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Adaptive compressed sensing of color images based on salient region detection

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Abstract

We propose a novel algorithm for color image compressed sensing (CS). Our method involves the adaptive measurement and reconstruction of color images based on visual saliency detection. First, we divide the image into blocks and transform the RGB channel into the YUV channel. Secondly, we use statistical texture distinctiveness to calculate the saliency of each block and normalize energy, thereby establishing an adaptive measurement rate and measurement matrix. Thirdly, we adaptively measure the Y channel according to block prominence and preserve the information of the UV channel. During reconstruction, we utilize adaptive block measurement rate to re-estimate block saliency and then reconstruct the objective function of the weighted reconstruction model according to the re-estimated block saliency. Finally, we combine the reconstructed Y channel with the reserved UV channel to obtain the final image. Experimental results show that compared with other state-of-the-art approaches, the proposed algorithm can not only provide good subjective visual quality but can also present higher peak signal to noise ratio (PSNR) under the same sampling rate.

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Acknowledgments

This work is supported by the NEPU Natural Science Foundation under Grant No. 2017PYZL-05, JYCX_CX06_2018 and JYCX_JG06_2018.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Northeast Petroleum University, Daqing, China

    Zheng Zhang, Hongbo Bi, Xiaoxue Kong, Ning Li & Di Lu

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  1. Zheng Zhang
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  2. Hongbo Bi
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  3. Xiaoxue Kong
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  4. Ning Li
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  5. Di Lu
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Correspondence to Hongbo Bi.

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Zhang, Z., Bi, H., Kong, X. et al. Adaptive compressed sensing of color images based on salient region detection. Multimed Tools Appl 79, 14777–14791 (2020). https://doi.org/10.1007/s11042-018-7062-6

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  • DOI: https://doi.org/10.1007/s11042-018-7062-6

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