Compressive Image Sensing: Turbo Fast Recovery with Lower-FrequencyMeasurement Sampling
نویسندگان
چکیده
In order to get better reconstruction quality from compressive sensing of images, exploitation of the dependency or correlation patterns among the transform coefficients has been popularly employed. Nevertheless, both recovery quality and recovery speed are not compromised well. In this paper, we study a new image sensing technique, called turbo fast compression image sensing, with computational complexity O(m), where m denotes the length of a measurement vector y = φx that is sampled from the signal x of length n via the sampling matrix φ with dimensionality m×n. In order to leverage between reconstruction quality and recovery speed, a new and novel sampling matrix is designed. Our method has the following characteristics: (i) recovery speed is extremely fast due to a closed-form solution is derived; (ii) certain reconstruction accuracy is preserved because significant components of x can be reconstructed with higher priority via an elaborately designed φ. Our method is particularly different from those presented in the literature in that we focus on the design of a sampling matrix without relying on exploiting certain sparsity patterns. Simulations and comparisons with state-of-the-art CS methodologies are provided and demonstrate the feasibility of the proposed method in terms of reconstruction quality and computational complexity. keywords: Compressive sensing, Measurement, Reconstruction, Sampling, Sparsity
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تاریخ انتشار 2011