Title:Underwater Image Enhancement based on Retinex Decomposition and Unsupervised Generative Adversarial Networks
Volume: 19
Issue: 7
Author(s): Yong Lai, Xuebo Zhang, Zhouyan He, Yang Song, Ting Luo and Haiyong Xu*
Affiliation:
- School of Mathematics and Statistics, Ningbo University, Ningbo, China
Keywords:
Retinex decomposition, unsupervised, underwater image enhancement, reflectance image, illumination image, generative adversarial networks.
Abstract:
Background: Due to the difficulty of obtaining the real dataset of paired underwater
images, it is urgent to build an unsupervised underwater image enhancement network.
Objective: To address the problem, a novel underwater image enhancement based on Retinex decomposition
and Unsupervised Generative Adversarial Network (RUGAN) is proposed.
Method: A color correction module is proposed considering the different color distortions of underwater
images. Further, considering the human visual perception mechanism, the RUGAN network,
which is similar to U-Net, is constructed using the characteristics of underwater imaging
and Retinex decomposition. Based on Retinex decomposition and the characteristics of underwater
imaging, the RUGAN network similar to U-Net is constructed. The reflectance image and illumination
image are obtained. The reflectance image with a better effect is taken as the enhancement
result. Unlike the previous supervised methods, RUGAN adopts clear air images and
distorted underwater images as training. RUGAN adopts the underwater image of the color correction
module as pseudo-ground truth to achieve an unsupervised effect.
Results: The superiority of RUGAN network is further supported by extensive experiments that
compared it with more methods.
Conclusion: The RUGAN performs well both subjectively and objectively.