High-quality image restoration from partial mixed adaptive-random measurements
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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.
KeywordsData acquisition Mixed adaptive-random sampling Total variation Compressive sensing
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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