• Vita
  • Publications
Show publication details

Hashisho, Yousif; Albadawi, Mohamad; Krause, Tom; Lukas, Uwe von

Underwater Color Restoration Using U-Net Denoising Autoencoder

2019

Proceedings of the 11th International Symposium Image and Signal Processing and Analysis

International Symposium on Image and Signal Processing and Analysis (ISPA) <11, 2019, Dubrovnik, Croatia>

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 publication details

Hashisho, Yousif; Lukas, Uwe von [Gutachter]; Staadt, Oliver [Gutachter]; Albadawi, Mohamad [Supervising Advisor]; Krause, Tom [Supervising Advisor]

Underwater Image Enhancement Using Autoencoders

2019

Rostock, Univ., Master Thesis, 2019

Visual inspection of underwater structures by vehicles, e.g. remotely operated vehicles (ROVs), plays an important role in scientific, military, and commercial aspects. However, the automatic extraction of information using software tools is hindered by the optical characteristics of water which degrade the quality of the videos. As a contribution for enhancing underwater images, we develop an algorithm using a single denoising autoencoder to restore the color and remove the disturbances such as marine snow from underwater images. Marine snow in some images is only partially removed using the proposed network; however, we show the reason behind this failure. Related learning methods use generative adversarial networks (GANs) for generating color corrected underwater images, and to our knowledge this thesis is the first to deal with a single autoencoder capable of producing same or better results. Moreover, underwater aligned image pairs are established for the training of the proposed network, where it is hard to obtain such dataset from underwater scenery. The objective is to increase the accuracy and reliability on automatic underwater operations that rely on robotic perception without human interference by improving the quality of the captured frames. At the end, the proposed network is evaluated using Mean Squared Error (MSE), Peak Signal-to- Noise Ratio (PSNR), and Structural Similarity Index Metric (SSIM) quality metrics. Additionally, we compare our experiment with a related method. The proposed network takes into consideration the computation cost and the accuracy to have real-time implementation on visual-driven tasks using a single autoencoder.