Underwater Optical Image Processing: a Comprehensive Review

Lu, Huimin; Li, Yujie; Zhang, Yudong; Chen, Min; Serikawa, Seiichi; Kim, Hyoungseop

doi:10.1007/s11036-017-0863-4

Cited by 173 publications

(85 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Numerous attempts have been made with different image improvement methods for restoring the color of raw underwater images. These methods fall into two categories [6]:…”

Section: Related Workmentioning

confidence: 99%

Underwater Color Restoration Using U-Net Denoising Autoencoder

Hashisho

Albadawi

Krause

et al. 2019

2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA)

View full text Add to dashboard Cite

Visual inspection of underwater structures by vehicles, e.g. remotely operated vehicles (ROVs), plays an important role in scientific, military, and commercial sectors. However, the automatic extraction of information using software tools is hindered by the characteristics of water which degrade the quality of captured videos. As a contribution for restoring the color of underwater images, Underwater Denoising Autoencoder (UDAE) model is developed using a denoising autoencoder with U-Net architecture. The proposed network takes into consideration the accuracy and the computation cost to enable realtime implementation on underwater visual tasks using end-toend autoencoder network. Underwater vehicles perception is improved by reconstructing captured frames; hence obtaining better performance in underwater tasks. Related learning methods use generative adversarial networks (GANs) to generate color corrected underwater images, and to our knowledge this paper is the first to deal with a single autoencoder capable of producing same or better results. Moreover, image pairs are constructed for training the proposed network, where it is hard to obtain such dataset from underwater scenery. At the end, the proposed model is compared to a state-of-the-art method.

show abstract

“…Numerous attempts have been made with different image improvement methods for restoring the color of raw underwater images. These methods fall into two categories [6]:…”

Section: Related Workmentioning

confidence: 99%

Underwater Color Restoration Using U-Net Denoising Autoencoder

Hashisho

Albadawi

Krause

et al. 2019

2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA)

View full text Add to dashboard Cite

show abstract

“…Generally, there are four ways to enhance underwater images: enhancement in the spatial domain [8,9], enhancement in the frequency domain, color-constancy-based enhancement, and multi-method-based enhancement [10][11][12].…”

Section: Classical Modelsmentioning

confidence: 99%

An Underwater Image Enhancement Algorithm for Environment Recognition and Robot Navigation

Xie

Pan

2018

Robotics

View full text Add to dashboard Cite

There are many tasks that require clear and easily recognizable images in the field of underwater robotics and marine science, such as underwater target detection and identification of robot navigation and obstacle avoidance. However, water turbidity makes the underwater image quality too low to recognize. This paper proposes the use of the dark channel prior model for underwater environment recognition, in which underwater reflection models are used to obtain enhanced images. The proposed approach achieves very good performance and multi-scene robustness by combining the dark channel prior model with the underwater diffuse model. The experimental results are given to show the effectiveness of the dark channel prior model in underwater scenarios.

show abstract

“…Moreover, such absorption and scattering effects are hard to be explicitly modeled, as they are relevant to many factors, such as water temperature, salinity, and types of particles, etc. This complex degradation makes it challenging to restore the visibility and color of underwater images [5]. Restoring underwater images with natural colors and fine details still remains an open problem [6].…”

Section: Introductionmentioning

confidence: 99%

MLFcGAN: Multilevel Feature Fusion-Based Conditional GAN for Underwater Image Color Correction

Liu

Gao

Chen

2020

IEEE Geosci. Remote Sensing Lett.

View full text Add to dashboard Cite

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 multiscale 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 in 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-arts approaches quantitatively and qualitatively, showing that our method achieves significant improvements.Index Terms-Underwater image color correction, image enhancement, feature extraction and fusion, conditional GAN.

show abstract

Underwater Optical Image Processing: a Comprehensive Review

Cited by 173 publications

References 44 publications

Underwater Color Restoration Using U-Net Denoising Autoencoder

Underwater Color Restoration Using U-Net Denoising Autoencoder

An Underwater Image Enhancement Algorithm for Environment Recognition and Robot Navigation

MLFcGAN: Multilevel Feature Fusion-Based Conditional GAN for Underwater Image Color Correction

Contact Info

Product

Resources

About