强化学习
人工智能
计算机科学
水下
马尔可夫决策过程
图像质量
图像(数学)
特征(语言学)
编码(集合论)
计算机视觉
钢筋
深度学习
马尔可夫过程
工程类
数学
集合(抽象数据类型)
海洋学
统计
地质学
哲学
结构工程
程序设计语言
语言学
作者
Shixin Sun,Hao Wang,Hao Zhang,Mingjie Li,Ming Xiang,Cai Luo,Peng Ren
出处
期刊:IEEE Journal of Oceanic Engineering
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:: 1-13
被引量:13
标识
DOI:10.1109/joe.2022.3152519
摘要
In this article, we develop an underwater image enhancement framework based on reinforcement learning. To do this, we model the underwater image enhancement as a Markov decision process (MDP), in which states are represented by image feature maps, actions are represented by image enhancement methods, and rewards are represented by image quality improvements. The MDP trained with reinforcement learning can characterize a sequence of enhanced results for an underwater image. At each step of the MDP, a state transitions from one to another according to an action of image enhancement selected by a deep Q network. The final enhanced image in the sequence is obtained with respect to the biggest overall image quality improvement. In this manner, our reinforcement learning framework effectively organizes a sequence of image enhancement methods in a principled manner. In contrast to the black box processing schemes of deep learning methods, our reinforcement learning framework gives a sequence of specific actions, which are transparent from the implementation perspective. Benefiting from the exploration and exploitation training fashion, our reinforcement learning framework possibly generates enhanced images that are of better quality than reference images. Experimental results validate the effectiveness of our reinforcement learning framework in underwater image enhancement. The code and detailed results are available at https://gitee.com/sunshixin_upc/underwater-image-enhancement-with-reinforcement-learning.
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