|
|
|
|
|
Muzi Cui, Hao Jiang and Chaozhuo Li
Image inpainting aims to synthesize missing regions in images that are coherent with the existing visual content. Generative adversarial networks have made significant strides in the development of image inpainting. However, existing approaches heavily r...
ver más
|
|
|
|
|
|
|
|
|
|
|
Calimanut-Ionut Cira, Martin Kada, Miguel-Ángel Manso-Callejo, Ramón Alcarria and Borja Bordel Sanchez
The road surface area extraction task is generally carried out via semantic segmentation over remotely-sensed imagery. However, this supervised learning task is often costly as it requires remote sensing images labelled at the pixel level, and the result...
ver más
|
|
|
|
|
|
|
|
|
|
|
Youngjin Choi, Youngmin Park, Jaedong Hwang, Kijune Jeong and Euihyun Kim
In this paper, we propose a novel method to enhance the accuracy of a real-time ocean forecasting system. The proposed system consists of a real-time restoration system of satellite ocean temperature based on a deep generative inpainting network (GIN) an...
ver más
|
|
|
|
|
|
|
|
|
|
|
En Xie, Peijun Ni, Rongfan Zhang and Xiongbing Li
High-quality limited-angle computed tomography (CT) reconstruction is in high demand in the medical field. Being unlimited by the pairing of sinogram and the reconstructed image, unsupervised methods have attracted wide attention from researchers. The re...
ver más
|
|
|
|
|
|
|
|
|
|
|
Song-Hee Kang, Youngjin Choi and Jae Young Choi
In this paper, we propose a novel deep generative inpainting network (GIN) trained under the framework of generative adversarial learning, which is optimized for the restoration of cloud-disturbed satellite sea surface temperature (SST) imagery. The prop...
ver más
|
|
|
|
|
|