Compressed sensing image reconstruction method combining bilateral total variation and non-local low-rank regularization

Abstract

A compressed sensing image reconstruction method combining bilateral total variation and non-local low-rank regularization comprises the following steps: 1, inputting an original test image x into a computer, wherein all the test images are gray level images with 256 * 256 pixels; 2, setting a sampling rate, arranging the test images in a one-dimensional vector form, generating a sampling matrix, and generating CS measurement by randomly sampling Fourier transform coefficients of the input images to obtain a measurement value; 3, inputting the obtained measurement value and the sampling matrix into a reconstruction model of a proposed joint algorithm as input, and iteratively solving a recovery image with 256 * 256 pixels by using an alternating direction multiplier method; and 4, selecting a CS reconstruction algorithm to perform subjective and objective comparison of the recovered image and the test image, and objectively evaluating algorithm reconstruction performance and selecting a peak signal-to-noise ratio and structural similarity as evaluation indexes. According to the invention, a more accurate and high-quality image restoration result can be realized.

Description

technical field [0001] The invention belongs to the technical field of image reconstruction algorithms, and relates to a compressed sensing image reconstruction method combining bilateral full variation and non-local low-rank regularization. Background technique [0002] In recent years, the theory of Compressed Sensing (CS) has been proposed, which is a new signal sampling method that breaks through the frequency limit of Nyquist sampling theorem. Compressed sensing theory points out that if the signal is sparse or sparsely representable in a certain transform domain, it can be achieved by sampling and data compression simultaneously by using a small number of random measurements generated by random Gaussian matrices or partial Fourier matrices. Perfect reconstruction of sparse signals. The compressed sensing method has the advantages of low sampling rate and high acquisition efficiency, and has been widely used in various fields including 3D imaging, video acquisition, en...

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Problems solved by technology

[0006] In order to solve the problem of over-smoothing and information degradation in the edge of the restored image based on the non-local low-rank reconstruction algorithm, the present invention provides an improved method, that is, adding the bilateral full variation as the global information prior, and obtaining a A new compressive sensing image reconstruction method that combines bilateral full variation and non-local low-rank regularization can achieve more accurate and high-quality image restoration results

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