Transmission and Color-guided Network for Underwater Image Enhancement
Not on the map. Why?
Abstract (arXiv)
In recent years, with the continuous development of the marine industry, underwater image enhancement has attracted plenty of attention. Unfortunately, the propagation of light in water will be absorbed by water bodies and scattered by suspended particles, resulting in color deviation and low contrast. To solve these two problems, we propose an Adaptive Transmission and Dynamic Color guided network (named ATDCnet) for underwater image enhancement. In particular, to exploit the knowledge of physics, we design an Adaptive Transmission-directed Module (ATM) to better guide the network. To deal with the color deviation problem, we design a Dynamic Color-guided Module (DCM) to post-process the enhanced image color. Further, we design an Encoder-Decoder-based Compensation (EDC) structure with attention and a multi-stage feature fusion mechanism to perform color restoration and contrast enhancement simultaneously. Extensive experiments demonstrate the state-of-the-art performance of the ATDCnet on multiple benchmark datasets.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 12 of its 34 references that are in the map. First posted on arXiv 9 Aug 2023.
Builds on
12 of 34 references.
References · 12
- 2019An Underwater Image Enhancement Benchmark Dataset and Beyondcited by 250
- 2019Fast Underwater Image Enhancement for Improved Visual Perceptioncited by 181
- 2021Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embeddingcited by 133
- 2021U-shape Transformer for Underwater Image Enhancementcited by 110
- 2018Enhancing Underwater Imagery using Generative Adversarial Networkscited by 94
- 2018Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Datasetcited by 90
- 2019Real-world Underwater Enhancement: Challenges, Benchmarks, and Solutionscited by 89
- 2020Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perceptioncited by 53
- 2022Uncertainty Inspired Underwater Image Enhancementcited by 53
- 2021UIEC^2-Net: CNN-based Underwater Image Enhancement Using Two Color Spacecited by 34
- 2019Diving Deeper into Underwater Image Enhancement: A Surveycited by 28
- 2019Investigating Task-driven Latent Feasibility for Nonconvex Image Modelingcited by 14
Built on by
Citing papers per year: 2024: 1.
1 paper.