Image transmission method and system based on compressed sensing

Abstract

The invention discloses an image transmission method based on compressed sensing, and the method is used by a transmitting end for transmitting an image to a receiving end. The method comprises the steps that S1, the transmitting end carries out the preprocessing of an original image, and generates a one-dimensional signal of the original image; S2, the transmitting end judges whether the original image is a sparse image or not according to the one-dimensional signal; S3, the transmitting end extracts the main feature information of the one-dimensional signal when the transmitting end judges that the original image is the sparse image; S4, the transmitting end codes the main feature information, generates first information, and transmits the first information to the receiving end; S5, the receiving end receives and analyzes the first information, and obtains the main feature information; S6, the receiving end carries out the reconstruction of the main feature information, and obtains the original image through restoration. The system enables a high-dimensional image signal to be converted into low-dimensional image signals through a conversion matrix, and finally carries out the high-probability reconstruction of a few of low-dimensional image signals through an optimization method to obtain the original signal.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an image transmission method and system based on compressed sensing. Background technique [0002] With the development of the digital information age, image compression and reconstruction technology has attracted more and more attention, especially video image transmission processing for video compression coding, source coding and 3D video reconstruction has become a research hotspot in this field. In recent years, video image sparse coding and reconstruction decoding technology based on compressed sensing has become a new direction for the development of image processing technology. Compressed sensing was proposed by Candes and Donoho. Its core idea is to compress the signal while sampling it, so as to overcome the limitation of Nyquist sampling requirements in traditional signal processing. But it is different from the traditional sampling theorem. [0003] First, the traditi...

AI Technical Summary

Problems solved by technology

[0004] Most of the existing image compression and reconstruction technologies adopt Shannon’s adoption theorem, which requires that the sampling frequency of the image signal must be more than twice the highest signal frequency to reconstruct the original image signal without distortion, and a large number of adoption points are required to restore the original signal. As a result, the number of samples increases and the transmission efficiency is low

If the original image has a large amount of information, more samples need to be collected, and most of the transformation matrix base coefficients are discarded, resulting in a waste of data and hardware resources

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