Coarse-to-fine underwater image enhancement based on multi-level wavelet transform

YUAN Guoming; YANG Guang; WANG Jinfeng; LIU Haijun; WANG Wei

doi:10.37188/OPE.20223022.2939

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Coarse-to-fine underwater image enhancement based on multi-level wavelet transform

Information Sciences | 更新时间：2022-11-24

- Coarse-to-fine underwater image enhancement based on multi-level wavelet transform
- Optics and Precision Engineering Vol. 30, Issue 22, Pages: 2939-2951(2022)
- 作者机构：
  
  1.防灾科技学院应急管理学院，河北三河 065201
  2.防灾科技学院信息工程学院，河北三河 065201
- 作者简介：
  
  E-mail： yangg202205@163.com
- 基金信息：
- DOI：10.37188/OPE.20223022.2939
  CLC： TP394.11
- Received：19 May 2022，
  
  Revised：17 June 2022，
  
  Published：25 November 2022
- 稿件说明：
移动端阅览
袁国铭,杨光,王金峰等.由粗到细的多级小波变换水下图像增强[J].光学精密工程,2022,30(22):2939-2951.

YUAN Guoming,YANG Guang,WANG Jinfeng,et al.Coarse-to-fine underwater image enhancement based on multi-level wavelet transform[J].Optics and Precision Engineering,2022,30(22):2939-2951.
袁国铭,杨光,王金峰等.由粗到细的多级小波变换水下图像增强[J].光学精密工程,2022,30(22):2939-2951. DOI： 10.37188/OPE.20223022.2939.

YUAN Guoming,YANG Guang,WANG Jinfeng,et al.Coarse-to-fine underwater image enhancement based on multi-level wavelet transform[J].Optics and Precision Engineering,2022,30(22):2939-2951. DOI： 10.37188/OPE.20223022.2939.

摘要

为实现水下降质图像的颜色校正和细节增强，提出了由粗到细的多级小波变换水下图像增强方法。首先，通过多级小波变换将水下图像分解为低频图像和一系列高频图像；随后，提出了由粗到细的多级小波变换水下图像增强网络，它包含基于多级小波变换的图像增强子网络和二阶龙格库塔模块构建的细化子网络。基于多级小波变换的图像增强子网络用于估计水下图像增强的初步结果，它包括低频分支和高频分支。低频分支校正低频图像的颜色信息，它将颜色校正问题看作隐式的类型转化问题，并将类型转化中常用的实例归一化和位置归一化用于低频分支的颜色校正中。高频分支为了适应低频分支的操作，如实地增强高频图像的边缘和细节，联合低频分支处理时的信息以及高频图像，计算增强高频细节的掩模；逐步上采样掩模，并将其与各级小波变换获取的高频图像相乘，以增强细节。实施逆小波变换，获得初步增强的水下图像；最后，设计基于二阶龙格库塔模块的细化子网络，对初步增强的结果进一步细化。实验结果表明：在合成和真实水下图像上，本文算法较已有的水下图像增强算法，具有更好的增强效果，PSNR值的提升幅度达9%。满足水下视觉任务的颜色校正、细节增强、清晰化等要求。

Abstract

To correct the color distortion and enhance the details of degraded underwater image， this paper proposes a coarse-to-fine underwater image enhancement method based on multi-level wavelet transform. Firstly， a raw underwater image is decomposed into a low-frequency image and a series of high-frequency images based on the wavelet transform. Secondly， a two-stage underwater enhancement network is proposed， which includes a multi-level wavelet transform sub-network and a refinement sub-network with the proposed second-order Runge-Kutta block. The multi-level wavelet transform sub-network， which estimates preliminary result， contains a low-frequency and a high-frequency branch. Specifically， the low-frequency branch treats the color correction problem as the implicit style transfer problem and introduces the instance normalization and the position normalization into the branch. To ensure an accurate reconstruction， when manipulating low-frequency information， the high-frequency branch calculates the enhanced mask according to the information from both low- and high-frequency images and implements the enhancement by multiplying the progressive up-sampling enhanced mask with the high-frequency images. We implemented the inverse wavelet transform and obtain the preliminary results. Finally， the refinement network was designed to further optimize the preliminary results with the proposed second-order Runge-Kutta block. Experimental results demonstrated that the proposed method outperformed the existing methods in enhancement effect on both synthetic and real images， whilst the Peak Signal-to-Noise Ratio （PSNR） improved by 9%. The proposed method also meets the requirement of underwater vision tasks， such as color correction， details enhancement， and clarity.

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