CROSS-REFERENCE TO RELATED APPLICATIONS

This is the U.S. National Stage of International Patent Application No. PCT/CN2018/073172 filed Jan. 18, 2018, which was published in Chinese under PCT Article 21(2), and which in turn claims the benefit of Chinese Patent Application No. 201710402262.4 filed on Jun. 1, 2017.

TECHNICAL FIELD

The present invention relates to the technical field of image imaging and target recognition, and in particular to a compressed sensing based object imaging system and an imaging method therefor.

BACKGROUND ART

In recent years, target recognition, which well integrates technologies in subjects such as pattern recognition, computer vision, and artificial intelligence, has become a very hot topic in the field of visual researches and has a broad application prospect. Since the target recognition method was proposed, many experts and researchers have carried out researches on related work. However, most target recognition methods involve corresponding processing of an image by using a recognition technology after the image is obtained, which causes a poor real-time performance of the system, and thus an accompanying problem of a large volume of image data which also becomes a bottleneck restricting the development of the target recognition methods.

With the rapid development of imaging and information technologies, people also have an increasing demand for electronic products and digital information in daily life. However, limited by the speed of information transmission and the speed of information processing, it is not feasible to increase the sampling rate of information at all costs in order to obtain high-quality data. Under the premise of ensuring that the quality provided by the information is basically unchanged, it is imperative to obtain information of similar quality by using a small amount of sampling.

In the newly emerging theory of compressed sensing, a non-adaptive linear projection value of a signal is acquired at a rate much lower than the Nyquist sampling rate, and then the original signal is accurately reconstructed by solving an optimization problem, which can greatly reduce the volume of data acquired by the system. A series of compressed sensing based target recognition solutions have been proposed gradually. However, although these solutions reduce the volume of data acquired by the system, cumbersome image information still exists during further image recognition and processing in a later stage, which is not conducive to image recognition. In addition, most of these solutions are implemented by way of digital signals in the electrical field, without making full use of the advantages of concurrent processing.

SUMMARY OF THE INVENTION

The main object of the present invention is to overcome the disadvantages and shortcomings in the prior art, and to provide a compressed sensing based object imaging system, which can simultaneously implement image compression, image sampling, and image recognition in a pure optical domain, thereby greatly reducing the volume of data recorded in image recognition and image matching, solving a problem of storage and transmission of massive data, improving the real-time performance of the system, and providing a possibility of concurrent processing by machine vision and artificial intelligence.

Another object of the present invention is to provide an imaging method based on the system above.

The objects of the present invention are implemented by using the following technical solution:

a compressed sensing based object imaging system, comprising a light source generation unit, a filter unit, an image generation unit, an image acquisition unit, and an image reconstruction unit that are sequentially connected,

wherein the light source generation unit generates experimental laser light;

the filter unit controls the attenuation of the light intensity of the laser light, filters off high frequency scattered light, and forms a zero-order diffraction spot, and then controls the diffraction spot to form parallel light;

the image generation unit generates an experimental image in which an object image and a specific measurement matrix are superimposed;

the image acquisition unit performs compressed sampling on the experimental image; and

the image reconstruction unit reconstructs sampling data to restore the object image.

Preferably, the light source generation unit comprises a laser and a mirror that can reflect laser light to change a propagation direction thereof.

Preferably, the filter unit comprises a circular tunable attenuator, a pinhole filter, and a Fourier lens that are sequentially arranged in parallel with an optical path.

Preferably, the image generation unit comprises a first polarizing film, a spatial light modulator loaded with the specific measurement matrix, and a second polarizing film that are sequentially arranged in parallel with an optical path.

Preferably, the image acquisition unit comprises a convergent lens and a single photon detector that are sequentially arranged in parallel with an optical path.

Preferably, the image reconstruction unit comprises a computer.

An imaging method based on the system above comprises the following steps:

step S1: emitting experimental laser light by using a laser light source;

step S2: performing attenuation processing on the experimental laser light, filtering off high frequency scattered light to obtain a zero-order diffraction spot, and adjusting the diffraction spot to be parallel light;

step S3: illuminating an object image with the parallel light, and generating, through a spatial light modulator loaded with a specific measurement matrix, an experimental image in which the object image and the specific measurement matrix are superimposed; and step S4: performing compressed sampling on the experimental image, such that an image reconstruction unit reconstructs the object image according to sampling data.

Preferably, a calculation method for the specific measurement matrix in step S3 is: establishing a sample library comprising the specific target object image, and training sample images by using a principal component analysis method to obtain the specific measurement matrix.

Specifically, according to the principal component analysis method, provided that there is a set of N image training samples X each having a size of p×q pixels, each sample has a vector Xithat is formed by a pixel grayscale value thereof, and the set of training samples constituted by vectors is X={X1, X2, . . . , Xi, . . . , XN}, the mean of the set of training samples is first calculated:

X_=1N⁢∑i=1N⁢⁢Xi,

data is further centralized:
{circumflex over (X)}i=Xi−X,

a covariance matrix is calculated for the centralized data:

R=1N⁢∑i=1N⁢⁢(Xi-X_)⁢(Xi-X_)T

eigenvalues λiof the covariance matrix R are arranged in a descending order, and eigenvectors uicorresponding to the first m eigenvalues are formed into a principal component matrix U,
U=[u1, u2, . . . , um]

wherein the m components extract features that represent main information of the images; and

the principal component matrix is subjected to rank inversion to form the specific measurement matrix, and according to the principle of compressed sensing, the specific measurement matrix after training with the sample library is expressed as:
Φ=UT=[ϕ1, ϕ2, . . . , ϕm]T(ϕi=ui).

Preferably, acquiring experimental measurement data in step S4 comprises: acquiring an optical signal at a single point and converting the optical signal into an electrical signal, with an output voltage value being expressed as:
yn=φnx,

where x∈Rp×q, x representing the object image, φn∈Rp×q, n∈{1, 2, . . . , m}, φnrepresenting an nthdimension of the specific measurement matrix, which implements an nthmeasurement of the object image x; and

repeating this process m times, such that a measured value Y can be obtained:
Y=[y1, y2, . . . , ym]T=[φ1, φ2. . . φm]Tx=Φx,

where Φ∈Rm×(p×q)is the specific measurement matrix, and Y∈Rm×1is the measured value.

Preferably, in step S4, the image reconstruction unit comprises a data screening processing module and an image reconstruction processing module, the data screening processing module is used to perform time domain screening and size screening on data acquired by an image acquisition unit, so as to obtain accurate experimental data; and the image reconstruction processing module reconstructs the object image by using a minimum total variation optimization algorithm.

Compared with the prior art, the present invention has the advantages and beneficial effects as follows.

In the present invention, the imaging of a specific target object is completed in a complex scene by training a specific measurement matrix comprising specific object information by using a machine learning method (i.e., a principal component method) and in connection with a compressed sensing technology. Compared with the prior art, the present invention simultaneously implements image compression, image sampling, and image recognition in an all-optical environment by combining the emerging compressed sensing theory and pattern recognition technologies, that is, implementing target recognition at the time of imaging. This technology greatly reduces the volume of data for storage and transmission, greatly improves the efficiency of target recognition, and provides a possibility of concurrent processing by machine vision and artificial intelligence.

