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Entropy-assisted adaptive compressive sensing for energy-efficient visual sensors

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Abstract

Compressive Imaging (CI) is a potential sensing technology for energy-efficient visual sensors, and its rate-distortion performance can be improved by adaptive Compressive Sensing (CS) of image. However, due to the unavailability of original image, it is a challenge for CI-based adaptive CS to extract an effective feature from measurements to evaluate the sparsity of image. In view of that, this paper presents an entropy-assisted adaptive CS system, whose merit is its definition of the sensed entropy without the original image. Based on sensed entropy, each image block is allocated sufficient measuring resources, guaranteeing a cost-effective reconstruction of image. Experimental results show that the proposed entropy-assisted adaptive CS system provides better objective and subjective recovery qualities with a low measuring and recovering complexity.

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References

  1. Baraniuk R (2007) Compressive sensing [Lecture Notes]. IEEE Signal Process Mag 24:118–121. https://doi.org/10.1109/CISS.2008.4558479

    Article  Google Scholar 

  2. Baraniuk R, Davenport M, Devore R, Wakin M (2008) A simple proof of the restricted isometry property for random matrices. Constr Approx 45:113–127. https://doi.org/10.1007/s00365-007-9003-x

    Article  MathSciNet  MATH  Google Scholar 

  3. Baraniuk R, Goldstein T, Sankaranarayanan AC, Christoph S, Veeraraghavan A, Wakin MB (2017) Compressive video sensing: algorithms, architectures, and applications. IEEE Signal Process Mag 34:52–66. https://doi.org/10.1109/MSP.2016.2602099

    Article  Google Scholar 

  4. Becker S, Bobin J, Candès EJ (2011) NESTA: a fast and accurate first-order method for sparse recovery. SIAM J Imag Sci 4:1–39. https://doi.org/10.1137/090756855

    Article  MathSciNet  MATH  Google Scholar 

  5. Bioucas-Dias JM, Figueiredo MAT (2007) A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration. IEEE Trans Image Process 16:2992–3004. https://doi.org/10.1109/TIP.2007.909319

    Article  MathSciNet  Google Scholar 

  6. Candès E, Romberg J (2007) Sparsity and incoherence in compressive sampling. Inverse Prob 23:969–985. https://doi.org/10.1088/0266-5611/23/3/008

    Article  MathSciNet  MATH  Google Scholar 

  7. Chen C, Tramel EW, Fowler JE (2011) Compressed-sensing recovery of images and video using multihypothesis predictions. In: 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 6–9 November 2011. IEEE, Pacific Grove, pp 1193–1198. https://doi.org/10.1109/ACSSC.2011.6190204

  8. Chen Z, Hon XS, Shao L, Gong C, Qian XM, Huang Y, Wang SD (2019) Compressive sensing multi-layer residual coefficients for image coding. IEEE Trans Circuits Syst Video Technol 30:1109–1120. https://doi.org/10.1109/TCSVT.2019.2898908

    Article  Google Scholar 

  9. Do TT, Gan L, Nguyen NH, Tran TD (2012) Fast and efficient compressive sensing using structurally random matrices. IEEE Trans Signal Process 60:139–154. https://doi.org/10.1109/TSP.2011.2170977

    Article  MathSciNet  MATH  Google Scholar 

  10. Duarte M, Wakin M, Baraniuk R (2005) Fast reconstruction of piecewise smooth signals from incoherent projections. Workshop on Signal Processing with Adaptive Sparse Structured Representations 1–4

  11. Elhoseny M, Hosny A, Hassanien AE, Muhammad K, Sangaiah AK (2017) Secure automated forensic investigation for sustainable critical infrastructures compliant with green computing requirements. IEEE Transactions on Sustainable Computing. https://doi.org/10.1109/TSUSC.2017.2782737

  12. Figueiredo MT, Nowak RD, Wright SJ (2007) Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems. IEEE J Sel Top Sign Proces 1:586–597. https://doi.org/10.1109/JSTSP.2007.910281

    Article  Google Scholar 

  13. Fowler JE, Mun S, Tramel EW (2010) Block-based compressed sensing of images and video. Foundations and Trends? in Signal Processing 4:297–416. https://doi.org/10.1561/2000000033

    Article  MATH  Google Scholar 

  14. Gan L (2007) Block compressed sensing of natural images. Proceedings of the 15th International Conference on Digital Signal Processing, Cardiff, UK, pp 403–406, doi: https://doi.org/10.1109/ICDSP.2007.4288604

  15. Gharbia R, Hassanien AE, Hassan EB, Mohamed E, Gunasekaran M (2018) Multi-spectral and panchromatic image fusion approach using stationary wavelet transform and swarm flower pollination optimization for remote sensing applications. Futur Gener Comput Syst 88:501–511. https://doi.org/10.1016/j.future.2018.06.022

    Article  Google Scholar 

  16. Ivan R, Damir S, Davor P (2019) Off-the-shelf measurement setup for compressive imaging. IEEE Trans Instrum Meas 68:502–511. https://doi.org/10.1109/TIM.2018.2847018

    Article  Google Scholar 

  17. Kareth LL, Laura G, Henry A (2019) Temporal colored coded aperture design in compressive spectral video sensing. IEEE Trans Image Process 28:253–264. https://doi.org/10.1109/TIP.2018.2867171

    Article  MathSciNet  MATH  Google Scholar 

  18. Li R, Duan XM, Guo XL, He W, Lv YF (2017) Adaptive compressive sensing of images using spatial entropy. Comput Intell Neurosci 2017:1–9. https://doi.org/10.1155/2017/9059204

