Fusion of Heterogeneous Adversarial Networks for Single Image Dehazing
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Jaihyun | - |
dc.contributor.author | Han, David K. | - |
dc.contributor.author | Ko, Hanseok | - |
dc.date.accessioned | 2021-08-31T16:06:40Z | - |
dc.date.available | 2021-08-31T16:06:40Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2020 | - |
dc.identifier.issn | 1057-7149 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/58999 | - |
dc.description.abstract | In this paper, we propose a novel image dehazing method. Typical deep learning models for dehazing are trained on paired synthetic indoor dataset. Therefore, these models may be effective for indoor image dehazing but less so for outdoor images. We propose a heterogeneous Generative Adversarial Networks (GAN) based method composed of a cycle-consistent Generative Adversarial Networks (CycleGAN) for producing haze-clear images and a conditional Generative Adversarial Networks (cGAN) for preserving textural details. We introduce a novel loss function in the training of the fused network to minimize GAN generated artifacts, to recover fine details, and to preserve color components. These networks are fused via a convolutional neural network (CNN) to generate dehazed image. Extensive experiments demonstrate that the proposed method significantly outperforms the state-of-the-art methods on both synthetic and real-world hazy images. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Fusion of Heterogeneous Adversarial Networks for Single Image Dehazing | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Ko, Hanseok | - |
dc.identifier.doi | 10.1109/TIP.2020.2975986 | - |
dc.identifier.scopusid | 2-s2.0-85081957368 | - |
dc.identifier.wosid | 000526697100005 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON IMAGE PROCESSING, v.29, pp.4721 - 4732 | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON IMAGE PROCESSING | - |
dc.citation.title | IEEE TRANSACTIONS ON IMAGE PROCESSING | - |
dc.citation.volume | 29 | - |
dc.citation.startPage | 4721 | - |
dc.citation.endPage | 4732 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordAuthor | Atmospheric modeling | - |
dc.subject.keywordAuthor | Image color analysis | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Scattering | - |
dc.subject.keywordAuthor | Estimation | - |
dc.subject.keywordAuthor | Gallium nitride | - |
dc.subject.keywordAuthor | Generative adversarial networks | - |
dc.subject.keywordAuthor | Image dehazing | - |
dc.subject.keywordAuthor | generative adversarial networks | - |
dc.subject.keywordAuthor | fusion method | - |
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