Laser & Optoelectronics Progress, Volume. 58, Issue 16, 1610005(2021)
Research on Multi-Frame Sea Surface Image Super Resolution Algorithm Based on Deep Learning
In recent years, deep learning has made a great achievement in image super-resolution reconstruction. Due to the complex ocean environment, the traditional image super-resolution algorithm has some problems, such as the difficulty in adjusting parameters. In addition, the single frame image super-resolution algorithm has an ill conditioned recovery and the generated pixels are uncertain. In this paper, a super-resolution reconstruction algorithm of multi-frame images is proposed for the study of sea surface image reconstruction. The convolution neural network in deep learning is used to learn the mapping relationship between multi-frame low-resolution images and high-resolution images, so as to realize super-resolution reconstruction. At the same time, because the ocean monitoring imaging system needs more high-frequency information to identify the target and lock contour, the residual network framework is proposed to improve the quality of network reconstruction images, recover more high-frequency information and enrich the image details. The experimental results show that the proposed algorithm has a better image reconstruction ability and better subjective and objective evaluation results compared with other methods.
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Yushuang Zhang, Wenbo Han, Danfei Huang, Liying Zhao, Aiqi Zhong. Research on Multi-Frame Sea Surface Image Super Resolution Algorithm Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2021, 58(16): 1610005
Category: Image Processing
Received: Nov. 6, 2020
Accepted: Dec. 8, 2020
Published Online: Aug. 19, 2021
The Author Email: Han Wenbo (hanwenbo@cust.edu.cn), Huang Danfei (huangd_f@163.com)