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A multi-level residual reconstruction based image compressed sensing recovery scheme

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Abstract

Traditional image compressed sensing recovery focuses on the research of sparse representation and reweight processing of measurements. However, the image content and structural feature vary dramatically in different natural images. The improvements in reconstruction quality brought by exploring new sparse representation models are not satisfactory. This paper focuses on a multimedia application scenario where the encoder is resource-constrained and the decoder has powerful computing ability. A novel image compressed sensing recovery scheme based on multi-level residual reconstruction is proposed to further improve the reconstruction quality. By converting the original image recovery to the multi-level residual image recovery, the reconstruction process is divided into three phases. The hidden information in image recovery is fully utilized. Moreover, a constraint-adaptive recovery model is proposed to perform the initial reconstruction of the original image and the initial residual image reconstruction. Combining the multihypothesis prediction, the final recovered residual image is obtained in the secondary residual image recovery phase. The final recovered image is obtained by combining the recovered original image and the residual image. Experimental results show that our proposal outperforms the state-of-the-art methods for image compressed sensing reconstruction in both objective and subjective quality.

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References

  1. Afonso MV, Bioucas-Dias JM, Figueiredo MAT (2010) Fast image recovery using variable splitting and constrained optimization. IEEE Trans Image Process 19(9):2345–2356

    Article  MathSciNet  MATH  Google Scholar 

  2. Aharon M, Elad M, Bruckstein A (2006) K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54(11):4311–4322

    Article  MATH  Google Scholar 

  3. Baraniuk RG (2007) Compressive sensing. IEEE Signal Process Mag 24(4):118–121

    Article  Google Scholar 

  4. Becker S, Bobin J, Candes E (2011) NEST: a fast and accurate first-order method for sparse recovery. SIAM J Imaging Sci 4(1):1–39

    Article  MathSciNet  MATH  Google Scholar 

  5. Bioucas-Dias J, Figueiredo M (2007) A new TwIST: two-step iterative shrinkage thresholding algorithms for image restoration. IEEE Trans Image Process 16(12):2992–3004

    Article  MathSciNet  Google Scholar 

  6. Bruckstein AM, Donoho DL, Elad M (2009) From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Rev 51(1):34–81

    Article  MathSciNet  MATH  Google Scholar 

  7. Candes EJ (2006) Compressive sampling. Marta Sanz Solé 25(2):1433–1452

    MathSciNet  MATH  Google Scholar 

  8. Candes EJ, Wakin MB, Boyd SP (2008) Enhancing sparsity by reweighted L1 minimization. Fourier Anal Appl 14(5):877–905

    Article  MathSciNet  MATH  Google Scholar 

  9. Chen C, Tramel EW, Fowler JE (2011) Compressed-sensing recovery of images and video using multi-hypothesis predictions. In Proceedings of the 45th Asilomar conference on signals, systems, and computers, Pacific Grove, CA, USA, pp 1193–1198

  10. Chen J, Chen Y, Qin D, Kuo YH (2015) An elastic net-based hybrid hypothesis method for compressed video sensing. Multimed Tools Appl 74(6):2085–2108

    Article  Google Scholar 

  11. Chen J, Gao YT, Ma CH et al (2017) Compressive sensing image reconstruction based on multiple regulation constraints. Circuits, Systems, and Signal Processing 36(4):1621–1638

    Article  Google Scholar 

  12. Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306

    Article  MathSciNet  MATH  Google Scholar 

  13. Gan L (2007) Block compressed sensing of natural images. In Proceedings of the International Conference on Digital Signal Processing, Cardiff, UK, pp 403–406

  14. Hao W, Li J (2015) Alternating total variation and non-local total variation for fast compressed sensing magnetic resonance imaging. Electron Lett 51(22):1740–1742

    Article  Google Scholar 

  15. He L, Carin L (2009) Exploiting structure in wavelet-based Bayesian compressive sensing. IEEE Trans Signal Process 57(9):3488–3497

    Article  MathSciNet  MATH  Google Scholar 

  16. He L, Chen H, Carin L (2010) Tree-structured compressive sensing with variational Bayesian analysis. IEEE Signal Process Lett 17(3):233–236

    Article  Google Scholar 

  17. Hosseini MS, Plataniotis KN (2014) High-accuracy total variation with application to compressed video sensing. IEEE Trans Image Process 23(9):3869–3884

    Article  MathSciNet  MATH  Google Scholar 

  18. Li C (2009) An efficient algorithm for total variation regularization with applications to the single pixel camera and compressive sensing. M.S. thesis, Department of Computational and Applied Mathematics, Rice University

  19. Li C, Yin W, Zhang Y (2009) TVAL3: TV minimization by augmented Lagrangian and alternating direction algorithm. http://www.caam.rice.edu/~optimization/L1/TVAL3/. Accessed May 2017

  20. Li H, Zeng Y, Yang N (2018) Image reconstruction for compressed sensing based on joint sparse bases and adaptive sampling. Mach Vis Appl 29(1):145–157

