Deformable Convolutional Network Constrained by Contrastive Learning for Underwater Image Enhancement
Jing Tian, Xinran Guo, Weifeng Liu, Dapeng Tao, Baodi Liu · IEEE Geoscience and Remote Sensing Letters · 2023
Autonomous underwater vehicles (AUVs) based on remote sensing technology have been widely applied in various underwater tasks. However, the complex underwater environment leads to challenges such as color distortion, blurred details, and fog effects in the underwater image directly acquired by AUVs. Although numerous existing methods aim to remove the color cast and restore image details, their effectiveness is still limited. This paper proposes a new method based on a deformable convolutional network and constrained by contrastive learning for underwater image enhancement. First, we propose a deformable convolutional residual block (DCRB) to achieve a more precise restoration of texture details by adaptively adjusting the convolution kernel shape. At the same time, we utilize the long-skip connection method of the U-Net architecture to preserve information that is prone to lose in shallow networks. Second, we propose a color contrastive loss function to compare the color difference between distorted images and the ground truth, resulting in a more realistic enhanced image. Finally, experimental results demonstrate that the proposed method outperforms the state-of-the-art methods regarding image quality and visual appeal.