Underwater image super-resolution reconstruction with local self-similarity analysis and wavelet decomposition
Xiaorun Wang, Rui Nian, Bo He, Bing Zheng, Amaury Lendasse · OCEANS 2017 - Aberdeen · 2017
Underwater target detecting is an important technique for the development of the ocean engineering and exploration also a significant task of the ocean detecting. It plays a substantial role not only for the civil economy but also for the national security. The formation of the super-resolution underwater image is significant topic in ocean detecting field. In order to enhance the visual quality of images obtained by underwater imaging systems, super resolution (SR) reconstruction is introduced, including single-frame and multi-frame SR algorithms. Real-world images often contain singularities such as edges and high-frequency textured regions. As a result, these methods suffer from various edge-related visual artifacts such as ringing, aliasing, jagging, and blurring we mainly focus on super-resolution from single low resolution input image. The goal of single image super-resolution is to estimate a high resolution image from a low resolution input. In this paper, we propose a new high-quality and efficient single-image upscaling technique.