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A Deep Network Based on Wavelet Transform for Image Compressed Sensing

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Abstract

Most conventional compressed sensing (CS) algorithms are impaired by the fact that the optimization of image reconstruction suffers from the need for multiple iterative calculations. Recently, deep learning-based CS algorithms have been proposed and they dramatically achieve efficient reconstruction and fast computing speed with fewer sampling measurements than traditional iterative optimization-based algorithms. However, the sampling process of common deep learning-based CS and traditional CS generally cannot sufficiently exploit the structural sparsity of image sequences to effectively conduct CS research. Motivated by the fact that a sparser signal is easier to reconstruct accurately, in this paper, we propose two novel algorithms called the WCS-Nets (WCS-Net and WCS-Net\(^+\)), which synthesize the advantages of a sampling network based on sparse representation and a deep elastic reconstruction network. WCS-Net is an improvement in DR\(^2\)-Net, and its primary innovation focuses on combining the sym8 wavelet transform with a sampling network. Moreover, considering that multi-scale residual learning has better reconstruction efficiency, an enhanced version, called WCS-Net\(^+\), is designed in the deep elastic reconstruction network and further improves the reconstruction accuracy. Experimental results demonstrate that the proposed methods achieve better results when compared with other state-of-the-art deep learning-based and traditional CS algorithms in terms of reconstruction quality, runtime and robustness to noise.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work was mainly supported by Key Research and Development Project of Hefei University Science Center, Chinese Academy of Sciences (Grant No. E06ACK123Z1).

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Authors and Affiliations

  1. High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China

    Zhu Yin, Zhongcheng Wu & Jun Zhang

  2. University of Science and Technology of China, Hefei, 230026, China

    Zhu Yin & Zhongcheng Wu

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  1. Zhu Yin
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  2. Zhongcheng Wu
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Correspondence to Zhu Yin.

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Yin, Z., Wu, Z. & Zhang, J. A Deep Network Based on Wavelet Transform for Image Compressed Sensing. Circuits Syst Signal Process 41, 6031–6050 (2022). https://doi.org/10.1007/s00034-022-02058-8

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