标题: Super-Resolution for Remote Sensing Imagery via the Coupling of a Variational Model and Deep Learning

作者: Sun, J (Sun, Jing); Shen, HF (Shen, Huanfeng); Yuan, QQ (Yuan, Qiangqiang); Zhang, LP (Zhang, Liangpei)

来源出版物: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING  卷: 63  文献号: 5611119  DOI: 10.1109/TGRS.2025.3532645  Published Date: 2025  

摘要: Image super-resolution (SR) is an effective way to enhance the spatial resolution and detail information of remote sensing images to obtain a superior visual quality. As SR is severely ill-conditioned, effective image priors are necessary to regularize the solution space and generate the corresponding high-resolution (HR) image. In this article, we propose a novel gradient-guided multiframe SR (MFSR) framework for remote sensing imagery reconstruction. The framework integrates a learned gradient prior as the regularization term into a model-based optimization method. Specifically, the local gradient regularization (LGR) prior is derived from the deep residual attention network (DRAN) through gradient profile transformation (GPT). The nonlocal total variation (NLTV) prior is characterized using the spatial structure similarity of the gradient patches with the maximum a posteriori (MAP) model. The modeled prior performs well in preserving edge smoothness and suppressing visual artifacts, while the learned prior is effective in enhancing sharp edges and recovering fine structures. By incorporating the two complementary priors into an adaptive norm-based reconstruction framework, the mixed L1 and L2 regularization minimization problem is optimized to achieve the required HR remote sensing image. Extensive experimental results on remote sensing data demonstrate that the proposed method can produce visually pleasant images and is superior to several of the state-of-the-art SR algorithms in terms of the quantitative evaluation.

作者关键词: Remote sensing; Image edge detection; Image reconstruction; Spatial resolution; Sensors; Superresolution; Estimation; Degradation; Deep learning; Satellites; image super-resolution (SR); local gradient regularization (LGR); nonlocal total variation (NLTV); remote sensing

KeyWords Plus: SUPER RESOLUTION; ALGORITHM

地址: [Sun, Jing; Shen, Huanfeng] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Shen, Huanfeng] Wuhan Univ, Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Hubei, Peoples R China.

[Yuan, Qiangqiang] Wuhan Univ, Sch Geodesy & Geomat, Wuhan 430079, Peoples R China.

[Zhang, Liangpei] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Remote Sensing, Wuhan 430079, Peoples R China.

通讯作者地址: Shen, HF (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

Shen, HF (通讯作者)，Wuhan Univ, Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Hubei, Peoples R China.

电子邮件地址: rainsunny@hotmail.com; shenhf@whu.edu.cn; yqiang86@gmail.com; zlp62@whu.edu.cn

影响因子：8.6