Optical Image-to-Underwater Small Target Synthetic Aperture Sonar Image Translation Algorithm Based on Improved CycleGAN

LI Bao-qi; HUANG Hai-ning; LIU Ji-yuan; LI Yu

doi:10.12263/DZXB.20200712

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Optical Image-to-Underwater Small Target Synthetic Aperture Sonar Image Translation Algorithm Based on Improved CycleGAN

PAPERS | 更新时间：2025-12-08

- Optical Image-to-Underwater Small Target Synthetic Aperture Sonar Image Translation Algorithm Based on Improved CycleGAN
- ACTA ELECTRONICA SINICA Vol. 49, Issue 9, Pages: 1746-1753(2021)
- 作者机构：
  
  1.中国科学院声学研究所，北京 100190
  2.中国科学院先进水下信息技术重点实验室，北京 100190
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20200712
  CLC： TP391
- Received：14 July 2020，
  
  Revised：2021-05-17，
  
  Published：25 September 2021
- 稿件说明：
移动端阅览
李宝奇,黄海宁,刘纪元等.基于改进CycleGAN的光学图像迁移生成水下小目标合成孔径声纳图像算法研究[J].电子学报,2021,49(09):1746-1753.

LI Bao-qi,HUANG Hai-ning,LIU Ji-yuan,et al.Optical Image-to-Underwater Small Target Synthetic Aperture Sonar Image Translation Algorithm Based on Improved CycleGAN[J].ACTA ELECTRONICA SINICA,2021,49(09):1746-1753.
李宝奇,黄海宁,刘纪元等.基于改进CycleGAN的光学图像迁移生成水下小目标合成孔径声纳图像算法研究[J].电子学报,2021,49(09):1746-1753. DOI： 10.12263/DZXB.20200712.

LI Bao-qi,HUANG Hai-ning,LIU Ji-yuan,et al.Optical Image-to-Underwater Small Target Synthetic Aperture Sonar Image Translation Algorithm Based on Improved CycleGAN[J].ACTA ELECTRONICA SINICA,2021,49(09):1746-1753. DOI： 10.12263/DZXB.20200712.

摘要

针对循环生成对抗网络CycleGAN（Cycle Generative Adversarial Networks）在光学图像迁移生成水下小目标合成孔径声纳图像过程中存在质量差和速度慢的问题，本文提出一种新的特征提取单元SDK（Selective Dilated Kernel），并利用SDK设计了一个新的生成器网络SDKNet.与此同时，提出了一种新的循环一致损失函数MS-CCLF（Multiscale Cyclic Consistent Loss Function），MS-CCLF增加了图像多尺度结构相似性约束.在自建的图像迁移数据集OPT-SAS上，本文SM-CycleGAN（Selective and Multiscale Cycle Generative Adversarial Networks）比原始CycleGAN的图像迁移质量提升4.64%，生成器网络参数降低4.13MB

运算时间减少0.143s.实验结果表明，SM-CycleGAN更适合水下小目标光学图像到合成孔径声纳图像的迁移任务.

Abstract

The original CycleGAN show poor quality and time consuming in optical image to underwater small target synthetic aperture sonar image translation task. To address those problems

a novel convolution building block

SDK (Selective Dilated Kernel)

is proposed. By stacking SDK blocks

a generator SDKNet is created. At the same time

Multiscale Cycle Consistent Loss Function (MS-CCLF) is proposed

which add the Multiscale Structural Similarity Index (MS-SSIM) between input images and reconstructed images. On our image translation dataset (OPT-SAS)

the classification accuracy of our SM-CycleGAN is 4.64% higher than that of original CycleGAN. The generator parameters of SM-CycleGAN is 4.13MB lower than that of CycleGAN

and the time consuming of SM-CycleGAN is 0.143s less than that of CycleGAN. The experimental results show that SM -CycleGAN is more suitable for the translation task of optical image to small underwater target synthetic aperture sonar image.

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