Global feature fusion generative adversarial network for underwater image enhancement

Abstract Most CNN‐based networks treat features in different channels and pixels similarly. However, the dispersion of light underwater results in uneven haze distribution throughout the images. To alleviate these problems, the global feature fusion generative adversarial network is proposed, to separate and enhance the global feature by channels and pixels simultaneously. The key novelty of our method is utilizing the attention mechanism to emphasize features in different channels and pixels in fusion block, which allocates higher weights to more important features. The network's reliability is further optimized through the incorporation of condition information to restrict the training of the network. Both qualitative and quantitative evaluations verify that the proposed method is capable of greater visual quality compared with other classic and state‐of‐the‐art methods.