根据场景动态排列哈达玛基，提升单像素光谱成像质量
Deep Scene-Driven Ordering of Hadamard Basis for Single-Pixel Spectral Imaging
- 基于场景特征动态调整哈达玛基的顺序，实现自适应编码
- 在可见光和近红外波段上，重构图像质量显著优于固定编码
- 适合对光谱成像质量要求高、需灵活适配不同场景的应用
光谱成像在环境监测和精准农业等领域具有重要价值，但专用传感器成本高昂，限制了其广泛应用。当前基于单像素成像（SPI）结合深度光学编码设计（DOCD）的方法，因缺乏反馈机制，图像质量受限，且仅在训练场景下表现最佳。本文重新构建DOCD框架，在SPI架构中引入基于场景的哈达玛基排序策略，利用数百次采样特性，通过端到端优化实现依据场景特征灵活选择调制模式。仿真与真实实验均表明，该方法在可见光与近红外波段的光谱图像重建质量显著优于固定编码设计。
原文摘要 · Abstract (English)
Spectral images are highly valuable for various applications, including environmental monitoring and precision agriculture. However, the high cost of specialized sensors limits the wide use of this technology in numerous applications. Current alternatives to acquire high spatial-spectral resolution spectral images, like Single-Pixel Imaging (SPI) enhanced with Deep Optical Coding Design (DOCD), have limitations due to their non-feedback optical designs, leading to limited image quality, with optimal performance achieved only for the specific scenes used during training. This work reformulates the DOCD framework to handle the scene-driven ordering of the Hadamard basis within the SPI architecture for spectral imaging. Taking into account that SPI usually acquires hundreds of snapshots, our approach introduces a scene-driven ordering of the Hadamard matrix for flexible SPI modulation pattern selection based on scene characteristics in an end-to-end optimization. Simulations on spectral datasets and real test-bed acquisitions demonstrate the effectiveness of the proposed method in improving the quality of VIS and NIR spectral images compared to fixed designs.
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