GRAVITATED LATENT SPACE LOSS GENERATED BY METRIC TENSOR FOR HIGH-DYNAMIC RANGE IMAGING
- Authors
- Lim, Heunseung; Shin, Jungkyoo; Choi, Hyoungki; Kim, Dohoon; Kim, Eunwoo; Paik, Joonki
- Issue Date
- 2024
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- gravitated latent space; high dynamic range; metric tensor
- Citation
- ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp 3300 - 3304
- Pages
- 5
- Journal Title
- ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
- Start Page
- 3300
- End Page
- 3304
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/74720
- DOI
- 10.1109/ICASSP48485.2024.10448122
- ISSN
- 0736-7791
1520-6149
- Abstract
- High Dynamic Range (HDR) imaging seeks to enhance image quality by combining multiple Low Dynamic Range (LDR) images captured at varying exposure levels. Traditional deep learning approaches often employ reconstruction loss, but this method can lead to ambiguities in feature space during training. To address this issue, we present a new loss function, termed Gravitated Latent Space (GLS) loss, that leverages a metric tensor to introduce a form of virtual gravity within the latent space. This feature helps the model in overcoming saddle points more effectively. Easy to integrate, the GLS loss function fosters stable learning within a convex environment and demonstrates its performance in improving HDR image quality. Experimental data confirms that the proposed method outperforms existing state-of-the-art techniques in quantitative evaluations. © 2024 IEEE.
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Collections - Graduate School of Advanced Imaging Sciences, Multimedia and Film > Department of Imaging Science and Arts > 1. Journal Articles
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