JP7608353B2 - Imaging device, vehicle lighting device, vehicle, and imaging method - Google Patents

Description

The present invention relates to an imaging device that utilizes ghost imaging.

For autonomous driving and automatic control of headlamp light distribution, an object identification system is used that senses the position and type of objects around the vehicle. The object identification system includes a sensor and a processing unit that analyzes the sensor output. The sensor is selected from cameras, LiDAR (Light Detection and Ranging, Laser Imaging Detection and Ranging), millimeter wave radar, ultrasonic sonar, etc., taking into consideration the application, required accuracy, and cost.

One type of imaging device (sensor) that is known is one that utilizes the principle of ghost imaging. Ghost imaging involves shining a reference light onto an object while randomly switching the intensity distribution (pattern) and measuring the optical detection intensity of the reflected light for each pattern. The optical detection intensity is the integral value of the energy or intensity over a certain plane, and is not an intensity distribution. Then, a correlation calculation is performed between the corresponding pattern and the optical detection intensity to reconstruct a restored image of the object.

Patent No. 6412673

As a result of studying imaging devices, the inventors have come to recognize the following problem. For correlation in ghost imaging, the correlation function of formula (1) is used. _Ir of the reference light is the r-th (r=1, 2..., M) intensity distribution, and _br is the value of the detected intensity obtained when irradiating the reference light having the r-th intensity distribution.

1 is a time chart showing sensing of one frame by an imaging device. As can be seen from equation (1), the correlation calculation requires an average value <b> of M detection intensities b ₁ to b _M obtained by irradiating the reference light M times.

Therefore, after M irradiations are completed, the average value <b> is calculated and the correlation calculation begins. Here, a photodetector is used to measure the light detection intensity. In addition to the reflected light from the object (i.e., the signal component), disturbance light (i.e., the noise component) is incident on the photodetector. If the intensity of the disturbance light is constant during the irradiation period T during which M irradiations are performed, the effect of the noise is canceled. However, if the intensity of the disturbance light fluctuates during the irradiation period T, the effect of the noise is not canceled, and the image quality of the reconstructed image is reduced. For example, when the imaging device is mounted on a moving object such as an automobile, this problem becomes more pronounced when the distance between the noise light source and the moving object changes.

The present invention has been made in light of this situation, and one exemplary objective of one aspect of the present invention is to provide an imaging device that has improved resistance to time-varying noise.

One aspect of the present invention relates to an imaging device. The imaging device includes an illumination device that sequentially switches and irradiates M (M≧2) reference light beams having a spatially random intensity distribution, a photodetector that measures reflected light from an object, and a processor that performs correlation calculations between the detection intensity based on the output of the photodetector and the intensity distribution of the reference light beams to reconstruct a restored image of the object. The processor divides the M reference light beams into a plurality of k (k≧2) units, performs correlation calculations for each divided unit to generate intermediate images, and synthesizes the k intermediate images obtained for the k units to generate a restored image.

Another aspect of the present invention is also an imaging device. This imaging device includes an illumination device that sequentially switches between M reference light beams having a spatially random intensity distribution and irradiates the object, a photodetector that measures reflected light from the object, and a processor that performs correlation calculations between the detection intensity based on the output of the photodetector and the intensity distribution of the reference light beam and reconstructs a restored image of the object. The processor performs correlation calculations to generate an intermediate image each time n (n≧2) reference light beams are irradiated, and synthesizes the latest k (k≧2) intermediate images to generate a restored image.

According to one aspect of the present invention, it is possible to increase the tolerance of an imaging device to time-varying noise.

