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
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
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
Baraniuk RG (2007) Compressive sensing. IEEE Signal Process Mag 24(4):118–121
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
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
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
Candes EJ (2006) Compressive sampling. Marta Sanz Solé 25(2):1433–1452
Candes EJ, Wakin MB, Boyd SP (2008) Enhancing sparsity by reweighted L1 minimization. Fourier Anal Appl 14(5):877–905
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
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
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
Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306
Gan L (2007) Block compressed sensing of natural images. In Proceedings of the International Conference on Digital Signal Processing, Cardiff, UK, pp 403–406
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
He L, Carin L (2009) Exploiting structure in wavelet-based Bayesian compressive sensing. IEEE Trans Signal Process 57(9):3488–3497
He L, Chen H, Carin L (2010) Tree-structured compressive sensing with variational Bayesian analysis. IEEE Signal Process Lett 17(3):233–236
Hosseini MS, Plataniotis KN (2014) High-accuracy total variation with application to compressed video sensing. IEEE Trans Image Process 23(9):3869–3884
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
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
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
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
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
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
Rudin L, Osher S, Fatemi E (1992) Nonlinear total variation based noise removal algorithms. Physica D 60(1):259–268
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
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
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
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
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
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
Wang Y, Bai H, Zhao L et al (2018) Cascaded reconstruction network for compressive image sensing. EURASIP J Image Video Process 2018(1):77
**ao 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
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
Yao H, Dai F, Zhang D et al (2017) DR2-net: deep residual reconstruction network for image compressive sensing. ar**v preprint. ar**v: 1702.05743
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
Zhang J, Liu S, **ong 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, Bei**g, China, pp 2836–2839
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
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
Zhang J, Zhao D, Gao W (2014) Group-based sparse representation for image restoration. IEEE Trans Image Process 23(8):3336–3351
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
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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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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