Underwater image enhancement algorithm based on multi-scale block cascade

HAO Jun-yu; YANG Hong-bo; HOU Xia; ZHANG Yang

doi:10.37188/CJLCD.2022-0352

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Underwater image enhancement algorithm based on multi-scale block cascade

Image Processing | 更新时间：2023-09-27

- Underwater image enhancement algorithm based on multi-scale block cascade
- Chinese Journal of Liquid Crystals and Displays Vol. 38, Issue 9, Pages: 1272-1280(2023)
- 作者机构：
  
  1.北京信息科技大学自动化学院，北京 100089
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62001035);the Scientific Research Program of Beijing Municipal(KM202111232018)
- DOI：10.37188/CJLCD.2022-0352
  CLC：
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HAO Jun-yu, YANG Hong-bo, HOU Xia, et al. Underwater image enhancement algorithm based on multi-scale block cascade. [J]. Chinese Journal of Liquid Crystals and Displays 38(9):1272-1280(2023)
DOI：

HAO Jun-yu, YANG Hong-bo, HOU Xia, et al. Underwater image enhancement algorithm based on multi-scale block cascade. [J]. Chinese Journal of Liquid Crystals and Displays 38(9):1272-1280(2023) DOI： 10.37188/CJLCD.2022-0352.

摘要

由于水中悬浮物的散射以及水体对光的吸收，水下图像有严重的色偏、雾化以及模糊现象。针对现有基于深度学习的水下图像增强算法使用单一的卷积和上下采样方式，导致图像特征提取不充分的问题，本文构建了基于多尺度特征提取的下采样模块、上采样模块和特征提取模块，并在此基础上提出了一个基于多尺度特征提取块级联（MS-FEBC）的水下图像增强网络框架。为进一步提高网络的特征提取能力，在网络高维特征空间中添加了CBAM注意力机制。实验结果表明，与现有算法相比，本文算法有效解决了水下图像存在色偏、雾化和细节丢失等质量较低的问题，在4种客观评价指标上均有显著提升，对图像SIFT特征点检测和Canny边缘检测视觉任务的性能有明显提高。

Abstract

Underwater images often suffer from severe color degradation， haze and local blur， which are attenuated by the scattering of suspended objects in water and the absorption of light by water. Aiming at the problem that the existing underwater image enhancement algorithms based on deep learning use a single convolution， up-sampling and down-sampling mode which leads to insufficient image feature extraction， this paper constructs the down-sampling module， up-sampling module and feature extraction module based on multi-scale feature extraction. On this basis， an underwater image enhancement network framework based on multi-scale feature extraction block cascade （MS-FEBC） is proposed. To further improve the feature extraction capability of the network， the CBAM attention mechanism is added to the high-dimensional feature space of the network. The experimental results demonstrate that compared with the existing algorithms， the algorithm in this paper effectively solves the problem that the underwater images have lower quality such as color-cast， hazing and detail loss. There is a significant improvement in all four objective evaluation indexes. The performance of the image SIFT feature point detection and Canny edge detection vision tasks is significantly improved.

关键词

水下图像增强级联网络多尺度特征提取

Keywords

underwater image enhancementcascaded networkmulti-scale feature extraction

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