Smoothness on rank-order path graphs and its use in compressive spectral imaging with side information
Smoothness on rank-order path graphs and its use in compressive spectral imaging with side information
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DOI：
10.1016/j.sigpro.2022.108707
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
J. F. Florez-Ospina;D. Lau;D. Guillot;K. Barner;G. Arce
中科院分区：
文献类型：
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作者：
J. F. Florez-Ospina;D. Lau;D. Guillot;K. Barner;G. Arce
本文提出了一种新的具有全色边信息的压缩光谱成像（CSI）重建方法，该方法基于近似秩序统计量的概念。为此，我们假设感兴趣的信号在未知图上足够平滑。当被限制到路径图的家庭，我们表明，最佳路径确实是therank-order路径图诱导的信号。也就是说，其边结构由按升序对信号的条目进行排序的置换给出的路径图。我们的目标是表明，从一个共同注册的全色信号的秩序统计推断的秩序路径图上的平滑度可以用来从场景的压缩快照中找到准确的光谱图像估计。我们推导出秩序路径图的理论性质，并给出了它们在欠采样测量信号恢复中的应用的说明性例子。我们的方法导致解决方案的封闭形式，发现有效地通过迭代反演的高度稀疏的线性方程组。我们评估我们的方法，通过实验演示和广泛的模拟。我们的方法对一个双边过滤器图模型，适应的任务，和一些传统的和最先进的算法表现得更好。
This paper proposes a novel reconstruction approach to compressive spectral imaging (CSI) with panchromatic side information, which is based on the notion of approximate rank-order statistics. To that end, we assume that the signal of interest is sufficiently smooth on an unknown graph. When restricted to the family of path graphs, we show that the best path is indeed therank-order path graphinduced by the signal. That is, the path graph whose edge structure is given by the permutation that sorts the entries of the signal in ascending order. Our goal is to show that smoothness on rank-order path graphs inferred from the rank-order statistics of a co-registered panchromatic signal can be used to find accurate spectral image estimates from a compressive snapshot of the scene. We derive theoretical properties of rank-order path graphs and give illustrative examples of their use in signal recovery from undersampled measurements. Our approach leads to solutions with a closed-form, found efficiently by iterative inversion of highly sparse systems of linear equations. We evaluate our method through an experimental demonstration and extensive simulations. Our method performs notably better against a bilateral-filter graph model, adapted to the task, and some traditional and state-of-the-art algorithms.