4 is a time chart showing sensing of one frame by the imaging device. 1 is a diagram showing an imaging device according to a first embodiment; 4 is a time chart illustrating the operation of the imaging apparatus. FIG. 4A is a diagram showing the effect of noise on collective restoration, and FIG. 4B is a diagram showing the effect of noise on divided restoration. FIG. 5A shows a target image, and FIG. 5B shows images obtained by collective restoration and divided restoration when no noise is present. FIG. 13 is a diagram showing a restored image G(x, y) obtained by collective restoration and divided restoration when linear noise is present. 11A to 11C are diagrams illustrating an imaging method according to a second embodiment. FIG. 1 is a block diagram of an object identification system. FIG. 1 is a block diagram of a vehicle equipped with an object identification system. 1 is a block diagram showing a vehicle lamp equipped with an object detection system;

(Overview of the embodiment)
In this specification, "intensity distribution is random" does not mean that it is completely random, but it is sufficient that it is random to the extent that an image can be reconstructed in ghost imaging. Therefore, in this specification, "random" can include a certain degree of regularity. In addition, "random" does not require that it is unpredictable, but may be predictable and reproducible.

One embodiment disclosed in this specification relates to an imaging device. The imaging device includes an illumination device that sequentially switches and irradiates M (M≧2) reference light beams having a spatially random intensity distribution, a photodetector that measures reflected light from an object, and a processor that performs correlation calculation between the detection intensity based on the output of the photodetector and the intensity distribution of the reference light beam to reconstruct a restored image of the object. The processor divides the M reference light beams into a plurality of k (k≧2) units, performs correlation calculation for each divided unit to generate an intermediate image, and synthesizes the k intermediate images obtained for the k units to generate a restored image.

By dividing the correlation calculation into units, the amount of noise change per correlation calculation can be reduced, increasing the restoration accuracy and improving image quality. In addition, the correlation calculation can be started without waiting for the completion of irradiation of M reference beams, which shortens the time required to generate a restored image.

The number of reference beams contained in each unit may be M/k, which simplifies signal processing.

The number of units k may be dynamically controlled. By selecting the optimal number of units depending on the noise fluctuation speed and noise waveform, image quality can be further improved.

The number of irradiations per unit may vary from unit to unit.

One embodiment disclosed in this specification also relates to an imaging device. This imaging device includes an illumination device that sequentially switches between M reference light beams having a spatially random intensity distribution and irradiates the object, a photodetector that measures reflected light from the object, and a processor that performs correlation calculation between the detection intensity based on the output of the photodetector and the intensity distribution of the reference light beam and reconstructs a restored image of the object. The processor performs correlation calculation every time n (n≧2) reference light beams are irradiated to generate an intermediate image, and synthesizes the latest k (k≧2) intermediate images to generate a restored image.

By dividing the correlation calculation into units, the amount of noise change per correlation calculation can be reduced, improving the restoration accuracy and image quality. It also makes it possible to increase the update speed (frame rate) of the restored image.

(Embodiment)
The present invention will be described below based on preferred embodiments with reference to the drawings. The same or equivalent components, parts, and processes shown in each drawing are given the same reference numerals, and duplicated descriptions are omitted as appropriate. In addition, the embodiments are illustrative rather than limiting the invention, and all features and combinations thereof described in the embodiments are not necessarily essential to the invention.

(Embodiment 1)
2 is a diagram showing an imaging device 100 according to embodiment 1. The imaging device 100 is a correlation function image sensor that uses the principle of ghost imaging, and includes an illumination device 110, a photodetector 120, and a processor 130. The imaging device 100 is also called a quantum radar camera.

The illumination device 110 is a pseudo-thermal light source that generates a reference light S1 having a spatial intensity distribution I(x,y) that can be considered substantially random, and irradiates the object OBJ. The reference light S1 is irradiated sequentially multiple times M times while randomly changing its intensity distribution. The number of irradiations M is the number of times that the original image can be restored using a conventional imaging method (batch restoration, described below).

The illumination device 110 includes a light source 112, a patterning device 114, and a pattern generator 132. The light source 112 generates light S0 having a uniform intensity distribution. The light source 112 may be a laser, a light-emitting diode, or the like. The wavelength and spectrum of the reference light S1 are not particularly limited, and may be white light having multiple or continuous spectra, or monochromatic light including a predetermined wavelength. The wavelength of the reference light S1 may be infrared or ultraviolet.

