Self-adaptive regularized smoothed l<0> norm method

Abstract

The invention discloses a self-adaptive regularized smoothed l<0> norm method. A regularized SL0 algorithm is improved; in a steepest ascent method in an inner loop, a signal residual item estimated value iterative for the first time and a sparse signal estimated deviation value before and after the iteration are used as the selection basis of current regularization parameters; therefore, the signal sparse degree and the weight value of an error tolerance item in an outer loop every time can be adjusted self-adaptively; the balance of the two is kept in an optimization process; therefore, the reconstruction error of sparse signals can be effectively reduced; the anti-noise interference capability of the algorithm is improved; large-scale matrix inversion operation projected in operation of a feasible solution set in an iterative process can be avoided by introducing a SVD method; and the reconstruction speed to the sparse signals in the method disclosed by the invention is effectively increased.

Description

technical field [0001] The present invention relates to an adaptive regularization smoothing l 0 A norm method belongs to the technical field of compressed sensing restoration. Background technique [0002] As a new technology in the field of signal processing, compressed sensing has been widely used in the fields of biomedicine, image processing, wireless communication and radar signal processing. Compressive sensing theory solves l 0 The norm minimization problem can reconstruct sparse signals with high probability from a small number of non-adaptive projected measurements. However, l 0 The norm minimization problem is an NP-hard problem, which needs to be solved by combinatorial search, but it is difficult to solve the problem when the signal dimension is large. smooth l 0 Norm (Smoothed l 0 Norm, SL0) algorithm is to use a series of Gaussian functions to approximate l 0 norm, so that l 0 The NP-hard problem of norm minimization is transformed into an easy-to-solv...

AI Technical Summary

Problems solved by technology

In this algorithm, the regularization parameter is a fixed value in all iterative operations. However, in the actual iterative operation, the value of the error tolerance term will change greatly. Therefore, in different iterative operations, the fixed regularization parameter cannot Effectively maintain the balance between signal sparsity and error tolerance, resulting in weak anti-noise ability and low robustness of the algorithm

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