Change detection (CD) is an important research field in remote sensing, aimed at identifying differences in multi-temporal images. Despite the progress made by convolutional neural networks and Transformer architectures in visual analysis, challenges remain in achieving robust feature representation and global contextual understanding. To address these issues, we propose a transformer-based multi-scale difference enhancement network (TMDENet). Our approach utilizes a multi-scale feature extraction module (MFEM) to capture diverse feature representations, while incorporating a channel-spatial cooperation mechanism for refined detail enhancement. The extracted features are encoded into semantic tokens, which are processed by a Transformer to capture long-range dependencies. This is further complemented by a cross-hierarchical linear fusion (CHLF) module for multi-scale feature integration. Additionally, a difference enhancement module (DEM) isolates and emphasizes change-related features. Extensive evaluations on benchmark datasets (LEVIR, CDD, DSIFN, WHUCD) show that TMDENet achieves state-of-the-art results in boundary delineation and change localization. This study establishes TMDENet as a robust framework for high-resolution remote sensing CD, offering significant improvements in both precision and reliability.

Addressing the common issues of low brightness, poor contrast, and blurred details in images captured under conditions such as night, backlight, and adverse weather, we propose a zero-reference dual-path network based on multi-scale depth curve estimation for low-light image enhancement. Utilizing a no-reference loss function, the enhancement of low-light images is converted into depth curve estimation, with three curves fitted to enhance the dark details of the image: a brightness adjustment curve (LE-curve), a contrast enhancement curve (CE-curve), and a multi-scale feature fusion curve (MF-curve). Initially, we introduce the TCE-L and TCE-C modules to improve image brightness and enhance image contrast, respectively. Subsequently, we design a multi-scale feature fusion (MFF) module that integrates the original and enhanced images at multiple scales in the HSV color space based on the brightness distribution characteristics of low-light images, yielding an optimally enhanced image that avoids overexposure and color distortion. We compare our proposed method against ten other advanced algorithms based on multiple datasets, including LOL, DICM, MEF, NPE, and ExDark, that encompass complex illumination variations. Experimental results demonstrate that the proposed algorithm adapts better to the characteristics of images captured in low-light environments, producing enhanced images with sharp contrast, rich details, and preserved color authenticity, while effectively mitigating the issue of overexposure.