用物理模型和频域感知提升光谱重建精度
PF-Trans: Physics-Embedded Frequency-Aware Transformer for Spectral Reconstruction
- 融合物理成像模型与频域滤波机制，显式建模传感器特性
- 在GF-5上海数据集上达到48.50 dB的峰值信噪比，领先现有方法
- 适合需要高保真光谱重建的遥感应用，如环境监测
快照宽带滤波阵列（BFA）成像虽具备高通光率优势，但因复杂调制导致严重光谱混叠。现有深度学习方法仅限于空间去噪，难以解决由掩码结构引发的全局频率特异性退化问题。为此，我们提出物理嵌入的频域感知变换器（PF-Trans），通过掩码注入与灰度一致性损失显式融入物理感知模型，确保重建结果的物理合理性。同时引入双域块，包含并行快速傅里叶变换分支，使网络能在频域感知并抑制混叠伪影。在多个数据集上的大量实验表明，PF-Trans达到当前最优性能，在GF-5上海数据集上最高取得48.50 dB的峰值信噪比，显著优于对比方法。
原文摘要 · Abstract (English)
Snapshot Broadband Filter Array (BFA) imaging provides high light throughput for spectral reconstruction but introduces severe spectral aliasing due to complex modulation. Current deep learning approaches, limited to spatial denoising, often fail to address the global frequency-specific degradations caused by the mask structure. To address this, we propose a Physics-embedded Frequency-aware Transformer (PF-Trans) for high-fidelity remote sensing spectral reconstruction. Our method explicitly integrates the physical sensing model through mask injection and a gray-scale consistency loss to ensure physical fidelity. Furthermore, we introduce a Dual-domain Block with a parallel Fast Fourier Transform (FFT) branch, enabling the network to perceive and suppress aliasing artifacts in the frequency domain. Extensive experiments on multiple datasets demonstrate that PF-Trans achieves state-of-the-art performance, achieving a Peak Signal-to-Noise Ratio (PSNR) of up to 48.50 dB on the GF-5 Shanghai dataset, significantly outperforming comparison methods.
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