Image restoration (IR) has been extensively studied with lots of excellent strategies accumulated over the years. However, most existing methods still have large room for improvement. In this paper, we boost an unsupervised iterative feature refinement model (IFR) with the enhanced high-dimensional deep mean-shift prior (EDMSP), termed IFR-EDMSP. The proposed model inherits the fantastic noise suppression characteristic of embedded network and the fine detail preservation ability of IFR model. Moreover, based on the fact that multiple implementations of artificial noise in prior learning improve underlying representation capability, three-sigma rule is adopted in IFR-EDMSP for accurate and robust results. Extensive experiments demonstrated that IFR-EDMSP outperforms the typical methods in compressed sensing, image deblurring and super-resolution.

图像恢复（IR）已经被广泛研究，多年来积累了许多优秀的策略。然而，大多数现有方法仍有很大的改进空间。在本文中，我们使用增强的高维深度均值偏移先验 (EDMSP) 来提升无监督迭代特征细化模型 (IFR)，称为 IFR-EDMSP。所提出的模型继承了嵌入式网络出色的噪声抑制特性和IFR模型的精细细节保留能力。此外，基于在先验学习中人工噪声的多种实现提高了底层表示能力的事实，在 IFR-EDMSP 中采用了三西格玛规则以获得准确和稳健的结果。大量实验表明，IFR-EDMSP 在压缩感知、图像去模糊和超分辨率方面优于典型方法。