A novel multi-dictionary framework with global sensing matrix design for compressed sensing

In this paper, a new compressed sensing (CS) system is proposed to reduce computational burden of dictionary learning and improve reconstruction accuracy. The proposed CS system employs a novel framework which contains multiple dictionaries. In multi-dictionary framework, the whole training dataset is divided into multiple subdatasets for optimizing multiple dictionaries. Dictionary learning process can be accelerated due to the parallel computation and the reduction of training dataset size. Each dictionary can get an image (called snapshot) independently with the same measurements in the image reconstruction process. These snapshots will be fused to be one image with averaging strategy. In order to keep the measurement size of the proposed CS system same as that of traditional CS system and improve reconstruction accuracy, a new method of designing global sensing matrix for multi-dictionary framework is also explored. Experiments demonstrate the effectiveness of new framework and the method to design global sensing matrix. Compared with other CS systems, the proposed CS system shows a superior performance for real images. (C) 2018 Elsevier B.V. All rights reserved.