Efficient reconstruction method in compression perceptual system

Abstract

The invention discloses an efficient reconstruction method in a compression perceptual system, relates to a data processing method which is used for solving the problem that the precision and the speed cannot be improved simultaneously in the existing reconstruction method and comprises the following steps: firstly arranging a measurement value Y0 into a form for easily realizing a reconstruction algorithm, if one-dimensional reconstruction is required, ensuring that the measurement value is not arranged, and if two-dimensional reconstruction is required, vectorizing the measurement value to obtain Y; then enabling the k to be equal to 1, uk to be equal to 0 and vk to be equal to 0, thus obtaining expressions as shown in the specification; performing non-contribution iteration in an iterative step to compute the times s of the non-contribution iteration; if the change times s of the vk just enable the uk+1 to change, containing the following iterative formulas as shown in the specification in the iterative steps to judge an expression as shown in the specification, then judging whether the judgment is met so as to determine whether to converge the iteration, iterating until the iteration is converged; and finally, if an one-dimensional signal is required to reconstruct, directly using signal sparseness to represent a reconstruction original signal, and if a two-dimensional signal is required to reconstruct, inversely vectorizing a sparseness coefficient u, and using the sparseness of the image to represent the reconstruction original image. The efficient reconstruction method can be applied to the reconstruction of the one-dimensional or two-dimensional signal in the compression perceptual system.

Description

technical field [0001] The invention relates to a data processing method, in particular to a signal reconstruction method in a compressed sensing system. Background technique [0002] In recent years, a new theory - compressive sensing (compressive sensing) theory has emerged internationally. The theory points out that as long as the signal is compressible or the signal is sparse in a certain transformation domain, then the original signal can be measured with a measurement matrix unrelated to the sparse matrix, and the high-dimensional image can be projected to a low-dimensional space, the original signal can then be completely reconstructed from this small number of projected values ​​by solving an optimization problem. Compressed sensing does not need to go through the process of sampling and then compressing, which largely overcomes the shortcomings of traditional signal acquisition and processing. [0003] The compressive sensing measurement and reconstruction process...

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Problems solved by technology

[0023] In order to solve the problem that the accuracy and speed cannot be improved at the same time in the existing reconstruction method, the present invention proposes an efficient reconstruction method in the compressed sensing system

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