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

Dujuan Zhou, Zhanchuan Cai () and Dan He
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Dujuan Zhou: School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, Macau 999078, 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

Mathematics, 2024, vol. 12, issue 9, 1-16

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.

Keywords: underwater image enhancement; K-layer network; wavelet decomposition (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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