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MSCNN와 cGAN을 이용한 실내안개제거
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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | 심재준 | - |
| dc.contributor.author | 정우진 | - |
| dc.contributor.author | 양현석 | - |
| dc.contributor.author | 한복규 | - |
| dc.contributor.author | 조용채 | - |
| dc.contributor.author | 이호경 | - |
| dc.contributor.author | 문영식 | - |
| dc.date.accessioned | 2021-06-22T11:21:56Z | - |
| dc.date.available | 2021-06-22T11:21:56Z | - |
| dc.date.issued | 2018-11 | - |
| dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/5146 | - |
| dc.description.abstract | We propose an indoor haze removal method using MSCNN and cGAN. The structure of the network consists of multi-scale CNN and cGAN for photo realistic result. Our method outputs the haze removal image immediately, unlike the existing methods of estimating the depth map. Our method has a quantitative evaluation of 22.6879 in PSNR and 0.8872 in SSIM, which is higher than state of the art by 1.342 in PSNR and 0.0116 in SSIM. It also has good results in qualitative evaluation. | - |
| dc.format.extent | 4 | - |
| dc.language | 한국어 | - |
| dc.language.iso | KOR | - |
| dc.publisher | 대한전자공학회 | - |
| dc.title | MSCNN와 cGAN을 이용한 실내안개제거 | - |
| dc.title.alternative | Indoor dehaze using MSCNN and cGAN | - |
| dc.type | Article | - |
| dc.publisher.location | 대한민국 | - |
| dc.identifier.bibliographicCitation | 2018년 대한전자공학회 추계학술대회 논문집, pp 414 - 417 | - |
| dc.citation.title | 2018년 대한전자공학회 추계학술대회 논문집 | - |
| dc.citation.startPage | 414 | - |
| dc.citation.endPage | 417 | - |
| dc.description.isOpenAccess | N | - |
| dc.description.journalRegisteredClass | other | - |
| dc.identifier.url | https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE07624887 | - |
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