Compact optical device

Abstract

In observation of a distant object with a telescope or the like, it is required to remove noise in a visible light region such as atmospheric turbulence. Although a method using SPI has been proposed as a method for removing noise due to atmospheric turbulence, the resolution of observation in the visible region is low and atmospheric turbulence has not been removed, although remote monitoring is barely possible with the current SPI.SOLUTION: In order to solve the above problem, optical signals are acquired using a DMD and a plurality of photodetectors, and in a correlation process of imaging using the optical signals, optical correlation signals are acquired from selective wavelengths of the optical signals from the plurality of photodetectors, thereby simultaneously acquiring reconstructed images in a wide wavelength band. By mounting the DMD, the plurality of photodetectors, and the correlation process of this configuration on an imaging device or a magnifying optical device, it is possible to perform imaging instead of adaptive optics for suppressing spatial noise. As a light source, a reconstructed image is acquired for a wide range of wavelengths such as visible light, infrared light, and ultraviolet light.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a technique for acquiring a noise-corrected image of a photographed object, with the aim of correcting various noise components generated in the observation system and making the original image clearer, in a method for generating the original image by reconstructing the image of the target object using an optical correlation process and deep learning, in which an SPI optical system is installed in an observation optical system such as a telescope or microscope, and object image information is acquired as coded light intensity. [Background technology]

[0002] High-precision imaging is required for long-distance monitoring during disasters and astronomical observations. High-precision information transfer is also essential in optical wireless communications. In these situations, atmospheric turbulence has traditionally reduced imaging and information acquisition accuracy. Atmospheric turbulence is a phenomenon of light refraction caused by uneven temperature distribution in the air. Atmospheric turbulence is wavelength-dependent, with its effect being weaker at longer wavelengths. In contrast, observations at shorter wavelengths improve imaging resolution. Therefore, there is a trade-off between imaging accuracy and resolution in long-distance photography. Adaptive optics is a method for suppressing atmospheric turbulence. Adaptive optics is a technology that measures light disturbed by atmospheric turbulence and performs precise and accurate correction. Currently, effective solutions have been proposed for wavelengths longer than the near-infrared, where the effects of atmospheric turbulence are weak (Non-Patent Document 1). In contrast, high-resolution imaging in the visible range has been limited to observation systems in space, where there is no atmosphere (such as the Hubble Space Telescope). In addition, the optical equipment that constitutes adaptive optics is very expensive, and the system is complex. On the other hand, there is Single-Pixel Imaging (SPI), a device that can observe a wide wavelength range from 300 nm to 2000 nm and has higher noise resistance than camera images. SPI is a technology that converts observed light (two-dimensional) into a light intensity signal by encoding it using an image display element, which is then acquired by a photodetector and photographed using analytical calculations. Previous research on SPI has reported that it can observe objects as small as 20 cm at a distance of 2 km (Non-Patent Document 2). Another report claims that it can simultaneously photograph an object 18 km away at two wavelengths, the visible range (300 to 800 nm) and the near-infrared range (800 to 1800 nm), with a resolution of 64 × 64 pixels (Non-Patent Document 3). Regarding optical correlation imaging, improved noise resistance has been achieved by incorporating deep learning into the reconstruction process (Patent Document 1). [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] Yongxiong Ren, et.al. Optica 1, 376-382 (2014)

[0004] [Non-patent document 2] Yu, et al. Sci Rep 4, 5834 (2014)

[0005] [Non-patent document 3] Yiwei Zhang, et.al. Opt. Express 28, 18180-18188 (2020)MF Duarte, et.al, IEEE Signal Proc. Mag. 25, 83 (2008). [Patent documents]

[0006] [Patent Document 1] Patent Publication No. 2021-132330 Summary of the Invention [Problem to be solved by the invention]

[0007] For example, when observing distant objects using a telescope, it is necessary to remove noise in the visible light range, such as atmospheric turbulence. Therefore, currently, observations in outer space, where there is no atmospheric turbulence, or from the top of a high-altitude mountain, where atmospheric turbulence is minimal, require expensive equipment and conditions for photography, which is not realistic. Furthermore, a method using SPI has been proposed as a way to remove atmospheric turbulence noise, but while the current SPI is only capable of distant monitoring, its resolution in the visible range observation is low, and it has not yet succeeded in removing atmospheric turbulence. [Means for solving the problem]

[0008] As a means to solve the above problem, optical signals are acquired using a DMD and multiple photodetectors, and in the correlation process of imaging using those optical signals, optical correlation signals are acquired from the optical signals from the multiple photodetectors at selective wavelengths, thereby simultaneously obtaining reconstructed images over a wide wavelength band. By incorporating the DMD and multiple photodetectors with the above configuration and the correlation process into an imaging device or magnifying optical equipment, imaging that replaces adaptive optics, which suppresses spatial noise, becomes possible. Furthermore, reconstructed images are obtained using deep learning appropriate for a wide range of wavelengths, including visible light, infrared, and ultraviolet, as the light source. Furthermore, light over a wide range of wavelengths can be dispersed using a prism or diffraction grating and incident on a photodetector, thereby obtaining reconstructed images for each dispersed wavelength and imaging the object for each wavelength. Alternatively, light can be incident on the photodetector by rapidly switching between TFT elements and RGB filters, thereby achieving higher accuracy in color reproduction. [Effects of the Invention]

