Underwater image enhancement using adaptive standardization and normalization networks.

This paper proposes a machine learning-based underwater image enhancement scheme using an adaptive standardization network and normalization network. The adaptive standardization network is designed to match the distribution of input features. This helps correct the distorted distribution of underwater images and facilitates training. The proposed adaptive normalization network is constructed using two squeeze-and-excitation blocks and the conventional feature normalization method. It is designed to increase the contrast, remove the hazy effect, and restore the brightness. An improved performance of underwater image enhancement is achieved through an appropriate configuration of the two proposed networks. The structure of the proposed network is simple and therefore requires fewer parameters. The simulation results verify that the proposed underwater image enhancement scheme outperforms other state-of-the-art approaches. The proposed method demonstrates outstanding performance both subjectively and objectively in improving underwater images. The code is available on https://github.com/cwoop92. • This paper presents a machine learning-based underwater image enhancement scheme using an adaptive standardization network and normalization network. • The adaptive standardization network consists of a convolution layer before batch normalization and a sigmoid activation function. It is responsible for matching the histogram of the color channel. • The adaptive normalization network is designed using two modified squeeze-and-excitation blocks to construct an adaptive normalization. The adaptive normalization network can adjust the brightness of an image and enhance its contrast. • The simulation results show that the proposed underwater image enhancement scheme outperforms state-of-the-art approaches regarding computational simplicity and enhancement efficiency. [ABSTRACT FROM AUTHOR]