Residual dense network for image compression sensing

Yasong Bai; Xiangjun Wu

doi:10.1117/12.2673900

6 April 2023 Residual dense network for image compression sensing

Yasong Bai, Xiangjun Wu

Proceedings Volume 12615, International Conference on Signal Processing and Communication Technology (SPCT 2022); 126150N (2023) https://doi.org/10.1117/12.2673900
Event: International Conference on Signal Processing and Communication Technology (SPCT 2022), 2022, Harbin, China

Abstract

Traditional compressed sensing image reconstruction algorithms have poor image reconstruction quality at low sampling rates and is time-consuming due to the iterative optimization process. Deep learning-based compressed sensing reconstruction algorithms can significantly reduce time complexity and improve reconstruction quality compared to traditional iterative reconstruction algorithms. In this paper, we propose a new residual dense reconstruction network (RDRNet) for compressed sensing image reconstruction. RDRNet consists of a sampling network and a reconstruction network. The reconstruction network uses multiple residual dense blocks to extract image information. The residual dense blocks can be used to extract rich detail features by fusing local features to obtain the final reconstructed image. Through experimental validation on different datasets, the network has better image reconstruction quality and comparable runtime complexity compared to the existing DCS model.

Citation Download Citation

Yasong Bai and Xiangjun Wu "Residual dense network for image compression sensing", Proc. SPIE 12615, International Conference on Signal Processing and Communication Technology (SPCT 2022), 126150N (6 April 2023); https://doi.org/10.1117/12.2673900

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