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Underwater images can provide the underwater information intuitively and effectively. However, due to wavelength and distance related attenuation and scattering, underwater images may exhibit color distortion and low contrast. To address these two degradation issues, a novel two-stage network named as DAMcS-Net is proposed in this paper. In the first stage, a dual attention module that combines channel attention and spatial attention mechanisms is designed to amplify the network's perception of detail textures. In the second stage, a multi-color space stretch module is designed to adaptively adjust the histogram distribution in RGB, HSI, and Lab color spaces, so that color projection and artifacts can be eliminated effectively. Quantitative and qualitative experiments show that our model has achieved state-of-the-art performance in comparison with existing methods.
Liu et al. (Thu,) studied this question.