Statistical compressed sensing image reconstruction method based on layered Gauss mixing model

Abstract

The invention discloses a statistical compressed sensing image reconstruction method based on a layered Gauss mixing model, specifically relates to a statistical model learning and signal reconstruction algorithm based on image block data, and mainly solves the problem of an existing statistical compressed sensing image reconstruction method that a single Gauss prior model fails to accurately depict non-Gauss statistical property of sub image blocks and the quality of a reconstructed image is not high. According to the invention, on a first layer of the model, a global Gauss mixing model is learned, hard clustering is carried out on the sub image blocks, and the similar structures of the image blocks are fully utilized globally; and on a second layer of the model, a local Gauss mixing model of the sub image blocks are learned, and the differences among the sub image blocks are distinguished and models locally. Compared with an existing conventional compressed sensing and statistical compressed sensing reconstruction technology, higher reconstruction precision is obtained by less measurement, and the statistical compressed sensing image reconstruction method is suitable for the reconstruction of a natural image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image statistical compression sensing reconstruction method, which can be used to reconstruct natural images. Background technique [0002] Compressed Sensing (CS) is a new signal sampling theory formally proposed by Candès and Donoho et al. in 2006, such as: DonohoDL.Compressedsensing.IEEETransactionsonInformationTheory, 2006,52(4):1289-1306; CandèsE. : Universalencoding strategies? IEEE Transactions on Information Theory, 2006, 52(12): 5406-525. Different from the traditional sampling theory, CS synchronizes the sampling and compression process, directly perceives the signal in a compressed form, and the number of measurements obtained is much lower than the dimensionality of the sensed signal. In CS theory, under the assumption of a sparse signal model, an exact or approximate reconstruction of a signal can be obtained from a small number of measuremen...

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Problems solved by technology

The advantage of this method is: a Gaussian mixture model that describes the statistical properties of the sub-image blocks is formed globally, and the block sparse representation of the sub-image blocks is provided; the disadvantage is that a single Gaussian model is actually used to describe the sub-image blocks locally, but naturally Images usually exhibit significant non-Gaussian properties, and a single Gaussian model cannot flexibly and accurately represent different features existing in sub-image blocks, such as edges, textures, etc., resulting in low quality reconstructed images

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