaster monitoring. In: 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) (2021)","DOI":"10.1109\/VTC2021-Spring51267.2021.9448902"},{"key":"30_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2019.111443","volume":"237","author":"N Levin","year":"2020","unstructured":"Levin, N., et al.: Remote sensing of night lights: a review and an outlook for the future. Remote Sens. Environ. 237, 111443 (2020)","journal-title":"Remote Sens. Environ."},{"key":"30_CR6","doi-asserted-by":"publisher","first-page":"5646","DOI":"10.3390\/s21165646","volume":"21","author":"S Illarionova","year":"2021","unstructured":"Illarionova, S., Shadrin, D., Trekin, A., Ignatiev, V., Oseledets, I.: Generation of the NIR spectral band for satellite images with convolutional neural networks. Sensors 21, 5646 (2021)","journal-title":"Sensors"},{"key":"30_CR7","doi-asserted-by":"crossref","unstructured":"Song, Y., He, Z., Qian, H., Du, X.: Vision transformers for single image Dehazing. IEEE Trans. Image Process. 1\u20131 (2023)","DOI":"10.1109\/TIP.2023.3256763"},{"issue":"9","key":"30_CR8","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.1109\/TMM.2019.2902097","volume":"21","author":"X Min","year":"2019","unstructured":"Min, X., et al.: Quality evaluation of image dehazing methods using synthetic hazy images. IEEE Trans. Multimedia 21(9), 2319\u20132333 (2019)","journal-title":"IEEE Trans. Multimedia"},{"key":"30_CR9","doi-asserted-by":"publisher","first-page":"11595","DOI":"10.1109\/ACCESS.2020.2965174","volume":"8","author":"MF Khan","year":"2020","unstructured":"Khan, M.F., et al.: Fuzzy-based histogram partitioning for bi-histogram equalisation of low contrast images. IEEE Access. 8, 11595\u201311614 (2020)","journal-title":"IEEE Access."},{"key":"30_CR10","doi-asserted-by":"publisher","first-page":"17705","DOI":"10.1007\/s11042-021-10607-7","volume":"80","author":"P Wang","year":"2021","unstructured":"Wang, P., Wang, Z., Lv, D., Zhang, C., Wang, Y.: Low illumination color image enhancement based on Gabor filtering and Retinex Theory. Multimedia Tools Appl. 80, 17705\u201317719 (2021)","journal-title":"Multimedia Tools Appl."},{"key":"30_CR11","doi-asserted-by":"publisher","first-page":"1100","DOI":"10.1109\/TIP.2020.3040075","volume":"30","author":"P Li","year":"2021","unstructured":"Li, P., et al.: Deep Retinex network for single image dehazing. IEEE Trans. Image Process. 30, 1100\u20131115 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"30_CR12","doi-asserted-by":"publisher","first-page":"2692","DOI":"10.1109\/TIP.2019.2952032","volume":"29","author":"A Golts","year":"2020","unstructured":"Golts, A., Freedman, D., Elad, M.: Unsupervised single image dehazing using dark channel prior loss. IEEE Trans. Image Process. 29, 2692\u20132701 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"30_CR13","doi-asserted-by":"crossref","unstructured":"Likhitaa, P.S., Anand, R.: A comparative analysis of image dehazing using image processing and deep learning techniques. In: 2021 6th International Conference on Communication and Electronics Systems (ICCES) (2021)","DOI":"10.1109\/ICCES51350.2021.9489118"},{"key":"30_CR14","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1016\/j.neucom.2020.06.041","volume":"410","author":"S Zhang","year":"2020","unstructured":"Zhang, S., He, F., Ren, W.: NLDN: non-local dehazing network for dense haze removal. Neurocomputing 410, 363\u2013373 (2020)","journal-title":"Neurocomputing"},{"key":"30_CR15","doi-asserted-by":"publisher","first-page":"8968","DOI":"10.1109\/TIP.2021.3116790","volume":"30","author":"H Ullah","year":"2021","unstructured":"Ullah, H., et al.: Light-dehazenet: a novel lightweight CNN architecture for single image dehazing. IEEE Trans. Image Process. 30, 8968\u20138982 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"30_CR16","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.procs.2019.01.201","volume":"147","author":"H Rashid","year":"2019","unstructured":"Rashid, H., et al.: Single image dehazing using CNN. Procedia Comput. Sci. 147, 124\u2013130 (2019)","journal-title":"Procedia Comput. Sci."},{"key":"30_CR17","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1007\/s11760-021-02075-1","volume":"16","author":"F Li","year":"2022","unstructured":"Li, F., Di, X., Zhao, C., Zheng, Y., Wu, S.: FA-GAN: A feature attention GAN with fusion discriminator for non-homogeneous dehazing. SIViP 16, 1243\u20131251 (2022)","journal-title":"SIViP"},{"key":"30_CR18","doi-asserted-by":"crossref","unstructured":"Sanjay, A., Nair, J.J., Gopakumar, G.: Haze removal using generative Adversarial Network. In: Lecture Notes in Electrical Engineering, pp. 207\u2013217 (2021)","DOI":"10.1007\/978-981-33-6987-0_18"},{"issue":"1","key":"30_CR19","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1109\/TIP.2018.2867951","volume":"28","author":"B Li","year":"2019","unstructured":"Li, B., et al.: Benchmarking single-image dehazing and beyond. IEEE Trans. Image Process. 28(1), 492\u2013505 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"30_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, J., He, F., Duan, Y., Yang, S.: AIDEDNet: Anti-interference and detail enhancement dehazing network for real-world scenes. Front. Comput. Sci. 17 (2022)","DOI":"10.1007\/s11704-022-1523-9"},{"key":"30_CR21","doi-asserted-by":"crossref","unstructured":"Ge, W., Lin, Y., Wang, Z., Wang, G., Tan, S.: An improved U-net architecture for image dehazing. IEICE Trans. Inform. Syst. E104.D, 2218\u20132225 (2021)","DOI":"10.1587\/transinf.2021EDP7043"},{"key":"30_CR22","doi-asserted-by":"crossref","unstructured":"Karras, T. et al.: Analyzing and improving the image quality of stylegan. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00813"}],"container-title":["Smart Innovation, Systems and Technologies","Evolution in Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-6702-5_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T06:09:37Z","timestamp":1728454177000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-6702-5_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819967018","9789819967025"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-6702-5_30","relation":{},"ISSN":["2190-3018","2190-3026"],"issn-type":[{"type":"print","value":"2190-3018"},{"type":"electronic","value":"2190-3026"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"21 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FICTA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Frontiers of Intelligent Computing: Theory and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cardiff","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ficta2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ficta.co.uk\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}