Abstract
In this paper we propose a learning-based restoration approach to learn the optimal parameters for enhancing the quality of different types of underwater images and apply a set of intensity transformation techniques to process raw underwater images. The methodology comprises two steps. Firstly, a Convolutional Neural Network (CNN) Regression model is employed to learn enhancing parameters for each underwater image type. Trained on a diverse dataset, the CNN captures complex relationships, enabling generalization to various underwater conditions. Secondly, we apply intensity transformation techniques to raw underwater images. These transformations collectively compensate for visual information loss due to underwater degradation, enhancing overall image quality. In order to evaluate the performance of our proposed approach, we conducted qualitative and quantitative experiments using well-known underwater image datasets (U45 and UIEB), and using the proposed challenging dataset composed by 276 underwater images from the Amazon region (AUID). The results demonstrate that our approach achieves an impressive accuracy rate in different underwater image datasets. For U45 and UIEB datasets, regarding PSNR and SSIM quality metrics, we achieved 26.967, 0.847, 27.299 and 0.793, respectively. Meanwhile, the best comparison techniques achieved 26.879, 0.831, 27.157 and 0.788, respectively.
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Acknowledgements
This work was developed with support from Motorola, through the IMPACT-Lab R &D project at the Institute of Computing (IComp) of the Federal University of Amazonas (UFAM).
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Laura A. Martinho was responsible for the general design and development of the learning-based underwater image restoration approach. João M. B. Cavalcanti, José L. S. Pio and Felipe G. Oliveira have supervised the work and conducted the writing and revision of the paper. Felipe G. Oliveira was also responsible for the Amazon Underwater Image Dataset (AUID) acquisition.
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Martinho, L.A., Calvalcanti, J.M.B., Pio, J.L. et al. Diving into Clarity: Restoring Underwater Images using Deep Learning. J Intell Robot Syst 110, 32 (2024). https://doi.org/10.1007/s10846-024-02065-8
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DOI: https://doi.org/10.1007/s10846-024-02065-8