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Improving the quality of underwater imaging using deep convolution neural networks

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Abstract

Enhancing the quality of images captured underwater plays a key role in underwater computer vision applications. The underwater environment has attracted attention from scientists working on ocean mammals, shipwrecks, immersed cave structures, subaqueous studies, invertebrates, and geological features. Because of the underwater environment, undersea images suffer from issues such as light attenuation, color absorption, and variations with the type of water. A deep learning-based network known as the smart deep convolution neural network (SDCNN) is proposed herein. The model is divided into three subnetworks that address the white balance (WB-Net), histogram equalization (HE-Net), and edge sharpening (ES-Net). These three networks were trained using real-world underwater images, considering color cast correction, dehazing, and edge sharpening. The experimental results demonstrated that, using the proposed method, the visual quality of underwater images can be enhanced. The method uses traditional image enhancement methods with a deep convolution neural network.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Rajarambapu Institute of Technology, Islampur, Sakharale, Islampur, Maharashtra, India

    Nagaraj V. Dharwadkar & Anjali M.Yadav

  2. Department of Electrical Engineering, Imam Mohammad Ibn Saud Islamic University, Riyadh, Kingdom of Saudi Arabia

    Mohammad Ali Kadampur

Authors
  1. Nagaraj V. Dharwadkar
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  2. Anjali M.Yadav
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  3. Mohammad Ali Kadampur
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Correspondence to Nagaraj V. Dharwadkar.

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Dharwadkar, N.V., M.Yadav, A. & Kadampur, M.A. Improving the quality of underwater imaging using deep convolution neural networks. Iran J Comput Sci 5, 127–141 (2022). https://doi.org/10.1007/s42044-021-00093-3

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  • DOI: https://doi.org/10.1007/s42044-021-00093-3

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