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Saliency Detection for Compressive Sensing Measurements

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Abstract

Compressive sensing can obtain high-quality image reconstruction at a lower sampling rate. Using image saliency for compressive sensing measurement and reconstruction can effectively improve the image quality of reconstruction. For this reason, we propose a method of saliency detection using compressive sensing global measurement values. This method uses the characteristics of the Hadamard matrix to reconstruct the original image with low resolution, and then uses the low resolution image for saliency detection. This method takes advantage of the feature that saliency detection does not require high-resolution images. At the same time, low-resolution images are also conducive to neural network reconstruction. Experimental results prove that low-resolution images can indeed obtain better saliency detection results. In order to further improve the saliency detection results, we also propose a saliency detection method with an adaptive measurement matrix. Experiments show that our method can obtain better reconstruction quality and saliency maps with a set of trained compressive sensing measurement matrices.

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Funding

The funding was provided by the National Natural Science Foundation of China (62032022, 61972375, 61871258, 61671426) and Fundamental Research Funds for the Central Universities (E0E48980).

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Authors and Affiliations

  1. University of Chinese Academy of Sciences, Beijing, 100049, China

    Hongliang Li, Ke Lu & Jian Xue

  2. Institute of Computing Technology, CAS, Beijing, 100190, China

    Feng Dai

  3. University of Science and Technology of China, Hefei, 230027, China

    Yongdong Zhang

Authors
  1. Hongliang Li
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  2. Ke Lu
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  3. Jian Xue
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  4. Feng Dai
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  5. Yongdong Zhang
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Correspondence to Hongliang Li.

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Li, H., Lu, K., Xue, J. et al. Saliency Detection for Compressive Sensing Measurements. Sens Imaging 22, 43 (2021). https://doi.org/10.1007/s11220-021-00365-z

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  • DOI: https://doi.org/10.1007/s11220-021-00365-z

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