AI 中文总结
研究针对单像素光谱成像中因传感器成本高及现有方法局限性导致的问题，提出重新构建深度光学编码设计框架，引入基于场景特征的哈达玛矩阵场景驱动排序，经实验验证该方法可有效提升光谱图像质量。
AI 中文摘要
光谱图像在环境监测和精准农业等各种应用中具有很高价值，但专用传感器成本高限制了其广泛应用。当前如基于深度光学编码设计增强的单像素成像等获取高空间光谱分辨率光谱图像的替代方法，因非反馈光学设计存在局限性，图像质量有限，仅在训练时的特定场景有最佳性能。本文重新构建深度光学编码设计框架，在单像素成像架构内处理哈达玛基的场景驱动排序以进行光谱成像。考虑到单像素成像通常获取数百张快照，该方法引入基于场景特征的哈达玛矩阵场景驱动排序，通过端到端优化灵活选择调制模式。光谱数据集模拟和实际测试平台采集表明，与固定设计相比，该方法能有效提高可见光和近红外光谱图像质量。
英文摘要
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.
