保险丝(电气)
计算机科学
人工智能
可解释性
图像分辨率
图像融合
GSM演进的增强数据速率
频道(广播)
计算机视觉
过程(计算)
光学(聚焦)
特征(语言学)
对偶(语法数字)
计算
补语(音乐)
图像(数学)
模式识别(心理学)
算法
文学类
艺术
化学
互补
哲学
表型
工程类
物理
光学
电气工程
操作系统
基因
生物化学
语言学
计算机网络
作者
Mengyang Shi,Yesheng Gao,Lin Chen,Xingzhao Liu
出处
期刊:IEEE Geoscience and Remote Sensing Letters
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:19: 1-5
标识
DOI:10.1109/lgrs.2022.3221614
摘要
Single image super-resolution technology is critical in remote sensing fields because it can effectively improve the details of target images. However, the application of deep learning is limited due to the lack of interpretability and the need for many parameters. This letter proposes an interpretable dual-branch multi-scale channel fusion unfolding network (DMUNet) for optical remote sensing image (ORSI) super-resolution. We design an unfolding network with double branches, each optimized with different strategies. Two branches focus on texture and edge reconstruction, respectively. This unfolding network follows the iteration process of the alternating direction method of multipliers (ADMM) and can learn the hyper-parameters adaptively. The functions of the two branches can complement each other. Further, to better fuse the feature maps of the two branches, a multi-scale fusion module is proposed. This module can effectively fuse information between different branches, scales, and channels. It is noted that it only requires a little computation cost. Experiments on two public ORSI datasets demonstrate that our method can achieve significant performance in both quantitative evaluation and visual results.
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