SE-RWNN: an synergistic evolution and randomly wired neural network-based model for adaptive underwater image enhancement

作     者：Li, Yang Chen, Rong 

作者机构：Dalian Maritime Univ Coll Informat Sci & Technol Dalian Peoples R China 

出 版 物：《IET IMAGE PROCESSING》 (IET影像处理)

年 卷 期：2020年第14卷第16期

页      面：4349-4358页

核心收录：

学科分类：0808[工学-电气工程] 1002[医学-临床医学] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China [61672122, 61402070, 61602077, 61602050] Fundamental Research Funds for the Central Universities Next-Generation Internet Innovation Project of CERNET [NGII20181205] 

主　　题：image enhancement image denoising neural nets image colour analysis oceanographic techniques geophysical image processing edge detection SE-RWNN synergistic evolution neural network-based model adaptive underwater image enhancement water images severe degradation colour distortion fuzz content absorption scattering effects visual appearance adaptive algorithm effective underwater image enhancement randomly wired neural network colours adjustment contrast improvement luminance enhancement edge-preserving technique multistrategy cooperating evolution algorithm underwater images 

摘      要：Under water images are likely to suffer from severe degradation such as colour distortion, low contrast, and fuzz content, caused by the absorption and scattering effects of the water. To improve the visual appearance of the image, the authors present an adaptive algorithm for effective underwater image enhancement using a randomly wired neural network (RWNN) and synergistic evolution (SE). In doing so, they sequentially conduct colours adjustment, contrast improvement and luminance enhancement while enhancing details by an edge-preserving technique. To set up the system, they develop a multi-strategy cooperating evolution algorithm to figure out the optimal parameter values. Extensive experimental results show that the proposed model improves both subjectively and quantitatively the quality of underwater images.