# MLFcGAN: Multi-level Feature Fusion based Conditional GAN for Underwater Image Color Correction

arXiv: https://arxiv.org/abs/2002.05333 · HTML: https://mlsanity.com/paper/2002.05333/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2020.

## Built on by (most cited first, 8 of 8)

- [SGUIE-Net: Semantic Attention Guided Underwater Image Enhancement with Multi-Scale Perception](https://mlsanity.com/paper/2201.02832/) (2022)
- [Wavelength-based Attributed Deep Neural Network for Underwater Image Restoration](https://mlsanity.com/paper/2106.07910/) (2021)
- [Rank-One Prior: Toward Real-Time Scene Recovery](https://mlsanity.com/paper/2103.17126/) (2021)
- [Twice Mixing: A Rank Learning based Quality Assessment Approach for Underwater Image Enhancement](https://mlsanity.com/paper/2102.00670/) (2021)
- [UIERL: Internal-External Representation Learning Network for Underwater Image Enhancement](https://mlsanity.com/paper/2306.08344/) (2023)
- [Image-Conditional Diffusion Transformer for Underwater Image Enhancement](https://mlsanity.com/paper/2407.05389/) (2024)
- [Underwater Image Enhancement with Physical-based Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2409.18476/) (2024)
- [Impact of Underwater Image Enhancement on Feature Matching](https://mlsanity.com/paper/2507.21715/) (2025)

## Abstract (arXiv)

Color correction for underwater images has received increasing interests, due to its critical role in facilitating available mature vision algorithms for underwater scenarios. Inspired by the stunning success of deep convolutional neural networks (DCNNs) techniques in many vision tasks, especially the strength in extracting features in multiple scales, we propose a deep multi-scale feature fusion net based on the conditional generative adversarial network (GAN) for underwater image color correction. In our network, multi-scale features are extracted first, followed by augmenting local features on each scale with global features. This design was verified to facilitate more effective and faster network learning, resulting in better performance in both color correction and detail preservation. We conducted extensive experiments and compared with the state-of-the-art approaches quantitatively and qualitatively, showing that our method achieves significant improvements.