Cargando…
Multiscale Attention Fusion for Depth Map Super-Resolution Generative Adversarial Networks
Color images have long been used as an important supplementary information to guide the super-resolution of depth maps. However, how to quantitatively measure the guiding effect of color images on depth maps has always been a neglected issue. To solve this problem, inspired by the recent excellent r...
Autores principales: | Xu, Dan, Fan, Xiaopeng, Gao, Wen |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10296949/ https://www.ncbi.nlm.nih.gov/pubmed/37372180 http://dx.doi.org/10.3390/e25060836 |
Ejemplares similares
-
Improving Image Super-Resolution Based on Multiscale Generative Adversarial Networks
por: Yuan, Cao, et al.
Publicado: (2022) -
Super-Resolution Generative Adversarial Network Based on the Dual Dimension Attention Mechanism for Biometric Image Super-Resolution
por: Huang, Chi-En, et al.
Publicado: (2021) -
FA-GAN: Fused attentive generative adversarial networks for MRI image super-resolution
por: Jiang, Mingfeng, et al.
Publicado: (2021) -
Panchromatic Image Super-Resolution Via Self Attention-Augmented Wasserstein Generative Adversarial Network
por: Du, Juan, et al.
Publicado: (2021) -
Super-Resolution Reconstruction of Terahertz Images Based on Residual Generative Adversarial Network with Enhanced Attention
por: Hou, Zhongwei, et al.
Publicado: (2023)