Dual Color Space Underwater Image Enhancement Network

In the field of underwater image enhancement, existing methods generally rely heavily on the RGB color space and ignore the potential advantages of the perceptually uniform XYZ color space in color correction. Additionally, CNN-based methods are prone to losing long-distance dependency relationships during local feature extraction process, thus affecting the image restoration quality. To address the above issues, we propose DSCNet, an underwater image enhancement framework based on dual color spaces. The framework aims to break through the limitations of traditional methods, by innovatively introducing a parallel processing mechanism for both RGB and XYZ color spaces. Upon fully taking advantages of the XYZ space in terms of perceptual linearity, the model can improve the color correction and brightness enhancement processes. Furthermore, we design a hybrid computing architecture which combines convolutional operations with a novel lightweight Transformer module. Through channel splitting and dimensionality reduction strategies, the computational complexity is reduced significantly while maintaining the ability to effectively model global contextual information. Experimental results show that DCSNet exhibits excellent enhancement performance in various underwater scenarios and delivers superior visual effects. Moreover, with its small number of model parameters, DCSNet can be deployed on embedded or edge devices for practical underwater visualization applications.