The patterning device 114 has a plurality of pixels arranged in a matrix, and is configured to be able to spatially modulate the light intensity distribution I based on a combination of on and off states of the plurality of pixels. In this specification, a pixel in the on state is called an on pixel, and a pixel in the off state is called an off pixel. In the following description, for ease of understanding, each pixel will only take on two values (1, 0), on and off, but this is not limited to the above and may take intermediate gradations.

A reflective DMD (Digital Micromirror Device) or a transmissive liquid crystal device can be used as the patterning device 114. The patterning device 114 is provided with a pattern signal PTN (image data) generated by a pattern generator 132.

The pattern generator 132 generates a pattern signal _{PTN_r} that specifies the intensity distribution _Ir of the reference beam S1, and switches the pattern signal _{PTN_r} over time (r=1, 2, . . . M).

The photodetector 120 measures the reflected light from the object OBJ and outputs a detection signal D _r . The detection signal D _r is a spatial integral value of the light energy (or intensity) incident on the photodetector 120 when the object OBJ is irradiated with a reference light having an intensity distribution I _r . Therefore, a single-pixel photodetector can be used as the photodetector 120. The photodetector 120 outputs a plurality of detection signals D ₁ to D _M corresponding to the plurality of M intensity distributions I ₁ to I _M, respectively.

The arithmetic processing device 130 includes a pattern generator 132 and a reconstruction processing unit 134. The reconstruction processing unit 134 performs correlation calculation between a plurality of intensity distributions (also called random patterns) I ₁ to I _M and a plurality of detection intensities b ₁ to b _M , and reconstructs a restored image G(x, y) of the object OBJ.

The detection intensities b ₁ to b _M are based on the detection signals D ₁ to D _M. The relationship between the detection intensities and the detection signals may be determined taking into consideration the type and method of the photodetector 120.

It is assumed that a reference light S1 with a certain intensity distribution _Ir is irradiated for a certain irradiation period. The detection signal _Dr represents the amount of light received at a certain time (or a very short time), i.e., an instantaneous value. In this case, the detection signal _Dr may be sampled multiple times during the irradiation period, and the detection intensity _br may be the integral, average, or maximum value of all the sampled values of the detection signal _Dr. Alternatively, some of the all the sampled values may be selected, and the integral, average, or maximum value of the selected sampled values may be used. The selection of the multiple sampled values may be, for example, by extracting the xth to yth values in the order counting from the maximum value, by excluding sampled values lower than an arbitrary threshold value, or by extracting sampled values in a range in which the magnitude of signal fluctuation is small.

When a device capable of setting the exposure time, such as a camera, is used as the photodetector 120, the output D _r of the photodetector 120 can be used as the detection intensity b _r as it is.

The conversion from the detection signal D _r to the detection intensity b _r may be performed by the arithmetic processing device 130 or may be performed outside the arithmetic processing device 130 .

The reconstruction processing unit 134 of the calculation processing device 130 generates a restored image by dividing M irradiations into a plurality of k (k≧2) units. Specifically, the reconstruction processing unit 134 performs correlation calculations for each divided unit to generate an intermediate image M _j (x, y). Then, the k intermediate images M ₁ to M _k (x, y) obtained for the k units are synthesized to generate a final restored image G(x, y).

For simplicity, the number of irradiations n for each unit is assumed to be equal, M/k. In this case, the restored image G(x, y) is expressed by the following formula (2): _Ir is the rth intensity distribution, _br is the rth detection intensity value, and < _br ^[j] > is the average value of the detection intensity _br measured in the jth unit.

The j-th term on the right side of equation (2) represents the intermediate image M _j (x, y) of the j-th unit. Therefore, the restored image G(x, y) is synthesized by simply adding corresponding pixels of the multiple intermediate images M _j (x, y). Note that the synthesis method is not limited to simple addition, and weighted addition or other processing may be performed.

