An Optimization Method of Projection Mode in Image Reconstruction Based on Compressed Sensing

Abstract

The invention provides a method for optimizing projection modes in image reconstruction based on compression sensing. In the process of reconstructing compressed sensing images, the traditional projection mode usually projects line signals of the image by lines, which resulting in that the row information of the image is wasted, and the sparseness of the image cannot be fully utilized; or, the whole image is pulled into one line signal to be projected, but the projection efficiency is decreased, the complexity in calculation is increased, and the image reconstruction time is prolonged. The method has the advantages that the projection mode is optimized on the basis of traditional single-side line projection, a row and line double-side projection mode with super-low sampling rate is proposed; the sparseness of the global image is fully utilized, the projection efficiency is improved, and the reconstruction accuracy and quality of the image at low sampling rate are improved; especially,under the condition of super-low sampling rate of 1 / 16, compared with the single-side projection mode, the PSNR (peak signal-to-noise ratio) of the reconstructed image of the double-side projection mode is increased by about 4.5dB, and the SSIM (structure similarity) is increased by about 0.1.

Description

technical field [0001] The invention relates to an optimization method of projection mode based on compressed sensing in image reconstruction, which is characterized in that the original signal data of the signal is recovered and reconstructed at a lower sampling rate to obtain higher-precision original signal data, which is applied to signal compression and restoration, Image processing and computer vision, etc., belong to the field of signal compression transmission and restoration and reconstruction in signal and information processing. Background technique [0002] The core of compressive sensing is the linear measurement process. Let x(n) be the original signal, the length is N, and y(m) is obtained by multiplying the measurement matrix Φ by the left, and the length is M (M<N). If x(n) is not a sparse signal, an orthogonal sparse transformation will be performed to obtain s(k), which is denoted as x=Ψs, and the measurement process is rewritten as y=Θs, where Θ=ΦΨ(M×N...

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Problems solved by technology

[0006] but minimum l 0 The norm problem is an NP-hard problem that requires exhaustive enumeration of all non-zero values ​​in x permutations are possible, so it is impossible to solve

[0010] The RIP condition is a sufficient condition to ensure that the signal can be reconstructed. However, it is a very complicated problem to verify whether the sensing matrix satisfies this condition. Therefore, there is a need for a simple and easy-to-implement alternative to the RIP condition.

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