基于多尺度级联网络的水下图像增强方法

针对水下图像由于光吸收、后向散射等因素导致的严重色偏、细节丢失等问题，该文提出一种基于多尺度级联网络的水下图像增强方法。针对单一网络特征利用不全面导致的图像梯度消失问题，该方法通过级联多尺度原始图像与相应的特征图像，以获得更优异的细节保持效果，并实现从较浅层到较深层快速预测残差的能力。此外，引入联合密集网络块和递归块，通过特征重用有效解决多尺度网络参数过多的问题。为有效解决单一损失造成的图像细节恢复不均的问题，提出Charbonnier和结构相似度 (SSIM) 联合损失函数。经仿真实验分析，所提网络在处理水下图像严重色偏、细节丢失等方面都取得了显著的效果。

Underwater Image Enhancement Method Based on Multi-scale Cascade Network

Focusing on the serious color shift and loss of details caused by light absorption, backscattering and other factors in underwater images, an underwater image enhancement method based on multi-scale cascaded network is proposed in this paper. For the image gradient dissipation caused by incomplete utilization of features via single network, better details are preserved by cascading multi-scale original images and corresponding feature images, and rapid prediction of residuals from shallower layers to deeper layers can be realized at the same time. In addition, joint dense network block and recursive block are introduced to avoid effectively the problem of excessive parameters introduced by conventional multi-scale network through feature reuse. A joint loss function of Charbonnier and the Structural SIMilarity (SSIM) is proposed to solve effectively the problem of uneven restoration of image details caused by a single loss. The simulation experiments show that the proposed network has achieved excellent results in dealing with severe color shift and loss of details.