The arithmetic processing device 130 can be implemented as a combination of a processor (hardware) such as a CPU (Central Processing Unit), MPU (Micro Processing Unit), or microcomputer, and a software program executed by the processor (hardware). The arithmetic processing device 130 may be a combination of multiple processors. Alternatively, the arithmetic processing device 130 may be configured only from hardware.

The above is the configuration of the imaging device 100. Next, we will explain its operation.

Fig. 3 is a time chart illustrating the operation of imaging apparatus 100. In Fig. 3, the total number of pixels of patterning device 114 is represented by p = 4 x 4 = 16 pixels, and M random patterns I ₁ to I _M are generated for the generation of one restored image G(x, y) (capturing one frame).

The reconstructed image is generated by dividing it into k units, each containing n exposures.

The first unit includes the first to nth irradiations, and generates detection intensities b ₁ to b _n corresponding to the nth irradiation, and generates their average value < _br ^[1] >. Then, correlation calculation is performed using the detection intensities b ₁ to b _n , their average value < _br ^[1] >, and the intensity distributions I _n+1 to I _2n to generate an intermediate image M ₁ (x, y).

The second unit includes the n+1 to 2n irradiations, and generates detection intensities b _n+1 to b _2n corresponding to the n irradiations, and generates their average value _{<br [2] >} ^. Then, correlation calculation is performed using the detection intensities b _n+1 to b _2n , ^their average value _{<br [2] >} , and the intensity distributions I _n+1 to I _2n to generate an intermediate image M ₂ (x, y).

Similarly, the jth unit includes the (j-1)n+1 to jn irradiations, and detection intensities b _(j-1)n+1 to _bjn corresponding to the nth irradiation are generated, and their average value < _br ^[j] > is generated. Then, correlation calculation is performed using the detection intensities b _(j-1)n+1 to _bjn , their average value < _br ^[j] >, and the intensity distribution I _(j-1)n+1 to _Ijn , to generate an intermediate image _Mj (x,y).

The final k-th unit includes the (k-1)n+1 to kn irradiations, and detection intensities b _(k-1)n+1 to _bkn corresponding to the n irradiations are generated, and their average value < _br ^[k] > is generated. Then, correlation calculation is performed using the detection intensities b _(k-1)n+1 to _bkn , their average value < _br ^[k] >, and the intensity distributions I _(k-1)n+1 to _Ikn to generate an intermediate image _Mk (x,y).

Then, the k intermediate images M ₁ (x, y) to M _k (x, y) are synthesized to generate the final restored image G(x, y).

The above is the operation of the imaging device 100. According to this imaging device 100, by dividing the correlation calculation into units, it is possible to reduce the amount of noise change per correlation calculation, thereby increasing the restoration accuracy and improving the image quality. In addition, since the correlation calculation can be started without waiting for the completion of M irradiations, the time required to generate a restored image can be shortened.

The following describes the improvement in noise resistance achieved by the imaging device 100. Hereinafter, imaging using correlation calculation for each unit based on equation (2) will be referred to as split restoration. Conventional imaging using correlation calculation based on equation (1) will be referred to as collective restoration.

For example, consider noise that increases monotonically with time (called linear noise). This type of linear noise can occur when the distance between the noise source and the photodetector decreases over time.

Fig. 4(a) is a diagram showing the influence of noise in collective reconstruction, and Fig. 4(b) is a diagram showing the influence of noise in divided reconstruction. The horizontal axis represents the number of the irradiation pattern, i.e., time. _σr represents the noise intensity when the rth pattern is irradiated, and here, it is assumed that it increases according to _σr = α × r.

