Awesome Underwater Image Enhancement

By Xiang Chen, Yufeng Li, Yufeng Huang

1 Description

• A collection of awesome underwater image enhancement methods. Papers, codes and datasets are maintained.

2 Related Work

2.1 Datasets

• U45 [download]
• EUVP [download]
• DUIE [download]
• UIEB [download]
• UWCNN [download]
• TURBID [download]
• Uw-imagenet [download]
• MHL, Jamaica field [download]

2.2 Papers

2020

• Marques et al, L2UWE: A Framework for the Efficient Enhancement of Low-Light Underwater Images Using Local Contrast and Multi-Scale Fusion. [paper][code]
• Zhou et al, Domain Adaptive Adversarial Learning Based on Physics Model Feedback for Underwater Image. [paper][code]
• Marques et al, L2UWE: A Framework for the Efficient Enhancement of Low-Light Underwater Images Using Local Contrast and Multi-Scale Fusion. [paper][code]
• Islam et al, Fast Underwater Image Enhancement for Improved Visual Perception. [paper][code]

2019

• Anwar et al, Diving Deeper into Underwater Image Enhancement: A Survey. [paper][code]
• Li et al, An Underwater Image Enhancement Benchmark Dataset and Beyond. [paper][code]
• Roznere et al, Real-time Model-based Image Color Correction for Underwater Robots. [paper][code]
• Jamadandi et al, Exemplar based Underwater Image Enhancement augmented by Wavelet Corrected Transforms. [paper][code]
• Song et al, Enhancement of Underwater Images With Statistical Model of Background Light and Optimization of Transmission Map. [paper][code]
• Li et al, Underwater Scene Prior Inspired Deep Underwater Image and Video Enhancement. [paper][code]
• Uplavikar et al, All-In-One Underwater Image Enhancement using Domain-Adversarial Learning. [paper][code]
• Guo et al, Underwater Image Enhancement Using a Multiscale Dense Generative Adversarial Network. [paper][code]
• Li et al, A Fusion Adversarial Underwater Image Enhancement Network with a Public Test Dataset. [paper][code]
• Hashisho et al, Underwater Color Restoration Using U-Net Denoising Autoencoder. [paper][code]
• Ding et al, Jointly Adversarial Network to Wavelength Compensation and Dehazing of Underwater Images. [paper][code]
• Park et al, Adaptive Weighted Multi-Discriminator CycleGAN for Underwater Image Enhancement. [paper][code]
• Liu et al, Real-world Underwater Enhancement: Challenges, Benchmarks, and Solutions. [paper][code]

2018

• Fabbri et al, Enhancing Underwater Imagery using Generative Adversarial Networks. [paper][code]
• Ancuti et al, Color Balance and Fusion for Underwater Image Enhancement. [paper][code]
• Yu et al, Underwater-GAN: Underwater Image Restoration via Conditional Generative Adversarial Network. [paper][code]
• Zhang et al, Underwater image enhancement via extended multi-scale Retinex. [paper][code]
• Li et al, WaterGAN: Unsupervised Generative Network to Enable Real-Time Color Correction of Monocular Underwater Images. [paper][code]
• Akkaynak et al, A Revised Underwater Image Formation Model. [paper][code]

3 Image Quality Assessment Metrics

• PSNR (Peak Signal-to-Noise Ratio) [matlab code] [python code]
• SSIM (Structural Similarity) [matlab code] [python code]

4 Note

• The above content is constantly updated, welcome continuous attention!

5 Contact

• If you have any question, please feel free to contact me.