采用改进初始化结构的侧扫声纳图像超分辨率

IF 0.2 Q4 ACOUSTICS
Junyeop Lee, Bonhwa Ku, Wanjin Kim, Hanseok Ko
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引用次数: 0

摘要

本文提出了一种利用基于学习的压缩传感提高侧扫声纳图像分辨率的超分辨率方法。基于学习的压缩感知与深度学习和压缩感知相结合,采用前馈网络的结构,并通过学习自动设置参数。特别是,我们提出了一种方法,可以通过各种初始化方法有效地提取超分辨率过程中所需的附加信息。代表性的实验结果表明,与传统方法相比,该方法在峰值信噪比(PSNR)和结构相似性指数测量(SSIM)方面具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Side scan sonar image super-resolution using an improved initialization structure
This paper deals with a super-resolution that improves the resolution of side scan sonar images using learning-based compressive sensing. Learning-based compressive sensing combined with deep learning and compressive sensing takes a structure of a feed-forward network and parameters are set automatically through learning. In particular, we propose a method that can effectively extract additional information required in the super-resolution process through various initialization methods. Representative experimental results show that the proposed method provides improved performance in terms of Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) than conventional methods.
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来源期刊
CiteScore
0.60
自引率
50.00%
发文量
1
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