Image restoration (IR) is a long-standing challenging problem in low-level image processing. It is of utmost importance to learn good image priors for pursuing visually pleasing results. In this paper, we develop a multi-channel and multi-model-based denoising autoencoder network as image prior for solving IR problem. Specifically, the network that trained on RGB-channel images is used to construct a prior at first, and then the learned prior is incorporated into single-channel grayscale IR tasks. To achieve the goal, we employ the auxiliary variable technique to integrate the higher-dimensional network-driven prior information into the iterative restoration procedure. In addition, according to the weighted aggregation idea, a multi-model strategy is put forward to enhance the network stability that favors to avoid getting trapped in local optima. Extensive experiments on image deblurring and deblocking tasks show that the proposed algorithm is efficient, robust, and yields state-of-the-art restoration quality on grayscale images.

中文翻译：

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用于灰度图像恢复的多通道和基于多模型的自动编码先验。

在低级图像处理中，图像恢复（IR）是一个长期存在的挑战性问题。对于追求视觉愉悦的结果，学习良好的图像先验至关重要。在本文中，我们开发了一种基于多通道，基于多模型的去噪自动编码器网络作为图像处理红外问题的先决条件。具体而言，首先使用在RGB通道图像上训练的网络来构造先验，然后将获知的先验合并到单通道灰度IR任务中。为了实现该目标，我们采用辅助变量技术将高维网络驱动的先验信息集成到迭代恢复过程中。另外，根据加权聚合的思想，提出了一种多模型策略来增强网络的稳定性，有利于避免陷入局部最优状态。