Open Access

ARTICLE

Research on the Application of Super Resolution Reconstruction Algorithm for Underwater Image

Tingting Yang¹, Shuwen Jia¹, Hao Ma^{2, *}

1 University of Sanya, Sanya, 572000, China.
2 1455 Boulevard de Maisonneuve O, Montréal, QC H3G 1M8, Canada.

* Corresponding Author: Tingting Yang. Email: .

Computers, Materials & Continua 2020, 62(3), 1249-1258. https://doi.org/10.32604/cmc.2020.05777

Abstract

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.

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Citations