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.
Similar content being viewed by others
References
Afonso MV (2010) Fast image recovery using variable splitting and constrained optimization. IEEE Trans Image Process 19(9):2345–2356
Bioucasdias JM, Figueiredo MAT (2007) A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration. IEEE Trans Image Process 16(12):2992–3004
Blumensath T, Davies ME (2008) Iterative hard thresholding for compressed sensing. Applied & Computational Harmonic Analysis 27(3):265–274
Chang K, Liang Y, Chen C, Tang Z, Qin T (2017) Color image compressive sensing reconstruction by using inter-channel correlation. In: Visual Communications and Image Processing, pp 1–4
Cheng MM, Zhang GX, Mitra NJ, Huang X, Hu SM (2011) Global contrast based salient region detection. In: Computer Vision and Pattern Recognition (CVPR), pp 409–416
Liu YX (2010) Regularized adaptive matching pursuit algorithm for signal reconstruction based on compressive sensing. Journal of Electronics & Information Technology 32(11):2713–2717
Lu G (2007) Block compressed sensing of natural images. In: 2007 15th international conference on digital signal processing. In: pp 403–406
Lu H, Li B, Zhu J et al (2017) Wound intensity correction and segmentation with convolutional neural networks. Concurrency and Computation Practice & Experience 29(6):3927
Lu H, Li Y, Uemura T et al (2018) Low illumination underwater light field images reconstruction using deep convolutional neural networks. Futur Gener Comput Syst 82:142–148
Lu H, Li Y, Chen M et al (2018) Brain intelligence: go beyond artificial intelligence. Mobile Networks and Applications 23:368–375
Majumdar A, Ward RK (2010) Compressive color imaging with group-sparsity on analysis prior. In: IEEE International Conference on Image Processing (ICIP), pp 1337–1340
Majumdar A, Ward RK (2010) Compressed sensing of color images. Signal Process 90(12):3122–3127
Nagesh P, Li B (2009) Compressive imaging of color images. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp 1261–1264
Ran L, Zongliang G, Ziguan C, Minghu W, Xiuchang Z (2013) Distributed adaptive compressed video sensing using smoothed projected landweber reconstruction. China Communications 10(11):58–69
Scharfenberger C, Wong A, Clausi DA (2015) Structure-guided statistical textural distinctiveness for salient region detection in natural images. IEEE Trans Image Process 24(1):457–470
Varadarajan B, Khudanpur S, Tran TD (2010) Stepwise optimal subspace pursuit for improving sparse recovery. IEEE Signal Processing Letters 18(1):27–30
Vijayanagar KR, Liu Y, Kim J (2014) Adaptive measurement rate allocation for block-based compressed sensing of depth maps. In: 2014 IEEE International Conference on Image Processing (ICIP), pp 1307–1311
Wakin MB, Laska JN, Duarte MF, Baron D, Sarvotham S, Takhar D, Kelly KF, Baraniuk RG (2007) An Architecture for Compressive Imaging. In: IEEE International Conference on Image Processing (ICIP), pp 1273–1276
Xu C, Zheng-Guang X, Hong-Wei H, Xiao-Yan J (2015) An adaptive reconstruction algorithm for image block Compressed Sensing under low sampling rate. In: 2015 12th International Joint Conference on e-Business and Telecommunications (ICETE), pp 14–21
Acknowledgments
This work is supported by the NEPU Natural Science Foundation under Grant No. 2017PYZL-05, JYCX_CX06_2018 and JYCX_JG06_2018.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
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
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-018-7062-6