4(a), when performing collective restoration, the integrated value of the absolute value |Δσ r | of the difference Δσ _r ( ₌ σ _r −<σ _r >) between the noise intensity b _r detected during each pattern irradiation and the noise average value <σ _r > over all irradiations corresponds to the hatched area and is expressed by the following formula (3).
Σ|Δσ _r |=2×1/2×(kn/2)×(knα/2)=n ² αk ² /4 …(3)

4B, where k = 5. When performing division and restoration, the integrated value Σ|Δσ r | of the absolute value |Δσ _r | of the difference Δσ _r (=σ _r -<σ _r ^[j] > ₎ in the j-th unit corresponds to the hatched area and is expressed by formula (4).
Σ|Δσ _r |=2×1/2×(n/2)×(nα/2)=n ² α/4 …(4)
The integrated value over the entire irradiation can be calculated by multiplying the value of equation (4) by k, which is kn ² α/4.

In this way, by performing division and restoration, the integrated value of the absolute value |Δσ _r | of the noise intensity difference becomes small, and noise resistance can be improved.

Next, the results of simulations regarding batch restoration and divided restoration will be explained.
Fig. 5(a) shows a target image, and Fig. 5(b) shows images obtained by collective restoration and divided restoration when there is no noise. The simulation was performed for M = 1000, M = 10000, and M = 100000. The number of units k is set to 10.

In batch restoration, M = 100,000 is required to restore the original target image to a level where it can be recognized, and the same is true for partitioned restoration. At M = 100,000, the accuracy of the final restored image is equivalent between batch restoration and partitioned restoration.

6 is a diagram showing a restored image G(x, y) obtained by collective restoration and divided restoration when linear noise is present. The total number of irradiations is M=10,000, and the average value of the detection intensity is _<br> =1000. The noise coefficient α is calculated in four ways: 0, 0.01, 0.1, and 1. α=0 corresponds to the absence of noise. Regarding the divided restoration, calculations are performed for k=10, 100, and 1000.

As can be seen from the simulation results in Figure 6, the higher the number of divisions k, the higher the noise resistance.

(Embodiment 2)
In the first embodiment, as shown in Fig. 3, an independent process is performed for each frame, and a new restored image is generated for each frame period, whereas in the second embodiment, the restored image is updated every time a new intermediate image M is generated.

Figure 7 is a diagram explaining an imaging method according to embodiment 2. In embodiment 2, reference light is continuously irradiated, and correlation calculations are performed every n irradiations. Then, the process of generating a restored image G(x, y) by synthesizing the most recent k intermediate images M(x, y) is repeated for each unit. Note that the imaging method according to embodiment 2 can be realized in hardware terms by the imaging device 100 of Figure 2, by simply modifying the processing of the reconstruction processing unit 134.

The rest is the same as in embodiment 1. According to the imaging method of embodiment 2, the speed at which the restored image is generated can be increased.

The present invention has been described above based on an embodiment. This embodiment is merely an example, and those skilled in the art will understand that various modifications are possible in the combination of each component and each processing process, and that such modifications are also within the scope of the present invention. Below, such modifications are described.

(Variation 1)
In the embodiment, the number of irradiations per unit is equal, but this is not limited thereto, and the number of irradiations per unit does not have to be equal.

(Variation 2)
In the embodiment, the number of units k is constant, but the number of units k may be dynamically controlled. By selecting an optimal number of units k according to the noise fluctuation speed and noise waveform, the image quality can be further improved.

(Variation 3)
In the embodiment, illumination device 110 is configured by a combination of light source 112 and patterning device 114, but this is not limited thereto. For example, illumination device 110 may be configured by an array of a plurality of semiconductor light sources (LEDs (light emitting diodes) or LDs (laser diodes)) arranged in a matrix, and each semiconductor light source may be controlled to be turned on and off (or its brightness).

(Application)
Next, a description will be given of applications of the imaging device 100. Fig. 8 is a block diagram of an object identification system 10. This object identification system 10 is mounted on a vehicle such as an automobile or motorcycle, and determines the type (category) of an object OBJ that exists around the vehicle.

The object identification system 10 includes an imaging device 100 and a processor 40. As described above, the imaging device 100 generates a restored image G of the object OBJ by irradiating the object OBJ with a reference light S1 and measuring the reflected light S2.

