Abstract Remote Sensing Image Dehazing (RSID) poses significant challenges in real-world scenarios due to the complex atmospheric conditions and severe color distortions that degrade image quality.

Researchers have developed a pipeline that integrates zero-shot AI detection and segmentation tools to achieve robust, automated segmentation of remote sensing images. By leveraging a sliding ...

1 Introduction. Near-ground remote sensing image dehazing occupies a vital role in land resource monitoring, delivering precise, high-resolution data essential for assessing land utilization, soil ...

Due to the presence of turbid media, such as microdust and water vapor in the environment, outdoor pictures taken under hazy weather circumstances are typically degraded. To enhance the quality of ...

Citation: Degradation-adaptive neural network for jointly single image dehazing and desnowing (2024, May 9 ... Novel frequency-adaptive methods enhance remote sensing image processing. Jan 18, 2024.

Abstract: The existing remote sensing (RS) image dehazing methods based on deep learning have sought help from the convolutional frameworks. Nevertheless, the inherent limitations of convolution, {\em ...

where I S R i and I H R i are the i-th reconstructed super-resolution image and its corresponding labeled high-resolution image, respectively.. 3.3. Context-aware transformer block. CATB consists of ...

Remote sensing image dehazing is of great scientific interest and application value in both military and civil fields. In this article, we propose an enhanced attention-guide generative adversarial ...

Remote sensing image dehazing has become important in remote sensing image preprocessing, promoting the use of remote sensing data and the precision of target recognition. Existing remote sensing ...