Abstract
A novel framework to construct an efficient sensing (measurement) matrix, called mixed adaptive-random (MAR) matrix, is introduced for directly acquiring a compressed image representation. The mixed sampling (sensing) procedure hybridizes adaptive edge measurements extracted from a low-resolution image with uniform random measurements predefined for the high-resolution image to be recovered. The mixed sensing matrix seamlessly captures important information of an image, and meanwhile approximately satisfies the restricted isometry property. To recover the high-resolution image from MAR measurements, the total variation algorithm based on the compressive sensing theory is employed for solving the Lagrangian regularization problem. Both peak signal-to-noise ratio and structural similarity results demonstrate the MAR sensing framework shows much better recovery performance than the completely random sensing one. The work is particularly helpful for high-performance and lost-cost data acquisition.
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Acknowledgments
Dr. Sha acknowledges Ms. Qing Ye to motivate the compressive sensing work. We also would like to acknowledge the support of China NSF under Grant 61173081 and 61201122; and Guangdong Natural Science Foundation, China, under Grant S2011020001215.
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Yang, J., Sha, W.E.I., Chao, H. et al. High-quality image restoration from partial mixed adaptive-random measurements. Multimed Tools Appl 75, 6189–6205 (2016). https://doi.org/10.1007/s11042-015-2566-9
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DOI: https://doi.org/10.1007/s11042-015-2566-9