The calculation processing device 40 processes the output image G of the imaging device 100 and determines the position and type (category) of the object OBJ.

The classifier 42 of the arithmetic processing device 40 receives the image G as an input and determines the position and type of the object OBJ contained therein. The classifier 42 is implemented based on a model generated by machine learning. The algorithm of the classifier 42 is not particularly limited, but may be YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), R-CNN (Region-based Convolutional Neural Network), SPPnet (Spatial Pyramid Pooling), Faster R-CNN, DSSD (Deconvolution-SSD), Mask R-CNN, or an algorithm to be developed in the future.

The above is the configuration of the object identification system 10. By using the imaging device 100 as a sensor for the object identification system 10, the following advantages can be obtained.

By using the imaging device 100, i.e., the quantum radar camera, noise resistance is significantly improved. For example, when driving in rain, snow, or fog, it is difficult to recognize the object OBJ with the naked eye, but by using the imaging device 100, it is possible to obtain a restored image G of the object OBJ without being affected by rain, snow, or fog.

In addition, by using the imaging device 100, it is possible to reduce calculation delays, thereby providing low-latency sensing. In particular, in vehicle-mounted applications, the object OBJ may move at high speeds, so the benefits of low-latency sensing are enormous.

Figure 9 is a block diagram of an automobile equipped with an object identification system 10. The automobile 300 is equipped with headlights 302L and 302R. The imaging device 100 is built into at least one of the headlights 302L and 302R. The headlight 302 is located at the very front of the vehicle body, and is the most advantageous location for installing the imaging device 100 in terms of detecting surrounding objects.

Figure 10 is a block diagram showing a vehicle lamp 200 equipped with an object detection system 210. The vehicle lamp 200, together with a vehicle-side ECU 304, constitutes a lighting system 310. The vehicle lamp 200 comprises a light source 202, a lighting circuit 204, and an optical system 206. The vehicle lamp 200 is further provided with an object detection system 210. The object detection system 210 corresponds to the above-mentioned object identification system 10, and includes an imaging device 100 and a processing device 40.

The information on the object OBJ detected by the arithmetic processing device 40 may be used for the light distribution control of the vehicle lamp 200. Specifically, the lamp side ECU 208 generates an appropriate light distribution pattern based on the information on the type of object OBJ and its position generated by the arithmetic processing device 40. The lighting circuit 204 and the optical system 206 operate to obtain the light distribution pattern generated by the lamp side ECU 208.

In addition, information regarding the object OBJ detected by the calculation processing device 40 may be transmitted to the vehicle side ECU 304. The vehicle side ECU may perform automatic driving based on this information.

The present invention has been described using specific terms based on the embodiments, but the embodiments only show one aspect of the principles and applications of the present invention, and many modifications and changes in arrangement are permitted to the embodiments without departing from the concept of the present invention as defined in the claims.

The present invention relates to an imaging device that utilizes ghost imaging.

OBJ... object, 10... object identification system, 40... processing device, 42... classifier, 100... imaging device, 110... lighting device, 112... light source, 114... patterning device, 120... photodetector, 130... processing device, 132... pattern generator, 134... reconstruction processing unit, 200... vehicle lamp, 202... light source, 204... lighting circuit, 206... optical system, 300... automobile, 302... headlamp, 310... lamp system, 304... vehicle side ECU

Claims (9)

an illumination device that sequentially switches and irradiates reference light having a spatially random intensity distribution;
a photodetector for measuring reflected light from an object;
a processor that performs correlation calculation between a detection intensity based on an output of the photodetector and an intensity distribution of the reference light, and reconstructs a restored image of the object;
Equipped with
The arithmetic processing device includes :
M (M≧2) reference beams are divided into a plurality of k (k≧2) units;
For the j-th unit, the number of the multiple reference beams included in the unit is n _j (where n _j ≧2), the intensity distribution of the r-th reference beam (1≦r≦n _j ) is I _r , and the detected intensity of the r-th reference beam is _br. Then, a correlation calculation is performed based on the formula (1) to generate an intermediate image M _j ;

