Underwater image processing technologies have always been challenging tasks due to the complex underwater environment. Images captured under water are not only affected by the water itself, but also by the diverse suspended particles that increase the effect of absorption and scattering. Moreover, these particles themselves are usually imaged on the picture, causing the spot noise signal to interfere with the target objects. To address this issue, we propose a novel deep neural network for removing the spot noise from underwater images. Its main idea is to train a generative adversarial network (GAN) to transform the noisy image to clean image. Based on the deep encoder and decoder framework, the skip connections are introduced to combine the features of low-level and high-level to help recover the original image. Meanwhile, the self-attention mechanism is employed to the generative network to capture global dependencies in the feature maps, which can generate the image with fine details at every location. Furthermore, we apply the spectral normalization to both the generative and discriminative networks to stabilize the training process. Experiments evaluated on synthetic and real-world images show that the proposed method outperforms many recent state-of-the-art methods in terms of quantitative and visual quality. Besides, the results also demonstrate that the proposed method has the good ability to remove the spot noise from underwater images while preserving sharp edge and fine details.

由于复杂的水下环境，水下图像处理技术一直是具有挑战性的任务。在水下拍摄的图像不仅会受到水本身的影响，还会受到增加吸收和散射效果的各种悬浮颗粒的影响。此外，这些粒子本身通常会成像在图片上，从而导致点噪声信号干扰目标对象。为了解决这个问题，我们提出了一种新颖的深度神经网络，用于从水下图像中去除斑点噪声。其主要思想是训练一个生成对抗网络（GAN），将嘈杂的图像转换为清晰的图像。在深度编码器和解码器框架的基础上，引入了跳过连接以结合低级和高级功能，以帮助恢复原始图像。与此同时，自我注意机制用于生成网络，以捕获特征图中的全局依存关系，从而可以在每个位置生成具有精细细节的图像。此外，我们将频谱归一化应用于生成网络和判别网络，以稳定训练过程。在合成图像和真实图像上进行的实验评估表明，在定量和视觉质量方面，该方法优于许多最新技术。此外，结果还表明，该方法具有良好的去除水下图像斑点噪声的能力，同时保留了锐利的边缘和精细的细节。我们将频谱归一化应用于生成网络和判别网络，以稳定训练过程。在合成图像和真实图像上进行的实验评估表明，在定量和视觉质量方面，该方法优于许多最新技术。此外，结果还表明，该方法具有良好的去除水下图像斑点噪声的能力，同时保留了锐利的边缘和精细的细节。我们将频谱归一化应用于生成网络和判别网络，以稳定训练过程。在合成图像和真实图像上进行的实验评估表明，在定量和视觉质量方面，该方法优于许多最新技术。此外，结果还表明，该方法具有良好的去除水下图像斑点噪声的能力，同时保留了锐利的边缘和精细的细节。