{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T09:15:57Z","timestamp":1782378957687,"version":"3.54.5"},"reference-count":38,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T00:00:00Z","timestamp":1672963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Basic Science Research Program through the National Research Foundation of Korea (NRF)","award":["NRF-2021R1I1A3049604"],"award-info":[{"award-number":["NRF-2021R1I1A3049604"]}]},{"name":"Basic Science Research Program through the National Research Foundation of Korea (NRF)","award":["4199990113966"],"award-info":[{"award-number":["4199990113966"]}]},{"name":"Ministry of Education, Korea","award":["NRF-2021R1I1A3049604"],"award-info":[{"award-number":["NRF-2021R1I1A3049604"]}]},{"name":"Ministry of Education, Korea","award":["4199990113966"],"award-info":[{"award-number":["4199990113966"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In this study, we proposed an image conversion method that efficiently removes raindrops on a camera lens from an image using a deep learning technique. The proposed method effectively presents a raindrop-removed image using the Pix2pix generative adversarial network (GAN) model, which can understand the characteristics of two images in terms of newly formed images of different domains. The learning method based on the captured image has the disadvantage that a large amount of data is required for learning and that unnecessary noise is generated owing to the nature of the learning model. In particular, obtaining sufficient original and raindrops images is the most important aspect of learning. Therefore, we proposed a method that efficiently obtains learning data by generating virtual water-drop image data and effectively identifying it using a convolutional neural network (CNN).<\/jats:p>","DOI":"10.3390\/e25010118","type":"journal-article","created":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T03:54:49Z","timestamp":1672977289000},"page":"118","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Rainwater-Removal Image Conversion Learning with Training Pair Augmentation"],"prefix":"10.3390","volume":"25","author":[{"given":"Yu-Keun","family":"Han","sequence":"first","affiliation":[{"name":"School of Electronic and Electrical Engineering, Kyungpook National University, 80 Deahakro, Buk-Gu, Daegu 702-701, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2258-3396","authenticated-orcid":false,"given":"Sung-Woon","family":"Jung","sequence":"additional","affiliation":[{"name":"School of Electronic and Electrical Engineering, Kyungpook National University, 80 Deahakro, Buk-Gu, Daegu 702-701, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7665-4049","authenticated-orcid":false,"given":"Hyuk-Ju","family":"Kwon","sequence":"additional","affiliation":[{"name":"School of Electronic and Electrical Engineering, Kyungpook National University, 80 Deahakro, Buk-Gu, Daegu 702-701, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1030-381X","authenticated-orcid":false,"given":"Sung-Hak","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Electronic and Electrical Engineering, Kyungpook National University, 80 Deahakro, Buk-Gu, Daegu 702-701, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A.A. 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