SCN: A Novel Underwater Images Enhancement Method Based on Single Channel Network Model

Abstract

Light is absorbed, reflected, and refracted in an underwater environment due to the interaction between water and light. The red and blue channels in an image are attenuated due to these interactions. The red, green, and blue channels are typically employed as inputs for deep learning models, and the color casts, which result from different attenuation rates of the three channels, may affect the model's generalization performance. Besides, the color casts existing in the reference images will impact the deep-learning models. To address these challenges, a single channel network (SCN) model is introduced, which exclusively employs the green channel as its input, and is unaffected by the attenuations in the red and blue channels. An innovative feature processing module is presented, in which the characteristics of transformers and convolutional layers are fused to capture nonlinear relationships among the red, green, and blue channels. The public EUVP and LSUI data set experiments show that the proposed SCN model achieves competitive results with the existing best three channel models for the case of slight signal attenuation, and outperforms the existing state of arts three-channel models for the case of strong signal attenuation. Furthermore, the proposed model is trained on the self-built noncolor biased underwater image data set and is also tested on the public UCCS data set with three different types of color casts, whose experimental results exhibit balanced color distribution.