Image structure-based particle swarm optimization non-convex compressed sensing image reconstruction method

Abstract

The present invention discloses an image structure-based particle swarm optimization non-convex compressed sensing reconstruction method. The method comprises: 1. according to an observation vector, distinguishing an image block structure, and marking an image block as smooth, unidirectional and multi-directional; 3. clustering observation vectors corresponding to different types of image blocks, and constructing a corresponding over-complete redundant dictionary for each type of the image blocks; 4. constructing an initial population for each type of the image blocks; 5. for each type of the smooth image blocks, using a particle swarm algorithm based on a grouping initialization policy to search an atomic combination with an optimal scale; 6. for each type of the unidirectional and multi-directional image blocks, using a particle swarm algorithm based on a cross and an atomic direction constraint to search an atomic combination with an optimal direction and scale; and 7. calculating an estimated value of all image blocks, and sequentially splicing into an entire image to output. According to the method of the present invention, reconstruction time is short, and the reconstructed image has good visual effects, a high peak signal to noise ratio, and high structural similarity. The method can be used for non-convex compressed sensing reconstruction of an image signal.

Description

Technical field [0001] The present invention belongs to the technical field of image processing, and further relates to a compressed sensing reconstruction method, which can be used to obtain high-quality clear images from compressed observation of images. Background technique [0002] The emergence of compressed sensing CS theory broke the traditional Nyquist sampling theorem. The CS theory pointed out that the signal can be sampled at low speed and a small amount of sampling, and can be accurately reconstructed. The sampling rate is no longer determined by the bandwidth, but by the information in the signal. Structure and content. The research of compressed sensing mainly includes three aspects: compressed observation, sparse representation and reconstruction method. Among them, the research of reconstruction method to accurately reconstruct the original signal from the compressed observation of the signal is the core problem in compressed sensing. [0003] The original problem ...

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Problems solved by technology

[0004] However, the above two methods both have the disadvantage of slow reconstruction speed. Both methods use genetic algorithm and clone selection algorithm to optimize in two stages. The two methods distinguish smooth and non-smooth structures of image blocks, and the first The two methods do not use the prior information of the image structure to optimize the initialization population when the population is initialized, and the speed is slow, which is not conducive to real-time applications.

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