The rapidly growing requirement for data has put forward Compressed Sensing (CS) to realize low-ratio sampling and to reconstruct complete signals. With the intensive development of Deep Neural Network (DNN) methods, performance in image reconstruction from CS measurements is constantly increasing. Currently, many network structures pay less attention to the relevance of before- and after-stage results and fail to make full use of relevant information in the compressed domain to achieve interblock information fusion and a great receptive field. Additionally, due to multiple resamplings and several forced compressions of information flow, information loss and network structure redundancy inevitably result. Therefore, an Information Enhancement and Fusion Network for CS reconstruction (IEF-CSNET) is proposed in this work, and a Compressed Information Extension (CIE) module is designed to fuse the compressed information in the compressed domain and greatly expand the receptive field. The Error Comprehensive Consideration Enhancement (ECCE) module enhances the error image by incorporating the previous recovered error so that the interlink among the iterations can be utilized for better recovery. In addition, an Iterative Information Flow Enhancement (IIFE) module is further proposed to complete the progressive recovery with loss-less information transmission during the iteration. In summary, the proposed method achieves the best effect, exhibits high robustness at this stage, with the peak signal-to-noise ratio (PSNR) improved by 0.59 dB on average under all test sets and sampling rates, and presents a greatly improved speed compared with the best algorithm.
The rapid growth of sensing data demands compressed sensing (CS) in order to achieve high-density storage and fast data transmission. Deep neural networks (DNNs) have been under intensive development for the reconstruction of high-quality images from compressed data. However, the complicated auxiliary structures of DNN models in pursuit of better recovery performance lead to low computational efficiency and long reconstruction times. Furthermore, it is difficult for conventional neural network designs to reconstruct extra-high-frequency information at a very low sampling rate. In this work, we propose an efficient iterative neural network for CS reconstruction (EiCSNet). An efficient gradient extraction module is designed to replace the complex auxiliary structures in order to train the DNNs more efficiently. An iterative enhancement network is applied to make full use of the limited information available in CS for better iterative recovery. In addition, a frequency-aware weighted loss is further proposed for better image restoration quality. Our proposed compact model, EiCSNet2*1, improved the performance slightly and was nearly seven times faster than its counterparts, which shows that it has a highly efficient network design. Additionally, our complete model, EiCSNet6*1, achieved the best effect at this stage, where the average PSNR was improved by 0.37 dB for all testing sets and sampling rates.
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