Non-uniform Compressive Sensing Imaging Based on Image Saliency

LI Hongliang; DAI Feng; ZHAO Qiang; MA Yike; CAO Juan; ZHANG Yongdong

doi:10.23919/cje.2019.00.028

Volume 32 Issue 1

Jan. 2023

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Article Navigation > Chinese Journal of Electronics > 2023 > 32(1): 159-165

LI Hongliang, DAI Feng, ZHAO Qiang, et al., “Non-uniform Compressive Sensing Imaging Based on Image Saliency,” Chinese Journal of Electronics, vol. 32, no. 1, pp. 159-165, 2023, doi: 10.23919/cje.2019.00.028

Citation:

LI Hongliang, DAI Feng, ZHAO Qiang, et al., “Non-uniform Compressive Sensing Imaging Based on Image Saliency,” Chinese Journal of Electronics, vol. 32, no. 1, pp. 159-165, 2023, doi: 10.23919/cje.2019.00.028

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Non-uniform Compressive Sensing Imaging Based on Image Saliency

doi: 10.23919/cje.2019.00.028

1.
University of Chinese Academy of Sciences, Beijing 100049, China
2.
Key Lab of Intelligent Information Processing of Chinese Academy of Sciences, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
3.
University of Science and Technology of China, Hefei 230027, China

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Author Bio:
Hongliang LI reveived the B.S. degree in computer science and technology from the Xidian University, China, in 2011, and the M.S. degree in the Institute of Computing Technology, Chinese Academy of Sciences (CAS), in 2014. He is currently pursuing the Ph.D. degree with the Institute of Computing Technology, CAS. His current research interests include the compressive sensing, computational imaging, machine learning, and neural network. (Email: lihongliang@ict.ac.cn)

Feng DAI (corresponding author) received the Ph.D. degree in the Institute of Computing Technology, CAS, Beijing, China, in 2008. He is currently working as an Associate Professor in the Multimedia Computing Group, Advanced Research Lab, Institute of Computing Technology, CAS. His research interests include image/video processing, computational imaging, and computer vision. (Email: fdai@ict.ac.cn)
Received Date: 2019-01-18
Accepted Date: 2019-05-24

Available Online: 2022-06-09

Publish Date: 2023-01-05

Abstract

Abstract

For more effective image sampling, compressive sensing (CS) imaging methods based on image saliency have been proposed in recent years. Those methods assign higher measurement rates to salient regions, but lower measurement rate to non-salient regions to improve the performance of CS imaging. However, those methods are block-based, which are difficult to apply to actual CS sampling, as each photodiode should strictly correspond to a block of the scene. In our work, we propose a non-uniform CS imaging method based on image saliency, which assigns higher measurement density to salient regions and lower density to non-salient regions, where measurement density is the number of pixels measured in a unit size. As the dimension of the signal is reduced, the quality of reconstructed image will be improved theoretically, which is confirmed by our experiments. Since the scene is sampled as a whole, our method can be easily applied to actual CS sampling. To verify the feasibility of our approach, we design and implement a hardware sampling system, which can apply our non-uniform sampling method to obtain measurements and reconstruct the images. To our best knowledge, this is the first CS hardware sampling system based on image saliency.
- Compressive sensing,
- Non-uniform,
- Measurement density,
- Image saliency

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References(25)

