Name
Title | ||
|---|---|---|
$$\hbox {S}^2\hbox {RGAN}$$ S 2 RGAN : sonar-image super-resolution based on generative adversarial network |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s00371-020-01986-3 | The Visual Computer |
Keywords | DocType | Volume |
Sonar-image, Super-resolution, Generative adversarial network, Transfer learning | Journal | 37 |
Issue | ISSN | Citations |
8 | 0178-2789 | 3 |
PageRank | References | Authors |
0.40 | 7 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongtao Song | 1 | 3 | 0.73 |
| Minghao Wang | 2 | 3 | 0.40 |
| Liguo Zhang | 3 | 23 | 3.41 |
| Yang Li | 4 | 7 | 1.86 |
| Zhengyi Jiang | 5 | 3 | 0.40 |
| Guisheng Yin | 6 | 195 | 14.69 |