11 citations · 19 across the 6 of their papers we have counts for
6 papers · 1 filter
Task-Driven Prompt Learning: A Joint Framework for Multi-modal Cloud Removal and Segmentation
Zaiyan Zhang, Jie Li, Shaowei Shi +1
Optical remote sensing imagery is indispensable for Earth observation, yet persistent cloud occlusion limits its downstream utility. Most cloud removal (CR) methods are optimized f…
TDiffDe: A Truncated Diffusion Model for Remote Sensing Hyperspectral Image Denoising
Jiang He, Yajie Li, Jie L +1
Hyperspectral images play a crucial role in precision agriculture, environmental monitoring or ecological analysis. However, due to sensor equipment and the imaging environment, th…
Hybrid Noise Removal in Hyperspectral Imagery With a Spatial-Spectral Gradient Network
Qiang Zhang, Qiangqiang Yuan, Jie Li +3
The existence of hybrid noise in hyperspectral images (HSIs) severely degrades the data quality, reduces the interpretation accuracy of HSIs, and restricts the subsequent HSIs appl…
Spatial-Spectral Fusion by Combining Deep Learning and Variation Model
Huanfeng Shen, Menghui Jiang, Jie Li +3
In the field of spatial-spectral fusion, the model-based method and the deep learning (DL)-based method are state-of-the-art. This paper presents a fusion method that incorporates…
Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network
Qiangqiang Yuan, Qiang Zhang, Jie Li +2
Hyperspectral image (HSI) denoising is a crucial preprocessing procedure to improve the performance of the subsequent HSI interpretation and applications. In this paper, a novel de…
Learning a Dilated Residual Network for SAR Image Despeckling
Qiang Zhang, Qiangqiang Yuan, Jie Li +2
In this paper, to break the limit of the traditional linear models for synthetic aperture radar (SAR) image despeckling, we propose a novel deep learning approach by learning a non…