Compressed sensing image reconstructing method based on prior model and 10 norms

Abstract

The invention discloses a compressed sensing image reconstructing method based on a prior model and 10 norms, mainly used for solving the defects of poor visual effect and long operation time existing in image reconstruction in the prior art. In the technical scheme of the invention, a compressed sensing image reconstruction frame with 10 norms is optimized by utilizing a prior model; and the positioning of sparsity coefficient and solution of the sparsity coefficient value are achieved through two effective steps: step 1, establishing the prior model, and carrying out low frequency coefficient inverse wavelet transform so as to obtain an image with a fuzzy edge, determining the position of the edge by edge detection, and searching the position of wavelet high frequency subband sparsity coefficient through an immunization genetic algorithm by using the prior model of which the wavelet coefficient has inter-scale aggregation; and step 2, solving a corresponding high frequency subband by using an improved clone selective algorithm, and then carrying out the inverse wavelet transform so as to obtain a reconstructed image. Compared with the prior art, the method has the advantages of good visual effect and low calculation complexity, and can be used in the fields of image processing and computer visual.

Description

technical field [0001] The invention belongs to the technical field of image processing and relates to a compression sensing image reconstruction method, which can be used in practical engineering fields such as biological sensing, remote sensing image processing, wireless sensor network and image acquisition equipment development. Background technique [0002] With the advent of digitization and information age, people pay more and more attention to digital image processing technology, especially in the fields of SAR image processing, medical image processing and remote sensing image processing. In recent years, Donoho et al. proposed a novel theory - compressive sensing theory CS. In this theory, the sampling speed is no longer determined by the bandwidth of the signal, but by the structure and content in the signal. Based on the theory of compressed sensing, the signal can be sampled at low speed and then encoded, which greatly reduces the computational complexity. Once...

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Problems solved by technology

These two types of algorithms have their inherent shortcomings. The convex relaxation method uses a small number of observations to reconstruct the signal, and the quality of the reconstructed image is high, but it has a large amount of calculation and high time cost; the greedy pursuit algorithm and convex relaxation Compared with the traditional method, it overcomes the problem of long calculation time and is suitable for solving large-scale problems, but it requires a large number of observations, the quality of the reconstructed image is not high, and it imposes a limited isometric property on the compressed sensing framework. RIP constraints, in a sense, limit the scope of application of CS

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