As an important display mode of underwater environments, the sonar image has limitations on the resolution, which often leads to problems with low resolution of underwater objects. Therefore, the image super-resolution algorithm is needed to transform the images from low-resolution to high-resolution. It can improve the visual effect and contribute to subsequent processing such as 3D reconstruction and object recognition. This paper proposes a method for sonar image super-resolution based on generative adversarial networks (GAN). By comparing the super-resolution effects of various interpolation and convolutional neural network algorithms on sonar images, a Residual-in-Residual Dense Block network is employed as the generator of GAN since it has the low distortion and high perceptual quality. Because the sonar image training set does not have enough data, the generator utilizes the transfer learning on the sonar images to produce an optimized network model which is more suitable for super-resolution of sonar image. The VGG19 network is employed as the discriminator. In addition, the perceptual loss is introduced into the loss function of $$\hbox {S}^2\hbox {RGAN}$$ to further improve the perceptual quality of super-resolution images. The experimental results indicate that the proposed $$\hbox {S}^2\hbox {RGAN}$$ shows excellent performance. The generated super-resolution images of $$\hbox {S}^2\hbox {RGAN}$$ have the remarkable advantages of both lower distortion and higher perceptual quality comparing with other methods. Because $$\hbox {S}^2\hbox {RGAN}$$ focuses more on the reality and overall visual effect of super-resolution sonar images, it is suitable for various underwater situations.

中文翻译：

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$$\hbox {S}^2\hbox {RGAN}$$：基于生成对抗网络的声纳图像超分辨率

声纳图像作为水下环境的一种重要显示方式，在分辨率上存在局限性，这往往会导致水下物体分辨率低的问题。因此，需要图像超分辨率算法将图像从低分辨率转换为高分辨率。它可以改善视觉效果，并有助于3D重建和物体识别等后续处理。本文提出了一种基于生成对抗网络（GAN）的声纳图像超分辨率方法。通过比较各种插值和卷积神经网络算法对声纳图像的超分辨率效果，采用Residual-in-Residual Dense Block网络作为GAN的生成器，因为它具有低失真和高感知质量。由于声纳图像训练集没有足够的数据，生成器利用对声纳图像的迁移学习来生成更适合声纳图像超分辨率的优化网络模型。VGG19 网络用作鉴别器。此外，在$$\hbox {S}^2\hbox {RGAN}$$的损失函数中引入了感知损失，以进一步提高超分辨率图像的感知质量。实验结果表明，所提出的 $$\hbox {S}^2\hbox {RGAN}$$ 表现出优异的性能。$$\hbox {S}^2\hbox {RGAN}$$ 生成的超分辨率图像与其他方法相比具有更低失真和更高感知质量的显着优势。