Image structure model-based compressed sensing image reconstruction method

Abstract

The invention discloses an image structure model-based compressed sensing image reconstruction method, which mainly solves the problems that image structure information is not considered and blind iteration is carried out in the conventional method. The method comprises the following steps of: inputting an image A, and performing Fourier transform on the image A to obtain a Fourier coefficient matrix X1 of the input image A; sampling the Fourier coefficient matrix X1 according to a density variable sampling model for fully sampling Fourier coefficients at low frequency to obtain an observation vector f; performing inverse Fourier transform on the observation vector f to obtain a transformed image X2; performing edge detection on the transformed image X2 to obtain an edge detection image X3; performing Wavelet transform and Curvelet transform on the edge detection image X3, finding an edge position and positions of large coefficients, and finding corresponding coefficients in the transformed image X2 according to the obtained positions; and performing Wavelet-curvelet frame-based Split Bregman reconstruction algorithm to iterate for 20 times and finally obtaining the required reconstructed image. The method has the advantages of higher accuracy, better effect and shorter time for image reconstruction.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to an image reconstruction method, which can be used for image processing and compression. Background technique [0002] In the past ten years, with the improvement of imaging technology and the improvement of image resolution, the amount of image data has also been expanding, which has brought great pressure to image transmission. Therefore, it is very important to propose an effective image reconstruction technology; and the recently proposed compressed sensing (Compressed sensing) theory has found a new image reconstruction technology for compressible images, which solves the problem of image distortion well. Transmission problem. [0003] Compressed Sensing (CS), also known as Compressed Sampling or Sparse Sampling, is a technology that uses prior knowledge of "data is sparse or compressible" for signal acquisition and reconstruction. It was developed by American schol...

AI Technical Summary

Problems solved by technology

[0006] (1) Since the inherent structural information of the image itself is not fully considered, the details are blindly learned and iterated, which increases the time complexity of reconstruction;

[0007] (2) Since the detailed information of the image is not fully considered in the sampling model, the reconstruction accuracy and effect of the image are affected

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