Iterative reconstruction of compressive sensing combining image hierarchical-feature
The compressive sensing image reconstruction algorithms based on Convolutional Neural Net-works could not capture long-range dependency of high-resolution images.Although Transformer can ad-dress this issue,it significantly increases the number of network parameters and the image reconstruction time.This paper proposed CHFNet,a combining image hierarchical-feature network for compressive sensing iterative-reconstruction to improve image reconstruction quality and reduce reconstruction time.CHFNet consisted of two sub-networks,sampling and reconstruction.The sampling sub-network utilized a learnable sampling matrix to provide more effective measurements for reconstruction phase.In the recon-struction sub-network,we introduced an iterative strategy using gradient descent and feature optimization operations,and proposed a lightweight CNN-Transformer hybrid architecture to model and optimize ex-tremely fine-grained image hierarchical-feature,enhancing network's sensing-capability and reducing com-putation complexity.Moreover,CHFNet achieved complete end-to-end training by jointly optimizing sam-pling-reconstruction process.The experimental results show that the proposed algorithm obtains satisfacto-ry recovery performance on several public benchmark datasets.On the Urban100 dataset,the method of this paper improves the average PSNR and SSIM metrics by 0.63 dB and 0.007 6 respectively compared to the existing optimal algorithm CSformer.At 0.10 sampling rate,the average reconstruction time of CHFNet decreases 2.744 7 s,3.551 0 s,and 4.775 0 s compared to CSformer on Set11,BSD68,and Urban100 datasets respectively.
万方数据
维普
