Ding Jinting, Xu Honghui, Zhou Shengjun
School of Information and Electrical Engineering, Hangzhou City University, Hangzhou 310015, China.
School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China.
Entropy (Basel). 2025 May 27;27(6):567. doi: 10.3390/e27060567.
Pansharpening provides a computational solution to the resolution limitations of imaging hardware by enhancing the spatial quality of low-resolution hyperspectral (LRMS) images using high-resolution panchromatic (PAN) guidance. From an information-theoretic perspective, the task involves maximizing the mutual information between PAN and LRMS inputs while minimizing spectral distortion and redundancy in the fused output. However, traditional spatial-domain methods often fail to preserve high-frequency texture details, leading to entropy degradation in the resulting images. On the other hand, frequency-based approaches struggle to effectively integrate spatial and spectral cues, often neglecting the underlying information content distributions across domains. To address these shortcomings, we introduce a novel architecture, termed the Cross-Domain Fusion Attention Network (CDFAN), specifically designed for the pansharpening task. CDFAN is composed of two core modules: the Multi-Domain Interactive Attention (MDIA) module and the Spatial Multi-Scale Enhancement (SMCE) module. The MDIA module utilizes discrete wavelet transform (DWT) to decompose the PAN image into frequency sub-bands, which are then employed to construct attention mechanisms across both wavelet and spatial domains. Specifically, wavelet-domain features are used to formulate query vectors, while key features are derived from the spatial domain, allowing attention weights to be computed over multi-domain representations. This design facilitates more effective fusion of spectral and spatial cues, contributing to superior reconstruction of high-resolution multispectral (HRMS) images. Complementing this, the SMCE module integrates multi-scale convolutional pathways to reinforce spatial detail extraction at varying receptive fields. Additionally, an Expert Feature Compensator is introduced to adaptively balance contributions from different scales, thereby optimizing the trade-off between local detail preservation and global contextual understanding. Comprehensive experiments conducted on standard benchmark datasets demonstrate that CDFAN achieves notable improvements over existing state-of-the-art pansharpening methods, delivering enhanced spectral-spatial fidelity and producing images with higher perceptual quality.
全色锐化通过利用高分辨率全色（PAN）引导增强低分辨率高光谱（LRMS）图像的空间质量，为成像硬件的分辨率限制提供了一种计算解决方案。从信息论的角度来看，该任务涉及最大化PAN和LRMS输入之间的互信息，同时最小化融合输出中的光谱失真和冗余。然而，传统的空间域方法往往无法保留高频纹理细节，导致所得图像的熵降低。另一方面，基于频率的方法难以有效地整合空间和光谱线索，常常忽略跨域的潜在信息内容分布。为了解决这些缺点，我们引入了一种新颖的架构，称为跨域融合注意力网络（CDFAN），专门为全色锐化任务设计。CDFAN由两个核心模块组成：多域交互式注意力（MDIA）模块和空间多尺度增强（SMCE）模块。MDIA模块利用离散小波变换（DWT）将PAN图像分解为频率子带，然后用于构建跨小波和空间域的注意力机制。具体而言，小波域特征用于制定查询向量，而关键特征则从空间域导出，从而允许在多域表示上计算注意力权重。这种设计有助于更有效地融合光谱和空间线索，有助于高分辨率多光谱（HRMS）图像的卓越重建。作为补充，SMCE模块集成了多尺度卷积路径，以加强不同感受野的空间细节提取。此外，引入了专家特征补偿器，以自适应地平衡不同尺度的贡献，从而优化局部细节保留和全局上下文理解之间的权衡。在标准基准数据集上进行的综合实验表明，CDFAN相对于现有的最先进全色锐化方法取得了显著改进，提供了增强的光谱-空间保真度，并生成了具有更高感知质量的图像。
