The purpose of remote sensing image change detection (RSCD) is to detect
differences between bi-temporal images taken at the same place. Deep learning
has been extensively used to RSCD tasks, yielding significant results in terms
of result recognition. However, due to the shooting angle of the satellite, the
impacts of thin clouds, and certain lighting conditions, the problem of fuzzy
edges in the change region in some remote sensing photographs cannot be
properly handled using current RSCD algorithms. To solve this issue, we
proposed a Body Decouple Multi-Scale by fearure Aggregation change detection
(BD-MSA), a novel model that collects both global and local feature map
information in the channel and space dimensions of the feature map during the
training and prediction phases. This approach allows us to successfully extract
the change region's boundary information while also divorcing the change
region's main body from its boundary. Numerous studies have shown that the
assessment metrics and evaluation effects of the model described in this paper
on the publicly available datasets DSIFN-CD and S2Looking are the best when
compared to other models.

提出了一种新模型 BD-MSA，通过将特征图的全局和局部特征信息集合起来，从而成功地提取变化区域的边界信息并分离变化区域的主体，该模型在公开数据集 DSIFN-CD 和 S2Looking 上的评估效果是最优的。

BD-MSA：基于多尺度特征信息聚合引导的身体解耦 VHR 遥感图像变化检测方法

BD-MSA: Body decouple VHR Remote Sensing Image Change Detection method  guided by multi-scale feature information aggregation

Single image super-resolution (SISR), which aims to reconstruct a
high-resolution (HR) image from a low-resolution (LR) observation, has been an
active research topic in the area of image processing in recent decades.
Particularly, deep learning-based super-resolution (SR) approaches have drawn
much attention and have greatly improved the reconstruction performance on
synthetic data. Recent studies show that simulation results on synthetic data
usually overestimate the capacity to super-resolve real-world images. In this
context, more and more researchers devote themselves to develop SR approaches
for realistic images. This article aims to make a comprehensive review on
real-world single image super-resolution (RSISR). More specifically, this
review covers the critical publically available datasets and assessment metrics
for RSISR, and four major categories of RSISR methods, namely the degradation
modeling-based RSISR, image pairs-based RSISR, domain translation-based RSISR,
and self-learning-based RSISR. Comparisons are also made among representative
RSISR methods on benchmark datasets, in terms of both reconstruction quality
and computational efficiency. Besides, we discuss challenges and promising
research topics on RSISR.

通过对公共数据集、评估指标和四类方法的研究以及在基准数据集上的比较，本文全面评估了基于深度学习的单个图像超分辨率（SISR）方法中类别为基于退化建模、基于图像对、基于域转换和基于自学习的 RSISR 方法在重建质量和计算效率方面的表现，并讨论了 RSISR 的挑战和未来研究方向。