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Self-learning Methodology Based on Degradation Estimation for Underwater Image Enhancement

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Intelligent Systems (BRACIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13654 ))

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Abstract

Underwater images suffer from degradation caused by water turbidity, light attenuation and color casting. An image enhancement procedure improves the perception and the analysis of the objects in the scene. Recent works based on deep learning approaches require synthetically paired datasets to train their models. In this work, it is present a self-learning methodology to enhance images without a paired dataset. The proposed method estimates image degradation from the input image and attenuate it from the image. The output image of an autoencoder is replaced in the loss function by a synthetically degraded version during the training. This procedure drives the network learning to compensate additional degradation. Thereby, the output image is an enhanced version of the input image. It is highlighted that the proposed algorithm requires only one image as input during the training. The results obtained using our method show its effectiveness of color preservation, color cast reduction, and contrast improvement.

Supported by National Council for Scientific and Technological Development (CNPq), the Coordination for the Improvement of Higher Education Personnel (CAPES) and Agency for Petroleum, Natural Gas, and Biofuels (PRH-ANP).

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Acknowledgments

The authors would like to thank to National Council for Scientific and Technological Development (CNPq) and the Coordination for the Improvement of Higher Education Personnel (CAPES). In addition, the author thanks Human Resource Program of The Brazilian National Agency for Petroleum, Natural Gas, and Biofuels - PRH-ANP, supported with resources from oil companies considering the contract clause no 50/2015 of R, D &I of the ANP.

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Authors and Affiliations

  1. Center for Computer Science, Federal University of Rio Grande, Av. Italia - km8, Rio Grande, RS, 96203-900, Brazil

    Claudio Dornelles Mello Jr., Bryan Umpierre Moreira, Paulo Jefferson Dias de Oliveira Evald, Paulo Jorge Lilles Drews Jr. & Silvia Silva Costa Botelho

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  1. Claudio Dornelles Mello Jr.
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  2. Bryan Umpierre Moreira
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  3. Paulo Jefferson Dias de Oliveira Evald
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  4. Paulo Jorge Lilles Drews Jr.
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  5. Silvia Silva Costa Botelho
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Correspondence to Claudio Dornelles Mello Jr. .

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Editors and Affiliations

  1. Federal University of Rio Grande do Norte, Natal, Brazil

    João Carlos Xavier-Junior

  2. Federal University of Bahia, Salvador, Brazil

    Ricardo Araújo Rios

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Mello Jr., C.D., Moreira, B.U., de Oliveira Evald, P.J.D., Drews, P.J.L., Botelho, S.S.C. (2022). Self-learning Methodology Based on Degradation Estimation for Underwater Image Enhancement. In: Xavier-Junior, J.C., Rios, R.A. (eds) Intelligent Systems. BRACIS 2022. Lecture Notes in Computer Science(), vol 13654 . Springer, Cham. https://doi.org/10.1007/978-3-031-21689-3_7

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  • DOI: https://doi.org/10.1007/978-3-031-21689-3_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21688-6

  • Online ISBN: 978-3-031-21689-3

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