论文标题
基于全局结构和光谱相关性重建压缩光谱成像
Reconstruction of compressed spectral imaging based on global structure and spectral correlation
论文作者
论文摘要
在本文中，提出了一种基于全球结构特征和光谱相关性的卷积稀疏编码方法，以重建压缩光谱图像。光谱数据被认为是卷积内核和相应系数的卷积总和，使用卷积内核操作全局图像信息，从而在空间维度中保留光谱图像的结构信息。为了充分探索光谱之间的约束，与卷积内核相对应的系数受到L_（2,1）规范的约束，以提高光谱精度。并且，为了解决卷积稀疏编码对低频不敏感的问题，添加了全局总变化（TV）约束以估计低频组件。它不仅确保对低频的有效估计，而且还可以将卷积稀疏编码转换为一个降价过程，从而使重建过程变得更加简单。模拟表明，与当前的主流优化方法相比，所提出的方法可以在PSNR中提高重建质量高达4 dB，而SSIM中的重建质量可有所改善，并且在重建图像的细节上有了很大的改善。
In this paper, a convolutional sparse coding method based on global structure characteristics and spectral correlation is proposed for the reconstruction of compressive spectral images. The spectral data is regarded as the convolution sum of the convolution kernel and the corresponding coefficients, using the convolution kernel operates the global image information, preserving the structure information of the spectral image in the spatial dimension. To take full exploration of the constraints between spectra, the coefficients corresponding to the convolution kernel are constrained by the L_(2,1)norm to improve spectral accuracy. And, to solve the problem that convolutional sparse coding is insensitive to low frequency, the global total-variation (TV) constraint is added to estimate the low-frequency components. It not only ensures the effective estimation of the low-frequency but also transforms the convolutional sparse coding into a de-noising process, which makes the reconstructing process simpler. Simulations show that compared with the current mainstream optimization methods, the proposed method can improve the reconstruction quality by up to 4 dB in PSNR and 10% in SSIM, and has a great improvement in the details of the reconstructed image.
