基于联合注意力机制的单幅图像去雨算法

现有的单幅图像去雨算法难以充分发掘不同维度注意力机制的相互作用，因此提出一种基于联合注意力机制的单幅图像去雨算法。该算法包含通道注意力机制和空间注意力机制：通道注意力机制检测各通道雨线特征的分布，并差异化各个特征通道的重要程度；空间注意力机制则针对通道内雨线分布的空间关系，以局部到全局的方式积累上下文信息，从而高效准确地去雨。此外，引入深度残差收缩网络，以利用残差模块中嵌入的软阈值非线性变换子网络来通过软阈值函数将冗余信息置零，从而提升CNN在噪声中保留图像细节的能力。在公开降雨数据集与自构建的降雨数据集上进行实验，相较于单一空间注意力算法，联合注意力去雨算法的峰值信噪比（PSNR）提升4.5%，结构相似性（SSIM）提升0.3%。实验结果表明，所提算法可以有效地进行单幅图像去雨和图像细节的信息保留，在目视效果和定量指标上均优于对比算法。

De-raining algorithm based on joint attention mechanism for single image

It is challenging for the existing single image de-raining algorithms to fully explore the interaction of attention mechanisms in different dimensions. Therefore， an algorithm based on joint attention mechanism was proposed to realize single image de-raining. The algorithm contains a channel attention mechanism and a spatial attention mechanism. Specifically， in the channel attention mechanism， the distribution of rain streak features in each channel was detected and the importance of each feature channel was differentiated. In the spatial attention mechanism， aiming at the spatial relationship of rain streak distribution within channels， the context information was accumulated in a local to global manner to realize efficient and accurate de-raining. Additionally， a deep residual shrinkage network with a soft threshold nonlinear transformation sub-network embedded in the residual module was used to zero out redundant information via a soft threshold function， thereby improving the ability of the CNN in retaining image details in noise. Experiments were carried out on open rainfall data sets and self constructed rainfall data sets. Compared with spatial attention， the joint attention rain removal algorithm improved Peak Signal-to-Noise Ratio （PSNR） by 4.5% and the Structural SIMilarity （SSIM） by 0.3%. Experimental results show that the proposed algorithm can effectively perform single image de-raining and image detail preserving. At the same time， this algorithm outperforms the comparison algorithms in terms of visual effect and quantitative metrics.