A hybrid wavelet convolution network with sparse-coding for image super-resolution
This paper proposes a hybrid wavelet convolution network (HWCN) which is composed of a scattering convolution component and a convolution neural component. The hierarchical end-to-end network implements sparse-coding and high-dimensional reconstruction for inverse problem through cascade convolutions. With the pre-defined scattering convolutions from nonlinear operators, the network can be tailored in accordance with the frequency property to provide sparse code candidates, and the convolution neural component could automatically select and weight these candidates for sparse coding. Given a tiny dataset, HWCN could train complex deep network with better generalization by regularization from scattering convolutions, and thereby is a competitive alternative to convolutional neural networks (CNN). Moreover, we further demonstrate that HWCN is a superior selection of sparse-coding based image super-resolution and achieves state-of-the-art performance.