    Article  Google Scholar 

  21. Ma S, Yin W, Zhang Y et al (2008) An efficient algorithm for compressed MR imaging using total variation and wavelets. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA, pp 1–8

  22. Mun S, Fowler JE (2009) Block compressed sensing of images using directional transforms. In Proceedings of the IEEE International Conference on Image Processing, Cairo, Egypt, pp 3021–3024

  23. Mun S, Fowler JE (2011) Residual reconstruction for block-based compressed sensing of video. In Proceedings of the Data Compression Conference, Snowbird, UT, USA, pp 183–192

  24. Rudin L, Osher S, Fatemi E (1992) Nonlinear total variation based noise removal algorithms. Physica D 60(1):259–268

    Article  MathSciNet  MATH  Google Scholar 

  25. Sadeghipoor Z, Lu YM, Susstrunk S (2013) A novel compressive sensing approach to simultaneously acquire color and near-infrared images on a single sensor. In Proceedings of the IEEE international conference on acoustics, speech and signal processing, Vancouver, BC, Canada, pp 1–5

  26. Shu X, Ahuja N (2010) Hybrid compressive sampling via a new total variation TVL1. In Proceedings of the European conference on computer vision, pp 393–404

  27. Song X, Peng X, Xu J et al (2016) Compressive sensing based image transmission with side information at the decoder. In Proceedings of the IEEE visual communications and image processing, Singapore, pp 1–4

  28. Song X, Peng X, Xu J et al (2017) Distributed compressive sensing for cloud-based wireless image transmission. IEEE Trans Multimed 19(6):1351–1364

    Article  Google Scholar 

  29. Takhar D, Laska JN, Wakin MB et al (2006) A new compressive imaging camera architecture 8using optical-domain compression. In Proceedings of the computational imaging IV at SPIE electronic imaging, pp 43–52

  30. Wang N, Li J (2011) Block adaptive compressed sensing of SAR images based on statistical character. In Proceedings of the IEEE International Conference of Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada, pp 640–643

  31. Wang Y, Bai H, Zhao L et al (2018) Cascaded reconstruction network for compressive image sensing. EURASIP J Image Video Process 2018(1):77

    Article  Google Scholar 

  32. Xiao YH, Song HN (2012) An inexact alternating directions algorithm for constrained total variation regularized compressive sensing problems. J Math Imaging Vision 44(2):114–127

    Article  MathSciNet  MATH  Google Scholar 

  33. Xu J, Ma J, Zhang D et al (2012) Improved total variation minimization method for compressive sensing by intra-prediction. Signal Process 92(11):2614–2623

    Article  Google Scholar 

  34. Yao H, Dai F, Zhang D et al (2017) DR2-net: deep residual reconstruction network for image compressive sensing. arXiv preprint. arXiv: 1702.05743

  35. Zhang L, Zhang L, Mou X et al (2011) FSIM: a feature similarity index for image quality assessment. IEEE Trans Image Process 20(8):2378–2386

    Article  MathSciNet  MATH  Google Scholar 

  36. Zhang J, Liu S, Xiong R, et al (2012) Improved total variation based image compressive sensing recovery by nonlocal regularization. In Proceedings of the IEEE international symposium on circuits and systems, Beijing, China, pp 2836–2839

  37. Zhang J, Zhao D, Zhao C et al (2012) Image compressive sensing recovery via collaborative sparsity. IEEE J Emerging Sel Top Circuits Syst 2(3):287–296

    MathSciNet  Google Scholar 

  38. Zhang J, Zhao C, Zhao D et al (2014) Image compressive sensing recovery using adaptively learned sparsifying basis via L0 minimization. Signal Process 103(10):114–126

    Article  Google Scholar 

  39. Zhang J, Zhao D, Gao W (2014) Group-based sparse representation for image restoration. IEEE Trans Image Process 23(8):3336–3351

    Article  MathSciNet  MATH  Google Scholar 

  40. Zhao C, Ma S, Zhang J et al (2016) Video compressive sensing reconstruction via reweighted residual sparsity. IEEE Trans Circuits Syst Video Technol 27(6):1182–1195

    Article  Google Scholar 

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Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant No. 61771366) and the “111” project (Grant No. B08038).

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Authors and Affiliations

  1. School of Telecommunications Engineering, Xidian University, Xi’an, 710071, China

    Shuai Zheng, Jian Chen & Yonghong Kuo

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  1. Shuai Zheng
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  2. Jian Chen
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  3. Yonghong Kuo
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Correspondence to Jian Chen.

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Zheng, S., Chen, J. & Kuo, Y. A multi-level residual reconstruction based image compressed sensing recovery scheme. Multimed Tools Appl 78, 25101–25119 (2019). https://doi.org/10.1007/s11042-019-07746-3

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  • DOI: https://doi.org/10.1007/s11042-019-07746-3

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