计算机科学
人工智能
双三次插值
图像分辨率
图像(数学)
相似性(几何)
计算机视觉
图像纹理
模式识别(心理学)
代表(政治)
图形
人工神经网络
遥感
图像处理
地理
理论计算机科学
线性插值
政治
政治学
法学
作者
Baodi Liu,Lifei Zhao,Shuai Shao,Weifeng Liu,Dapeng Tao,Weijia Cao,Yicong Zhou
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:61: 1-13
被引量:1
标识
DOI:10.1109/tgrs.2023.3330876
摘要
The remote sensing (RS) image super-resolution (SR) algorithm aims to reconstruct a high-resolution (HR) image with rich texture details from a given low-resolution (LR) image, improving the spatial resolution. It has been widely concerned in remote sensing image processing and application. Most current deep learning-based methods rely on paired training datasets. However, most datasets are often based on bicubic degradation. This single construction way limits the performance of the pre-trained network. Moreover, SR is an ill-posed problem in that multiple SR images are constructed from a single LR input. This paper proposes a Region-Aware Network (RAN) for remote sensing image super-resolution to alleviate the above issues. First, we introduce the contrastive learning strategy to mine the latent degraded representation of the image and serve as the prior knowledge of the network. Considering the RS images are acquired in specific scenes that have apparent self-similarity. Then, we propose a Region-Aware Module (RAM) based on attention mechanisms and the graph neural network to explore region information and cross-patch self-similarity. Extensive experiments have demonstrated that the proposed RAN adapts to RS image super-resolution tasks with various degradations and performs better in constructing texture information.
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