用超瑞利散斑+深度学习，提升单次拍摄高光谱成像质量。
Hyperspectral image reconstruction by deep learning with super-Rayleigh speckles
- 设计简单有效的GISCnet网络，结合超瑞利散斑调制。
- 平均信噪比从27 dB提升至31 dB，重建细节更丰富。
- 适合对高光谱成像质量要求高的科研与工业场景。
通过稀疏性约束的鬼成像光谱相机（GISC）将三维高光谱图像（HSI）在单次拍摄中调制为二维压缩图像，再通过重建算法还原3D HSI。深度学习的发展为3D HSI重建提供了新路径。本文提出端到端的GISCnet，结合超瑞利散斑调制，显著提升成像质量。该网络结构简洁但高效，可通过调整参数优化重建效果。相比瑞利散斑，超瑞利散斑在重建3D HSI时展现出更丰富的细节。在648个3D HSI数据集上评估后发现，平均峰值信噪比由27 dB提升至31 dB。结果表明，利用优化的超瑞利调制与深度学习重建相结合，能有效提升GISC光谱相机的成像性能，为光场调制与图像重建的联合优化提供了新思路。
原文摘要 · Abstract (English)
Ghost imaging via sparsity constraints (GISC) spectral camera modulates the three-dimensional (3D) hyperspectral image into a two-dimensional (2D) compressive image with speckles in a single shot. It obtains a 3D hyperspectral image (HSI) by reconstruction algorithms. The rapid development of deep learning has provided a new method for 3D HSI reconstruction. Moreover, the imaging performance of the GISC spectral camera can be improved by optimizing the speckle modulation. In this paper, we propose an end-to-end GISCnet with super-Rayleigh speckle modulation to improve the imaging quality of the GISC spectral camera. The structure of GISCnet is very simple but effective, and we can easily adjust the network structure parameters to improve the image reconstruction quality. Relative to Rayleigh speckles, our super-Rayleigh speckles modulation exhibits a wealth of detail in reconstructing 3D HSIs. After evaluating 648 3D HSIs, it was found that the average peak signal-to-noise ratio increased from 27 dB to 31 dB. Overall, the proposed GISCnet with super-Rayleigh speckle modulation can effectively improve the imaging quality of the GISC spectral camera by taking advantage of both optimized super-Rayleigh modulation and deep-learning image reconstruction, inspiring joint optimization of light-field modulation and image reconstruction to improve ghost imaging performance.
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