DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Sunhyeok | ko |
dc.contributor.author | Jang, Dong Gon | ko |
dc.contributor.author | Kim, Dae-Shik | ko |
dc.date.accessioned | 2023-11-21T08:01:03Z | - |
dc.date.available | 2023-11-21T08:01:03Z | - |
dc.date.created | 2023-11-20 | - |
dc.date.issued | 2023-06-18 | - |
dc.identifier.citation | 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023, pp.4208 - 4217 | - |
dc.identifier.uri | http://hdl.handle.net/10203/314942 | - |
dc.description.abstract | Constructing annotated paired datasets for low-light image enhancement is complex and time-consuming, and existing deep learning models often generate noisy outputs or misinterpret shadows. To effectively learn intricate relationships between features in image space with limited labels, we introduce a deep learning model with a backbone structure that incorporates both spatial and layer-wise dependencies. The proposed model features a baseline image-enhancing network with spatial dependencies and an optimized layer attention mechanism to learn feature sparsity and importance. We present a progressive supervised loss function for improvement. Furthermore, we propose a novel Multi-Consistency Regularization (MCR) loss and integrate it within a Multi-Consistency Mean Teacher (MCMT) framework, which enforces agreement on high-level features and incorporates intermediate features for better understanding of the entire image. By combining the MCR loss with the progressive supervised loss, student network parameters can be updated in a single step. Our approach achieves significant performance improvements using fewer labeled data and unlabeled low-light images within our semi-supervised framework. Qualitative evaluations demonstrate the effectiveness of our method in leveraging comprehensive dependencies and unlabeled data for low-light image enhancement. | - |
dc.language | English | - |
dc.publisher | IEEE Computer Society | - |
dc.title | Temporally Averaged Regression for Semi-Supervised Low-Light Image Enhancement | - |
dc.type | Conference | - |
dc.identifier.scopusid | 2-s2.0-85170825640 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 4208 | - |
dc.citation.endingpage | 4217 | - |
dc.citation.publicationname | 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023 | - |
dc.identifier.conferencecountry | CN | - |
dc.identifier.conferencelocation | Vancouver | - |
dc.identifier.doi | 10.1109/CVPRW59228.2023.00443 | - |
dc.contributor.localauthor | Kim, Dae-Shik | - |
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