The k intermediate images M ₁ to M _k obtained for the k units are synthesized to generate the restored image;
M is the number of images G calculated based on formula (2) that can restore the original image when the irradiation is not divided and the image is restored in a single operation under noise-free conditions ,

The imaging device is characterized in that the number n ₁ to n _k of the reference beams included in one unit is smaller than the number of intermediate images M ₁ to M _k that can restore the original image under noise-free conditions . 2. The imaging apparatus according to claim 1, wherein the numbers of reference beams n ₁ to n _k included in each unit are equal to M/k. The imaging device according to claim 1 or 2, characterized in that the number k of units is dynamically controlled. An illumination device that sequentially switches between and irradiates M reference beams having a spatially random intensity distribution;
a photodetector for measuring reflected light from an object;
a processor that performs correlation calculation between a detection intensity based on an output of the photodetector and an intensity distribution of the reference light, and reconstructs a restored image of the object;
Equipped with
The arithmetic processing device regards n (n≧2) reference beams as one unit, and each time one unit is irradiated, performs correlation calculation based on Equation (3) to generate an intermediate image Mj, where _Ir is the intensity distribution of the r -th (1≦r≦n) reference beam and _br is the detected intensity of the r-th reference _beam ;

synthesizing the latest k (k≧2) intermediate images to generate the restored image;
The number n of reference beams included in one unit is smaller than the number of intermediate images Mj that can restore the original image under noise-free _conditions ,
An imaging device characterized in that k×n=M is a number of images G calculated based on equation (4) that can restore the original image when the irradiation is not divided and the image is restored all at once under noise-free conditions.

An imaging device according to any one of claims 1 to 4, characterized in that the synthesis of the intermediate images includes a simple addition process of corresponding pixel values. A vehicle lamp comprising an imaging device according to any one of claims 1 to 5. A vehicle comprising an imaging device according to any one of claims 1 to 6. A step of sequentially switching reference light having a random intensity distribution and irradiating the object;
measuring light reflected from the object with a light detector;
Dividing M (M≧2) reference beams into a plurality of k (k≧2) units;
a step of generating an intermediate image Mj by performing correlation calculation based on formula ( 1 ₎ for a j-th unit, where nj ( _nj ≧2) is the number of reference beams included in the unit, Ir is the intensity distribution of the r-th reference beam (1≦r≦ _nj ) _, and _br is the detection intensity based on the output of the photodetector obtained as a result of the r- th _irradiation ;

A step of synthesizing the k intermediate images M ₁ to M _k obtained for the k units to generate a restored image;
Equipped with
M is the number of images G calculated based on formula (2) that can restore the original image when the irradiation is not divided and restored collectively under noise-free conditions ,

The imaging method, wherein the number n ₁ to n _k of the reference beams included in one unit is smaller than the number of intermediate images M ₁ to M _k that can restore the original image under noise-free conditions . A step of sequentially switching reference light having a spatially random intensity distribution and irradiating the object;
measuring light reflected from the object with a light detector;
a step of performing a correlation calculation based on equation (3) to generate an intermediate image Mj each time one unit is irradiated, where _Ir is the intensity distribution of the r-th (1≦r≦n) reference light and b _r is the detection intensity based on the output of the photodetector obtained as a result of the r-th irradiation , where n (n≧2) reference light beams are one unit _;

A step of synthesizing the latest k (k≧2) intermediate images to generate a restored image;
Equipped with
The number n of reference beams included in one unit is smaller than the number of intermediate images Mj that can restore the original image under noise-free _conditions ,
An imaging method characterized in that k×n=M is a number of images G calculated based on equation (4) that can restore the original image when the irradiation is not divided and the image is restored all at once under noise-free conditions.

Images