Underwater Image Enhancement With Reinforcement Learning

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Abstract
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.
Funding Information
  • National Natural Science Foundation of China (61971444)
  • Innovative Research Team Program for Young Scholars at Universities in Shandong Province (2020KJN010)
  • Shandong Provincial Key R&D Program of China (2016GGX4312)
  • Key Program of Marine Economy Development Special Foundation of Department of Natural Resources of Guangdong Province ([2020]012)
  • Graduate Innovation Project of the China University of Petroleum (YCX2021125)

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