锐化
计算机科学
全色胶片
人工智能
特征(语言学)
频域
卷积(计算机科学)
特征提取
多光谱图像
卷积神经网络
计算机视觉
傅里叶变换
模式识别(心理学)
人工神经网络
数学
哲学
语言学
数学分析
作者
Yingying Wang,Y. Lin,Ge Meng,Zhenqi Fu,Yuhang Dong,Linyu Fan,Hedeng Yu,Xinghao Ding,Yue Huang
标识
DOI:10.1145/3581783.3611937
摘要
Pan-sharpening aims to utilize the high-resolution panchromatic (PAN) image as a guidance to super-resolve the spatial resolution of the low-resolution multispectral (MS) image. The key challenge in pan-sharpening is how to effectively and precisely inject high-frequency edges and textures from the PAN image into the low-resolution MS image. To address this issue, we propose a High-frequency Feature Enhancement and Alignment Network (HFEAN) for effectively encouraging the high-frequency learning. To implement it, three core designs are customized: a Fourier convolution based efficient feature enhancement module (FEM), an implicit neural alignment module (INA), and a preliminary alignment module (Pre-align). To be specific, FEM employs the fast Fourier convolution with attention mechanism to achieve the mixed global-local receptive field on each scale of the high-frequency domain, thus yielding the informative latent codes. INA leverages implicit neural function to precisely align the latent codes from different scales in the continuous domain. In this way, the high frequency signals at different scales are represented as functions of continuous coordinates, enabling a precise feature alignment in a resolution-free manner. Pre-align is developed to further address the inherent misalignment between PAN and MS pairs. Extensive experiments over multiple satellite datasets validate the effectiveness of the proposed network and demonstrate its favorable performance against the existing state-of-the-art methods both visually and quantitatively. Code is available at: https://github.com/Gracewangyy/HFEAN.
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