Zhao Yin-Ping, Zhang Jiancheng, Chen Yongyong, Wang Zhen, Li Xuelong
IEEE Trans Image Process. 2024;33:2347-2360. doi: 10.1109/TIP.2024.3374093. Epub 2024 Mar 25.
Deep unrolling-based snapshot compressive imaging (SCI) methods, which employ iterative formulas to construct interpretable iterative frameworks and embedded learnable modules, have achieved remarkable success in reconstructing 3-dimensional (3D) hyperspectral images (HSIs) from 2D measurement induced by coded aperture snapshot spectral imaging (CASSI). However, the existing deep unrolling-based methods are limited by the residuals associated with Taylor approximations and the poor representation ability of single hand-craft priors. To address these issues, we propose a novel HSI construction method named residual completion unrolling with mixed priors (RCUMP). RCUMP exploits a residual completion branch to solve the residual problem and incorporates mixed priors composed of a novel deep sparse prior and mask prior to enhance the representation ability. Our proposed CNN-based model can significantly reduce memory cost, which is an obvious improvement over previous CNN methods, and achieves better performance compared with the state-of-the-art transformer and RNN methods. In this work, our method is compared with the 9 most recent baselines on 10 scenes. The results show that our method consistently outperforms all the other methods while decreasing memory consumption by up to 80%.
基于深度展开的快照压缩成像（SCI）方法利用迭代公式构建可解释的迭代框架和嵌入式可学习模块，在从编码孔径快照光谱成像（CASSI）产生的二维测量中重建三维（3D）高光谱图像（HSI）方面取得了显著成功。然而，现有的基于深度展开的方法受到与泰勒近似相关的残差以及单手工艺先验的表示能力较差的限制。为了解决这些问题，我们提出了一种名为混合先验残差完成展开（RCUMP）的新型HSI构建方法。RCUMP利用残差完成分支来解决残差问题，并结合由新型深度稀疏先验和掩码先验组成的混合先验，以增强表示能力。我们提出的基于卷积神经网络（CNN）的模型可以显著降低内存成本，这是相对于以前的CNN方法的明显改进，并且与最先进的Transformer和循环神经网络（RNN）方法相比具有更好的性能。在这项工作中，我们的方法与10个场景中的9个最新基线进行了比较。结果表明，我们的方法始终优于所有其他方法，同时将内存消耗降低了多达80%。
