Image restoration is essentially recognized as an ill-posed problem. A promising solution in recent years is incorporating deep network-driven priors into the iterative restoration procedure as constrained conditions. Among them, deep mean-shift prior utilizes the denoising autoencoder to play the role of prior updating. In this study, we present multiple wavelets guided deep mean-shift prior, which integrates the advantages of structural representation in the wavelet transform and learning ability in deep network. Specifically, by re-arranging the multi-view and multi-resolution features generated by multiple wavelet transforms as the input of denoising autoencoder, a more powerful prior information is learned. It benefits from the recurrent structure-preserving and multi-view complementary aggregation properties. We embed the learned prior information into the iterative recovery process and adopt proximal gradient descent to tackle it. Extensive experiments on image deblurring and compressed sensing tasks demonstrated significantly improved performances both visually and quantitatively.

图像恢复本质上被认为是一个不适定的问题。近年来一个有前途的解决方案是将深度网络驱动的先验作为约束条件纳入迭代恢复过程。其中，deep mean-shiftprior利用去噪自编码器起到先验更新的作用。在这项研究中，我们提出了多个小波引导的深度均值偏移先验，它结合了小波变换中结构表示的优点和深度网络中的学习能力。具体来说，通过将多次小波变换产生的多视图、多分辨率特征重新排列作为去噪自编码器的输入，学习到更强大的先验信息。它受益于循环结构保留和多视图互补聚合特性。我们将学习到的先验信息嵌入到迭代恢复过程中，并采用近端梯度下降来解决它。对图像去模糊和压缩感知任务的大量实验表明，在视觉和数量上都显着提高了性能。