دورية أكاديمية
Gradient Prior Dilated Convolution Network for Remote Sensing Image Super-Resolution
| العنوان: | Gradient Prior Dilated Convolution Network for Remote Sensing Image Super-Resolution |
|---|---|
| المؤلفون: | Ziyu Liu, Ruyi Feng, Lizhe Wang, Tieyong Zeng |
| المصدر: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 3945-3958 (2023) |
| بيانات النشر: | IEEE, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Ocean engineering LCC:Geophysics. Cosmic physics |
| مصطلحات موضوعية: | Attention, dilated convolution (DC), gradient prior, remote sensing super-resolution, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809 |
| الوصف: | Super-resolution (SR) aims to recover a high-resolution image from a single or multiple low-resolution images, compensating for the limitations of satellite sensor imaging. Deep convolutional neural networks have made great achievement in remote sensing image SR. In this article, we propose a novel gradient prior dilated convolutional network (GPDCN) for remote sensing image SR, which obtains contextual spatial connections and alleviates structural distortions. The GPDCN comprises a multiscale feature extraction network and a feature reconstruction network. The former employs a double-path dilated residual block with dilation convolution to increase a receptive field, a global self-attention module to detect long-range reliance among image patches, and a gradient propagation network to extract high-level gradient information. The latter uses the mixed high-order attention module to reconstruct the feature by collecting the high-order characteristics of multiple frequency bands. Experiments with the Massachusetts_Roads and 3K VEHICLE_SR datasets demonstrate that the GPDCN outperforms recent techniques concerning both quantitative and qualitative measures. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2151-1535 |
| Relation: | https://ieeexplore.ieee.org/document/10059121/; https://doaj.org/toc/2151-1535 |
| DOI: | 10.1109/JSTARS.2023.3252585 |
| URL الوصول: | https://doaj.org/article/8cd30c461d574de89fa12911d97cfafd |
| رقم الأكسشن: | edsdoj.8cd30c461d574de89fa12911d97cfafd |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 21511535 |
|---|---|
| DOI: | 10.1109/JSTARS.2023.3252585 |