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An Integrated Approach of Conventional and Deep Learning Method for Underwater Image Enhancement

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Soft Computing for Intelligent Systems

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

Underwater imaging has proved its significance in underwater environment monitoring, inspection and providing security. For underwater imaging, large amount of data need to be exchanged between underwater sensors and terrestrial-monitoring device. Sensors deployed underwater for a long time must face the difficulty of having their battery recharged. Underwater images are affected by several underwater environmental factors such as water turbidity, light scattering, light absorption and light reflection. There are many conventional and deep learning techniques that help to compress underwater images for fast transmission. The conventional methods are affected by ‘blocking’ and ‘contouring’ artifacts and require complex computations. The deep learning methods need large dataset for training purpose. To overcome the drawbacks of the existing techniques, this paper proposed a method that provides an energy efficient, free learning convolutional neural compression—decompression network with a pre-processing step. The advantage of the proposed method is that the compression framework is solely dependent on untrained convolutional neural network structure and the pre-processed image. Experiments on three different datasets having different image characteristics accomplish the motive of the proposed method. The datasets considered for comparison are Fish dataset, USR-248 dataset and UIEB dataset. Findings of the study show that the proposed method provides better PSNR results for Fish dataset and USR-248 dataset but lower PSNR values for UIEB dataset when compared with SRCNN and Bi-cubic methods. But on visual examination, color and contrast correction in UIEB dataset using proposed method are better than SRCNN and Bi-cubic methods. This study enables researchers and practitioners to get refined reconstructed image at the receiver side by adopting the presented method.

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Author information

Authors and Affiliations

  1. Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Tamil Nadu, 620015, India

    Rashmi S. Nair

  2. Department of Production Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, 620015, India

    Rohit Agrawal

Authors
  1. Rashmi S. Nair
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  2. Rohit Agrawal
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Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Kurukshetra University, Kurukshetra, Haryana, India

    Nikhil Marriwala

  2. University Institute of Engineering and Technology (UIET), Kurukshetra University, Kurukshetra, Haryana, India

    C. C Tripathi

  3. Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India

    Shruti Jain

  4. Co-Founder of Dew Mobility, Santa Clara University, Santa Clara, CA, USA

    Shivakumar Mathapathi

Appendix

Appendix

See Tables 4, 5 and 6.

Table 4 Comparison of result for Fish-dataset with SRCNN and Bi-cubic methods
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Table 5 Comparison of result for USR-248 dataset with SRCNN and Bi-cubic methods
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Table 6 Comparison of results for UIEB dataset with SRCNN and Bi-cubic methods
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Nair, R.S., Agrawal, R. (2021). An Integrated Approach of Conventional and Deep Learning Method for Underwater Image Enhancement. In: Marriwala, N., Tripathi, C.C., Jain, S., Mathapathi, S. (eds) Soft Computing for Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-1048-6_14

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