以干涉高光谱成像模型为基础，提出一种结合先验约束的空谱信息同步复原模型，通过非负低秩特性和全变分（TV）正则项分别约束复原高光谱图像的谱间强相关性和空间分段平滑特性，并采用L₁范数和Frobenius范数分别对干涉数据中的稀疏性噪声和高斯噪声进行建模。模拟和真实干涉数据的对比实验验证了所提方法的有效性。相比于传统的干涉数据复原方法，所提方法在准确复原目标光谱信息的同时，能够有效地消除干涉图混合噪声的退化影响，从而提高复原高光谱图像的数据质量。

On the basis of the imaging model of an interferometric imaging spectrometer, a novel model with a priori constraints is proposed to recover spatial and spectral information simultaneously. The nonnegative low rank property and total variation (TV) regularization are adopted to constrain the strong spectral correlation and spatial piecewise smoothness of recovered hyperspectral image, respectively. Meanwhile, the sparse noise and Gaussian noise in interferometric data are modeled by the L₁ norm and Frobenius norm, respectively. The effectiveness of the proposed method is verified through comparative experiments on simulated and real interferometric data. Compared with traditional recovery methods for interferometric data, the proposed method not only recovers the spectral information of the object accurately but also effectively eliminates the degradation effect of mixed noise in the interferogram. As a result, the data quality of the recovered hyperspectral image is improved.