融入注意力机制的弱监督水下图像增强算法

基于监督学习的水下图像增强算法中所需成对训练样本获得困难，为此提出一种融入注意力机制的弱监督水下图像增强算法. 根据不同波长的光在水中传播时衰减程度不同的物理特性，计算红通道衰减图，并将依赖红通道衰减图引导的注意力模块融入生成网络，提高生成网络修正水下图像色偏的性能；设计对抗损失函数和结构相似性损失函数相结合的多项联合损失函数，在修正水下图像色偏的同时保留更多图像细节；在全局和局部两个尺度下优化提出的弱监督水下图像增强网络模型. 实验结果表明，所提算法在主观视觉质量和客观评价指标上都优于比较算法，可以有效地提高水下图像清晰度.

weakly supervised underwater image enhancement algorithm incorporating attention mechanism

The supervised underwater image enhancement algorithms need paired training image samples that are difficult to be obtained in some uncontrolled scenarios such as underwater scenarios. A weakly supervised underwater image enhancement algorithm incorporating attention mechanism was proposed. Firstly, the red channel attenuation map was calculated according to the characteristics that the light with different wavelengths suffers from different attenuation when it propagates in water. After that, the attention module guided by the calculated red channel attenuation map was integrated into the generator, which effectively improved the performance of the generator in terms of correcting the color deviation of underwater images. In addition, a multiple joint loss function, including an adversarial loss and a structural similarity loss, was designed, which retained more image details while correcting color deviation of underwater images. Finally, the underwater image enhancement network was optimized under global and local scales. Experimental results show that the proposed algorithm is better than the competing algorithms in both subjective visual quality and objective evaluation index, and thus can effectively improve the visibility of underwater images.