Published: Dec. 27, 2024
Language: Английский
Published: Dec. 27, 2024
Language: Английский
Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)
Published: Dec. 28, 2024
Underwater image enhancement (UIE) is challenging since degradation in aquatic environments complicated and changing over time. Existing mainstream methods rely on either physical-model or data-driven, suffering from performance bottlenecks due to changes imaging conditions training instability. In this article, we attempt adapt the diffusion model UIE task propose a Content-Preserving Diffusion Model (CPDM) address above challenges. CPDM first leverages as its fundamental for stable then designs content-preserving framework deal with conditions. Specifically, construct conditional input module by adopting both raw difference between noisy images at each time step of process, which can enhance model's adaptability considering involving underwater environments. To preserve essential content images, compensation content-aware extracting low-level features down block. We conducted tests LSUI, UIEB, EUVP datasets, results show that outperforms state-of-the-art subjective objective metrics, achieving best overall performance. The GitHub link code https://github.com/GZHU-DVL/CPDM.
Language: Английский
Citations
4Pattern Analysis and Applications, Journal Year: 2025, Volume and Issue: 28(2)
Published: Feb. 24, 2025
Language: Английский
Citations
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Language: Английский
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Published: April 16, 2025
Language: Английский
Citations
0Published: Dec. 27, 2024
Language: Английский
Citations
0