Image and Graph Restoration Dependent on Generative Adversarial Network Algorithm
As a research hotspot in the field of deep learning, image inpainting is of great significance in people's real life. There are various problems in the existing image inpainting algorithms, resulting in the visual inability to meet people's requirements. In view of the defects of the exist...
Guardado en:
| Autor principal: | |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/3edf5099bcde4ebab0b2ea23f665e985 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | As a research hotspot in the field of deep learning, image inpainting is of great significance in people's real life. There are various problems in the existing image inpainting algorithms, resulting in the visual inability to meet people's requirements. In view of the defects of the existing image inpainting algorithms, such as low accuracy, poor visual consistency and unstable training, in this paper the missing content is generated by adjusting the available data. For a data set, first analyze the samples in the data set into sample points in the probability distribution, quickly generate a large number of forged images by using the generation countermeasure network, search the code of the closest damaged image, and then infer the missing content through the generation model. Combining the semantic loss function and perceptual loss function, the problem that the gradient is easy to disappear is solved. Experiments show that the algorithm improves the accuracy of image restoration, can generate more realistic repaired images, is suitable for the repair of various types of images, and realizes the realism of photos. |
|---|