Multi-wavelet guided deep mean-shift prior for image restoration

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. [Display omitted] •The model inherits structural representation in wavelet and learning ability in CNN.•Multi-channel features generated by multi-wavelets is rearranged as the network input.•It involves the recurrent structure-preserving and multi-view aggregation properties.