Deconvolution is a widely used strategy to mitigate the blurring and noisy degradation of hyperspectral images~(HSI) generated by the acquisition devices. This issue is usually addressed by solving an ill-posed inverse problem. While investigating proper image priors can enhance the deconvolution performance, it is not trivial to handcraft a powerful regularizer and to set the regularization parameters. To address these issues, in this paper we introduce a tuning-free Plug-and-Play (PnP) algorithm for HSI deconvolution. Specifically, we use the alternating direction method of multipliers (ADMM) to decompose the optimization problem into two iterative sub-problems. A flexible blind 3D denoising network (B3DDN) is designed to learn deep priors and to solve the denoising sub-problem with different noise levels. A measure of 3D residual whiteness is then investigated to adjust the penalty parameters when solving the quadratic sub-problems, as well as a stopping criterion. Experimental results on both simulated and real-world data with ground-truth demonstrate the superiority of the proposed method.

翻译：去卷积是缓解采集设备产生的高光谱图像模糊和噪声退化的常用策略。该问题通常通过求解一个病态逆问题来解决。虽然研究适当的图像先验可以增强去卷积性能，但手动设计一个强大的正则化器并设置正则化参数并非易事。为了解决这些问题，本文提出了一种无调参的即插即用高光谱图像去卷积算法。具体而言，我们使用交替方向乘子法将优化问题分解为两个迭代子问题。设计了一种灵活的盲三维去噪网络来学习深度先验，并解决不同噪声水平下的去噪子问题。随后，利用三维残差白化度量来调整求解二次子问题时的惩罚参数，同时作为停止准则。在模拟数据和带真实值的实际数据上的实验结果表明了所提方法的优越性。