Research on the Application of Super Resolution Reconstruction Algorithm for Underwater Image
Research on the Application of Super Resolution Reconstruction Algorithm for Underwater Image
复制标题
DOI：
10.32604/cmc.2020.05777
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Tingting Yang;Shuwen Jia;H. Ma
中科院分区：
文献类型：
--
作者：
Tingting Yang;Shuwen Jia;H. Ma
水下成像技术在海洋、河流、湖泊探测中有着广泛的应用，但其受水体性质和光学特性的影响。为了解决水下图像受水体、光照等因素影响而形成的低分辨率图像，将图像超分辨率重建技术应用于水下图像处理中。研究了利用卷积神经网络框架技术生成超分辨率水下图像的问题。研究了水下图像的退化模型，分析了不同情况下水下图像分辨率降低的因素，并对传统的超分辨率图像重建算法进行了比较。我们进一步证明了基于深度卷积网络的超分辨率算法（SRCNN）应用于水下图像的超分辨率处理取得了良好的效果。
: Underwater imaging is widely used in ocean, river and lake exploration, but it is affected by properties of water and the optics. In order to solve the lower-resolution underwater image formed by the influence of water and light, the image super-resolution reconstruction technique is applied to the underwater image processing. This paper addresses the problem of generating super-resolution underwater images by convolutional neural network framework technology. We research the degradation model of underwater images, and analyze the lower-resolution factors of underwater images in different situations, and compare different traditional super-resolution image reconstruction algorithms. We further show that the algorithm of super-resolution using deep convolution networks (SRCNN) which applied to super-resolution underwater images achieves good results.