MB-DAMPNet: a novel multi-branch denoising-based approximate message passing algorithm via deep neural network for image reconstruction

Compressive sensing has gained great attention due to its effectiveness in solving linear inverse problems. However, how to further improve the accuracy of compressed image inversion while ensuring or even accelerating the speed is still a major challenge. To tackle this problem, we present a novel multi-branch denoising-based approximate message passing algorithm via deep neural network, dubbed MB-DAMPNet. It mainly consists of three components, i.e. sampling subnet, initial recovery subnet, and deep recovery subnet, which are optimized jointly. The sampling subnet is constructed to obtain the compressed measurements, the initial recovery subnet is employed to generate the reconstructed image by inverse transformation, while the deep recovery subnet is designed to refine the reconstructed results obtained by the former, so as to improve the image accuracy. Moreover, the matrix multiplication in the network is all designed as matrix convolution which can be learned automatically, so that the input image of the MB-DAMPNet can be of different scales, which improves the flexibility and applicability of the network. In addition, all parameters in the network are learned end-to-end instead of fixed or hand-crafted. The numerical results validate that our method significantly outperforms other state-of-the-art methods in image reconstruction accuracy.