IEEE Trans Image Process. 2020;29:142-156. doi: 10.1109/TIP.2019.2931240. Epub 2019 Jul 31.
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.
图像恢复（IR）是低水平图像处理中的一个长期存在的挑战性问题。学习良好的图像先验对于获得令人满意的视觉效果至关重要。在本文中，我们开发了一种基于多通道和多模型的去噪自动编码器网络作为图像先验，用于解决 IR 问题。具体来说，首先使用在 RGB 通道图像上训练的网络来构建先验，然后将学习到的先验信息纳入单通道灰度 IR 任务中。为了实现这一目标，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。此外，根据加权聚合思想，提出了一种多模型策略，以增强网络稳定性，有利于避免陷入局部最优。在图像去模糊和去块任务上的广泛实验表明，所提出的算法效率高、鲁棒性强，在灰度图像上可实现最先进的恢复质量。
