Underwater Image Restoration Based on a Parallel Convolutional Neural Network

• Published in: Remote sensing (Basel, Switzerland) Vol. 11; no. 13; p. 1591
• Main Authors: Wang, Keyan, Hu, Yan, Chen, Jun, Wu, Xianyun, Zhao, Xi, Li, Yunsong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2019
• Subjects: Artificial neural networks; Color; convolutional neural network; data collection; Datasets; image analysis; Image contrast; image degradation; Image enhancement; Image processing; Image restoration; Light; Methods; Neural networks; Parameter estimation; Pattern recognition; Quality; Remote sensing; Underwater; underwater imaging; Visibility; United States > US; China
• DOI: 10.3390/rs11131591
• ISSN: 2072-4292

Restoring degraded underwater images is a challenging ill-posed problem. The existing prior-based approaches have limited performance in many situations due to the reliance on handcrafted features. In this paper, we propose an effective convolutional neural network (CNN) for underwater image restoration. The proposed network consists of two paralleled branches: a transmission estimation network (T-network) and a global ambient light estimation network (A-network); in particular, the T-network employs cross-layer connection and multi-scale estimation to prevent halo artifacts and to preserve edge features. The estimates produced by these two branches are leveraged to restore the clear image according to the underwater optical imaging model. Moreover, we develop a new underwater image synthesizing method for building the training datasets, which can simulate images captured in various underwater environments. Experimental results based on synthetic and real images demonstrate that our restored underwater images exhibit more natural color correction and better visibility improvement against several state-of-the-art methods.