Underwater image enhancement network based on visual Transformer with multiple loss functions fusion - Xiaofeng CONG; Jie GUI; Jun ZHANG (智能科学与技术学报)
Due to the absorption and scattering of light in water, the images captured by underwater robots suffer from color distortion and reduced contrast.Aiming at alleviating the quality degradation phenomenon of underwater images, an underwater image enhancement network based on vision Transformer that be trained with multiple losses fusion strategy was proposed.The image enhancement network adopted an encoder-decoder architecture, and could be trained in an end-to-end manner.In order to effectively update the parameters of the network for enhancing underwater images, a linear combination of various losses was adopted as the overall optimization objective, including pixel loss, structure loss, edge loss and feature loss.Quantitative experiments were carried out on two large underwater datasets, and the proposed underwater image enhancement network was compared with 7 underwater image enhancement algorithms.The full reference evaluation metrics peak signal-to-noise ratio and structural similarity were calculated in experiment, and the non-referenced metric underwater image quality measure was also computed.The experimental results showed that the proposed underwater image enhancement network could effectively deal with color distortion and contrast reduction.