高光谱成像
比例(比率)
图像分辨率
扩散
分辨率(逻辑)
计算机科学
遥感
人工智能
地质学
物理
地图学
地理
热力学
作者
Jizhou Cui,Wenqian Dong,Jiahui Qu,Xiaoyang Wu,Song Xiao,Yunsong Li
出处
期刊:IEEE Geoscience and Remote Sensing Letters
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:21: 1-5
被引量:2
标识
DOI:10.1109/lgrs.2024.3369026
摘要
Hyperspectral image super-resolution (HSISR) has shown very promising potential for earth observation and deep space exploration tasks. However, most existing HSISR methods formulate HSISR tasks with different scale factors as independent tasks, and train a specific model for each scale factor. In this letter, we propose a coarse-to-fine meta diffusion HSISR method, termed as CFMDM, which is capable of solving the problem of HSISR with scale-arbitrary factors in a unified model. The proposed CFMDM is composed of a coarse-to-fine upsampling module. The module encompasses two pivotal units: a coarse meta upsampling unit that utilizes meta-learning to map features of arbitrary scales to the corresponding scales, and a gradual refinement diffusion unit, which is designed to refine the details of the reconstructed HSI. In addition, we develop an imaging model-driven downsampling algorithm for generating training samples tailored to practical applications. The proposed method performs well in both quantitative and qualitative evaluation on benchmark datasets, achieving the average PSNR of 41.45dB at 1.5x super-resolution for the CAVE dataset.
科研通智能强力驱动
Strongly Powered by AbleSci AI