Open AccessSE–RWNN: an synergistic evolution and randomly wired neural network‐based model for adaptive underwater image enhancement
Author(s) -
Li Yang,
Chen Rong
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.1677
Subject(s) - underwater , luminance , computer science , distortion (music) , artificial intelligence , contrast (vision) , image (mathematics) , enhanced data rates for gsm evolution , artificial neural network , image enhancement , set (abstract data type) , image restoration , computer vision , degradation (telecommunications) , algorithm , image processing , telecommunications , geology , oceanography , amplifier , bandwidth (computing) , programming language
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.