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A New Biorthogonal Spline Wavelet-Based K-Layer Network for Underwater Image Enhancement

Author

Listed:
  • Dujuan Zhou

    (School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, Macau 999078, China
    School of Applied Science and Civil Engineering, Beijing Institute of Technology, Zhuhai 519088, China)

  • Zhanchuan Cai

    (School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, Macau 999078, China)

  • Dan He

    (School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, Macau 999078, China
    School of Artificial Intelligence, Dongguan City University, Dongguan 523109, China)

Abstract

Wavelet decomposition is pivotal for underwater image processing, known for its ability to analyse multi-scale image features in the frequency and spatial domains. In this paper, we propose a new biorthogonal cubic special spline wavelet (BCS-SW), based on the Cohen–Daubechies–Feauveau (CDF) wavelet construction method and the cubic special spline algorithm. BCS-SW has better properties in compact support, symmetry, and frequency domain characteristics. In addition, we propose a K-layer network (KLN) based on the BCS-SW for underwater image enhancement. The KLN performs a K-layer wavelet decomposition on underwater images to extract various frequency domain features at multiple frequencies, and each decomposition layer has a convolution layer corresponding to its spatial size. This design ensures that the KLN can understand the spatial and frequency domain features of the image at the same time, providing richer features for reconstructing the enhanced image. The experimental results show that the proposed BCS-SW and KLN algorithm has better image enhancement effect than some existing algorithms.

Suggested Citation

  • Dujuan Zhou & Zhanchuan Cai & Dan He, 2024. "A New Biorthogonal Spline Wavelet-Based K-Layer Network for Underwater Image Enhancement," Mathematics, MDPI, vol. 12(9), pages 1-16, April.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:9:p:1366-:d:1386569
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    References listed on IDEAS

    as
    1. Shuqi Wang & Huajun Zhang & Xuetao Zhang & Yixin Su & Zhenghua Wang, 2023. "Voiceprint Recognition under Cross-Scenario Conditions Using Perceptual Wavelet Packet Entropy-Guided Efficient-Channel-Attention–Res2Net–Time-Delay-Neural-Network Model," Mathematics, MDPI, vol. 11(19), pages 1-20, October.
    2. Abdellah Lamnii & Mohamed Yassir Nour & Ahmed Zidna, 2021. "A Reverse Non-Stationary Generalized B-Splines Subdivision Scheme," Mathematics, MDPI, vol. 9(20), pages 1-16, October.
    3. Dachang Zhu, 2023. "Underwater Image Enhancement Based on the Improved Algorithm of Dark Channel," Mathematics, MDPI, vol. 11(6), pages 1-11, March.
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