A Compressed Sensing Imaging and Image Reconstruction Method Based on Observation Matrix

Abstract

The present invention provides an observation matrix based compressed sensing imaging and image reconstruction method. The method comprises: (1) dividing a to-be-sensed image into a salient region and a non-salient region; (2) setting a pore size of the observation matrix, wherein in the observation matrix, a pixel size corresponding to the salient region is smaller than a pixel size of remaining regions; (3) generating a matrix that meets a restricted isometry property, and sequentially filling a pixel value of each pixel of the generated matrix into the observation matrix set in step (2), so as to obtain an observation matrix with a variable pore size; and (4) obtaining and recording an observation value of the to-be-sensed image based on the observation matrix with the variable pore size. According to method provided by the present invention, a data amount of compressed sensing imaging is greatly reduced under the condition that image quality is ensured; and a sampling frequency is significantly reduced under the condition that image quality is ensured, and sampling time is reduced. in addition, the method can be directly applied to a camera imaging system, so that a data collection amount can be directly reduced in an image collection stage.

Description

technical field [0001] The invention relates to the field of computer image acquisition and processing, in particular, the invention relates to a compression sensing imaging and image reconstruction method based on an observation matrix. Background technique [0002] According to the classic Nyquist sampling law, the sampling frequency of the signal needs to be at least twice the signal bandwidth in order to restore the original signal from the digital signal without distortion. In recent years, people have used the method of compressed sensing to greatly compress the sparse signal, breaking through the Nyquist sampling law on the sparse signal, which greatly reduces the cost of image acquisition equipment, reduces the storage space of data, and can use a small amount of High-quality images can be recovered from the sampling data. [0003] The following is a brief introduction to compressive sensing technology. In the real world, people often need to observe the signal, su...

AI Technical Summary

Problems solved by technology

Even if the existing compressed sensing imaging scheme is adopted, it is still difficult to meet the increasing demand for information storage space, and the manufacturing of imaging equipment is becoming more and more difficult.

Images