References

[1]	E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Transactions on Information Theory, vol.52, no.2, pp.489–509, 2006. doi: 10.1109/TIT.2005.862083
[2]	E. J. Candès and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Processing Magazine, vol.25, no.2, pp.21–30, 2008. doi: 10.1109/MSP.2007.914731
[3]	A. K. Moorthy and A. C. Bovik, “Visual importance pooling for image quality assessment,” IEEE J. Sel. Top. Sign. Proces., vol.3, no.2, pp.193–201, 2009. doi: 10.1109/JSTSP.2009.2015374
[4]	Y. Yu, B. Wang, and L. Zhang, “Saliency-based compressive sampling for image signals,” IEEE Signal Processing Letters, vol.17, no.11, pp.973–976, 2010. doi: 10.1109/LSP.2010.2080673
[5]	X. Zhang, J. Chen, H. Meng, and X. Tian, “Selfadaptive structured image sensing,” Optical Engineering, vol.51, no.12, article no.127001, 2012. doi: 10.1117/1.OE.51.12.127001
[6]	R. Li, X. Duan, X. Guo, W. He, and Y. Lv, “Adaptive compressive sensing of images using spatial entropy,” Computational Intelligence and Neuroscience, vol.2017, pp.1–9, 2017.
[7]	B. Zhang, Y. Liu, X. Jing, et al., “Interweaving permutation meets block compressed sensing,” Chinese Journal of Electronics, vol.27, no.5, pp.1056–1062, 2018. doi: 10.1049/cje.2017.04.001
[8]	Z. Li, J. Xie, G. Zhu, et al., “Block-based projection matrix design for compressed sensing,” Chinese Journal of Electronics, vol.25, no.3, pp.551–555, 2016. doi: 10.1049/cje.2016.05.022
[9]	M. A. Figueiredo, R. D. Nowak, and S. J. Wright, “Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems,” IEEE Journal of Selected Topics in Signal Processing, vol.1, no.4, pp.586–597, 2007. doi: 10.1109/JSTSP.2007.910281
[10]	I. Daubechies, M. Defrise, and C. De Mol, “An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,” Communications on Pure and Applied Mathematics, vol.57, no.11, pp.1413–1457, 2004. doi: 10.1002/cpa.20042
[11]	L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D: Nonlinear Phenomena, vol.60, no.1-4, pp.259–268, 1992. doi: 10.1016/0167-2789(92)90242-F
[12]	L. Jia, “Joint bayesian and greedy recovery for compressive sensing,” Chinese Journal of Electronics, vol.29, no.5, pp.945–951, 2020. doi: 10.1049/cje.2020.08.010
[13]	N. He, R. Wang, J. Lyu, and J. Xue, “Low-rank combined adaptive sparsifying transform for blind compressed sensing image recovery,” Chinese Journal of Electronics, vol.29, no.4, pp.678–685, 2020. doi: 10.1049/cje.2020.05.014
[14]	C. Li, W. Yin, H. Jiang, and Y. Zhang, “An efficient augmented lagrangian method with applications to total variation minimization,” Computational Optimization and Applications, vol.56, no.3, pp.507–530, 2013. doi: 10.1007/s10589-013-9576-1
[15]	C. A. Metzler, A. Maleki, and R. G. Baraniuk, “From denoising to compressed sensing,” IEEE Transactions on Information Theory, vol.62, no.9, pp.5117–5144, 2016. doi: 10.1109/TIT.2016.2556683
[16]	C. Metzler, A. Mousavi, and R. Baraniuk, “Learned D-AMP: Principled neural network based compressive image recovery,” in Proc. of the 31st Int. Conf. on Neural Information Processing Systems, Long Beach, CA, USA, pp.1770–1781, 2017.
[17]	M. F. Duarte, M. A. Davenport, D. Takhar, et al., “Single-pixel imaging via compressive sampling,” IEEE Signal Processing Magazine, vol.25, no.2, pp.83–91, 2008. doi: 10.1109/MSP.2007.914730
[18]	G. Huang, H. Jiang, K. Matthews, et al., “Lensless imaging by compressive sensing,” in Proceedings of 2013 20th IEEE International Conference on Image Processing (ICIP), Melbourne, VIC, Australia, pp.2101–2105, 2013.
[19]	Z. Zhang, X. Ma, and J. Zhong, “Single-pixel imaging by means of fourier spectrum acquisition,” Nature Communications, vol.6, no.1, article no.6225, 2015. doi: 10.1038/ncomms7225
[20]	C. Yang, L. Zhang, H. Lu, et al., “Saliency detection via graph-based manifold ranking,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, pp.3166–3173, 2013.
[21]	D. Gao, S. Han, and N. Vasconcelos, “Discriminant saliency, the detection of suspicious coincidences. and applications to visual recognition,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.31, no.6, pp.989–1005, 2009. doi: 10.1109/TPAMI.2009.27
[22]	L. Galteri, L. Seidenari, M. Bertini, et al., “Deep generative adversarial compression artifact removal,” in Proceedings of 2017 IEEE International Conference on Computer Vision (ICCV),Venice, Italy, pp.4836–4845, 2017.
[23]	K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp.770–778, 2016.
[24]	O. Russakovsky, J. Deng, H. Su, et al., “Imagenet large scale visual recognition challenge,” International Journal of Computer Vision, vol.115, no.3, pp.211–252, 2015. doi: 10.1007/s11263-015-0816-y
[25]	J. Shi, Q. Yan, L. Xu, et al., “Hierarchical image saliency detection on extended CSSD,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.38, no.4, pp.717–729, 2016. doi: 10.1109/TPAMI.2015.2465960