Accurate super-resolution with residual network and cross stage partial network for sonar images

Junwei Yu; Min Yao

doi:10.1117/12.2652237

10 November 2022 Accurate super-resolution with residual network and cross stage partial network for sonar images

Junwei Yu, Min Yao

Proceedings Volume 12331, International Conference on Mechanisms and Robotics (ICMAR 2022); 123312Y (2022) https://doi.org/10.1117/12.2652237
Event: International Conference on Mechanisms and Robotics (ICMAR 2022), 2022, Zhuhai, China

Abstract

With the development of deep learning, deep neural network can be consumed to many kinds of tasks, such as target detection, super resolution and other image processing applications. However, super resolution for sonar images adopts traditional ways without neural network. Due to the characteristic of sonar sensors, which shoot images in turbid underwater environment and have low resolution, objects taken by sonar sensors are difficult to recognize. This paper proposes an efficient method called Multi-stage Residual Network (MRN) which combines neural network to achieve super resolution for sonar images. Adding pixel shuffle to the medium of the structure, the number of layers and blocks in every part is different. In addition, we trained the network with no modification from underwater sonar images. Experimental results show that low-resolution fuzzy images can acquire a clear super resolution by using our model, and furthermore, PSNR of our resultant images is higher than that of the interpolation algorithms and residual network.

Citation Download Citation

Junwei Yu and Min Yao "Accurate super-resolution with residual network and cross stage partial network for sonar images", Proc. SPIE 12331, International Conference on Mechanisms and Robotics (ICMAR 2022), 123312Y (10 November 2022); https://doi.org/10.1117/12.2652237

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KEYWORDS

Super resolution

Image resolution

Sensors

Neural networks

Reconstruction algorithms

Feature extraction

Convolution

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