Capsule networks embedded with prior known support information for image reconstruction

Compressed sensing (CS) has been successfully applied to realize image reconstruction. Neural networks have been introduced to the CS of images to exploit the prior known support information, which can improve the reconstruction quality. Capsule Network (Caps Net) is the latest achievement in neural networks, and can well represent the instantiation parameters of a specific type of entity or part of an object. This study aims to propose a Caps Net with a novel dynamic routing to embed the information within the CS framework. The output of the network represents the probability that the index of the nonzero entry exists on the support of the signal of interest. To lead the dynamic routing to the most likely index, a group of prediction vectors is designed determined by the information. Furthermore, the results of experiments on imaging signals are taken for a comparation of the performances among different algorithms. It is concluded that the proposed capsule network (Caps Net) creates higher reconstruction quality at nearly the same time with traditional Caps Net.