    Article  Google Scholar 

  19. Memos VA, Psannis KE, Ishibashi Y, Kim BG, Gupta BB (2018) An efficient algorithm for media-based surveillance system (EAMSuS) in IoT smart city framework. Futur Gener Comput Syst 83:619–628. https://doi.org/10.1016/j.future.2017.04.039

    Article  Google Scholar 

  20. Muhammad K, Lloret J, Baik SW (2019) Intelligent and energy-efficient data prioritization in green smart cities: current challenges and future. IEEE Commun Mag 57:60–65. https://doi.org/10.1109/MCOM.2018.1800371

    Article  Google Scholar 

  21. Mun S, Fowler JE (2010) Block compressed sensing of images using directional transforms. IEEE International Conference on Image Processing, Snowbird, UT, USA, pp. 3021–3024, doi: https://doi.org/10.1109/DCC.2010.90

  22. Plageras AP, Psannis KE, Stergiou C, Wang H, Gupta BB (2018) Efficient IoT-based sensor BIG data collection–processing and analysis in smart buildings. Futur Gener Comput Syst 82:349–357. https://doi.org/10.1016/j.future.2017.09.082

    Article  Google Scholar 

  23. Psannis KE, Ishibashi Y (2006) Impact of video coding on delay and jitter in 3G wireless video multicast services. EURASIP J Wirel Commun Netw 024616:1–7. https://doi.org/10.1155/WCN/2006/24616

    Article  Google Scholar 

  24. Psannis KE, Stergiou C, Gupta BB (2018) Advanced media-based smart big data on intelligent cloud systems. IEEE Transactions on Sustainable Computing 4(1):77–87. https://doi.org/10.1109/TSUSC.2018.2817043

    Article  Google Scholar 

  25. Shen Y, Li S (2015) Sparse signals recovery from noisy measurements by orthogonal matching pursuit. Inverse Problems & Imaging 9:231–238. https://doi.org/10.1109/TIP.2007.909319

    Article  MathSciNet  MATH  Google Scholar 

  26. Stergiou C, Psannis K, Kim E, Gupta BG, B (2018) Secure integration of IoT and cloud computing. Futur Gener Comput Syst 78:964–975. https://doi.org/10.1016/j.future.2016.11.031

  27. Vargas E, Espitia Ó, Arguello H, Tourneret JY (2019) Spectral image fusion from compressive measurements. IEEE Trans Image Process 28:2271–2282. https://doi.org/10.1109/TIP.2018.2884081

    Article  MathSciNet  Google Scholar 

  28. Wang AH, Liu L, Zeng B, Bai HH (2011) Progressive image coding based on an adaptive block compressed sensing. IEICE Electronics Express 8(8):575–581. https://doi.org/10.1587/elex.8.575

    Article  Google Scholar 

  29. Wang LZ, Zhang T, Fu Y, Huang H (2019) HyperReconNet: joint coded aperture optimization and image reconstruction for compressive hyperspectral imaging. IEEE Trans Image Process 28:2257–2270. https://doi.org/10.1109/TIP.2018.2884076

    Article  MathSciNet  Google Scholar 

  30. Wei ZR, Zhang JL, Xu ZY, Liu Y, Huang YM, Fan XS (2019) Improving the signal-to-noise ratio of superresolution imaging based on single-pixel camera. IEEE Photonics J 11:1–16. https://doi.org/10.1109/JPHOT.2019.2891061

    Article  Google Scholar 

  31. Wu M, Zhu X, Gan Z, Li X (2012) Adaptive dictionary learning for distributed compressive video sensing. International Journal of Digital Content Technology & its Applications 6:141–149. https://doi.org/10.4156/jdcta.vol6.issue4.17

    Article  Google Scholar 

  32. Xue JZ, Zhao YQ, Liao WZ, Chan JC-W (2019) Nonlocal tensor sparse representation and low-rank regularization for hyperspectral image compressive sensing reconstruction. Remote Sens 11:193. https://doi.org/10.3390/rs11020193

    Article  Google Scholar 

  33. Yu Y, Wang B, Zhang L (2010) Saliency-based compressive sampling for image signals. IEEE Signal Process Lett 17:973–976. https://doi.org/10.1109/LSP.2010.2080673

    Article  Google Scholar 

  34. Zhang JG, Xiang QM, Yin YG, Chen C, Luo X (2017) Adaptive compressed sensing for wireless image sensor networks. Multimed Tools Appl 76:4227–4242. https://doi.org/10.1007/s11042.016.3496.x

    Article  Google Scholar 

  35. Zheng S, Zhang X, Chen J, Kuo Y (2019) A high-efficiency compressed sensing based terminal-to-cloud video transmission system. IEEE Trans Multimedia 21:1905–1920. https://doi.org/10.1109/TMM.2019.2891415

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61601396, 31872704, in part by Innovation Team Support Plan of University Science and Technology of Henan Province (no. 19IRTSTHN014).

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  1. School of Computer and Information Technology, Xinyang Normal University, Xinyang, 464000, China

    Ran Li, Xiaomeng Duan, Wei He & Lei You

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  1. Ran Li
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Li, R., Duan, X., He, W. et al. Entropy-assisted adaptive compressive sensing for energy-efficient visual sensors. Multimed Tools Appl 79, 20821–20843 (2020). https://doi.org/10.1007/s11042-020-08900-y

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