Raw underwater images usually suffer from quality degradation, and their resolutions are lower. To obtain high-resolution underwater images, some super-resolution (SR) algorithms have achieved great visual effect based on the excellent ability of deep convolution neural network. However, most previous works fail to consider the full use of inner information. Besides, simply widening and deepening the network contribute little to performance improvement. In this paper, an attention-guided multi-path cross-convolution neural network (AMPCNet) is proposed for underwater image SR. We present a multi-path cross (MPC)-module, which contains residual blocks and dilated blocks, to enhance the model’s learning capacity and increase abstract feature representation. Specifically, the design of the cross-connections realizes the mutual fusion of local features learned by residual blocks and multi-scale features acquired by dilated blocks. And the combination of dilated convolution and ordinary convolution achieves a trade-off between performance and efficiency. Furthermore, an attention block is presented to adaptively rescale the channel-wise features for more discriminative representations. Finally, the upsample block helps the reconstruction of HR images. Experimental results demonstrate the superiority of our AMPCNet network in terms of both quantitative metrics and visual quality.

原始水下图像通常会出现质量下降，并且分辨率较低。为了获得高分辨率的水下图像，一些超分辨率（SR）算法基于深度卷积神经网络的出色能力取得了很好的视觉效果。然而，以前的大多数工作都没有考虑充分利用内部信息。此外，简单地扩大和深化网络对性能提升几乎没有贡献。在本文中，针对水下图像SR提出了一种注意力引导的多路径交叉卷积神经网络（AMPCNet）。我们提出了一个多路径交叉（MPC）模块，其中包含残差块和扩张块，以增强模型的学习能力并增加抽象特征表示。具体来说，交叉连接的设计实现了残差块学习的局部特征和扩张块获得的多尺度特征的相互融合。并且扩张卷积和普通卷积的结合实现了性能和效率之间的权衡。此外，还提出了一个注意力块，以自适应地重新调整通道特征，以获得更具辨别力的表示。最后，上采样块有助于重建 HR 图像。实验结果证明了我们的 AMPCNet 网络在定量指标和视觉质量方面的优越性。提出了一个注意力块，以自适应地重新调整通道特征，以获得更具辨别力的表示。最后，上采样块有助于重建 HR 图像。实验结果证明了我们的 AMPCNet 网络在定量指标和视觉质量方面的优越性。提出了一个注意力块，以自适应地重新调整通道特征，以获得更具辨别力的表示。最后，上采样块有助于重建 HR 图像。实验结果证明了我们的 AMPCNet 网络在定量指标和视觉质量方面的优越性。