[0009] In this invention, in order to achieve imaging in the visible range with atmospheric turbulence suppressed, the effects of atmospheric turbulence are analyzed in a complementary manner using deep learning that takes advantage of the characteristics of short wavelengths (ultraviolet and visible ranges) and long wavelengths (near-infrared, infrared, and terahertz), making it possible to create an SPI imaging system that removes fluctuations and noise in the desired wavelength band. Therefore, by using this method, the following items can be made possible. 1. The proposed system can achieve high-precision imaging of remotely located objects. 2. Instead of adaptive optics, it is possible to estimate a reconstructed image that suppresses atmospheric turbulence even in weak light. 3. By applying the reflective properties of the DMD, it becomes possible to simultaneously image wavelengths from ultraviolet to infrared with high precision. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram showing an example of an overall system configuration including a compact optical device according to an embodiment. [Figure 2] FIG. 2 is a software configuration diagram of the compact optical device according to the embodiment. [Figure 3] 10A to 10C are diagrams showing a specific technique for manufacturing a compact optical device according to an embodiment. [Figure 4] FIG. 1 is a diagram showing a deep learning technique according to the present invention. [Figure 5] A conceptual diagram for demonstrating the validity of the patent of the present invention. [Figure 6] FIG. 1 shows the patent validity of the present invention. [Figure 7] FIG. 1 illustrates another embodiment of the present patent. [Figure 8] FIG. 1 illustrates another embodiment of the present patent. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments of the present invention will be described with reference to the drawings. The same components are designated by the same reference numerals throughout the drawings, and duplicated explanations will be omitted. In the following embodiments, a compact optical device according to the present invention will be described as an example.

[0012] FIG. 1 is a diagram showing an example of the overall configuration of a compact optical device 1 according to an embodiment of the present invention. The compact optical device 1 in FIG. 1 is composed of an optical system 11 and a system control and processing function 12. The optical system 11 in the figure is a reflecting telescope-type optical system that magnifies light from a distant object using a reflective optical system. However, the optical system is not limited to this and may be a binocular-type or a microscope-type optical system, such as a transmission-type optical system. It may also be a camera device that captures images. The optical system in this compact optical device 1 is composed of a Maksutov-Cassegrain telescope, a DMD (Digital Mirror Device) 10, and two photodetectors 8a and 8b. The DMD can reflect light from 300 nm to 2500 nm, allowing it to accommodate a wide range of wavelengths. Furthermore, by taking advantage of the fact that light incident on the DMD is reflected in two directions, photodetectors corresponding to the desired wavelengths are installed at the end of each reflection to acquire an optical correlation signal. This constitutes the SPI3. In this case, one of the two photodetectors is a long-wavelength photodetector 8a and the other is a short-wavelength photodetector 8b. A filter is installed in front of the light detection section of each photodetector. Long-wavelength filter 20a transmits only long-wavelength light, while short-wavelength filter 20b transmits only short-wavelength light. The wavelength range of each filter depends on the filter, and the filter's wavelength band can be freely changed to suit the desired wavelength range. In this embodiment, the target object 4 is assumed to be a distant star. A weak optical signal from the star enters condenser lens 5, is reflected by primary mirror 6, and then by secondary mirror 7, before being incident on the focal plane. Two condenser lenses 9a, 9b, and lens 9c are placed between the focal plane of the Maksutov-Cassegrain telescope and DMD 10, and these lenses are movable. This allows the size and focal position of the image of target object 4 illuminated on DMD 10 to be adjusted as desired. Taking advantage of the fact that light incident on DMD 10 is reflected in two directions, a long-wavelength photodetector 8a and a short-wavelength photodetector 8b are installed at the end of each reflection. Although the above refers to long wavelengths and short wavelengths, specifically, the long wavelength region is assumed to be wavelengths of 800 nm or more, and the short wavelength region is assumed to be in the range of 370 nm to 780 nm.This is because the long-wavelength range (near-infrared, infrared, and terahertz) is relatively less affected by atmospheric turbulence, primarily for the purpose of obtaining detailed shape information of the target object. The short-wavelength range (ultraviolet and visible) is significantly affected by atmospheric turbulence, but is used to detect signals corresponding to various light wavelengths. Alternatively, the short-wavelength range (ultraviolet and visible) is used to obtain detailed color information by detecting light of wavelengths corresponding to the three primary colors of light. Furthermore, while the above description uses two types of photodetectors, one for long wavelengths and one for short wavelengths, this is not limited to this. When using long-wavelength filter 20a and short-wavelength filter 20b, inexpensive photodetectors compatible with white light can also be used. Furthermore, while the wavelength bands described here are wavelengths above 800 nm and the wavelength range from 370 nm to 780 nm, they are not limited to these, and light in even shorter wavelength ranges can also be used. Furthermore, the target object is not limited to stars; the target object will vary when using binoculars or a microscope.

[0013] The system control and processing function 12 controls the operation of the above-mentioned functions and performs calculations. It is composed of a CPU (Central Processing Unit) 13, ROM (Read Only Memory) 14, RAM (Random Access Memory) 15, external memory 16, and system bus 18. It also includes a display unit 17 that displays detailed images of the target object 4. The CPU 13 performs on / off and detailed control of the power supply to the long-wavelength photodetector 8a, short-wavelength photodetector 8b, and DMD 10 in the optical system 11, sets various settings for each device, controls the program that performs the image processing calculations for the target object 4 described below, and sets various commands. The ROM 14 stores system software, processing calculation programs, and their initial values. The RAM 15 is a memory space for executing various software programs run by the CPU 13, or for storing data and settings temporarily recorded during calculations. The external memory 16 is used to store data that cannot be stored in the ROM 14 or RAM 15, as well as previous data. It can also be used to store software updates and new settings. Image data of the target object 4, which has been subjected to image processing and calculation by the CPU 13 (to be described later), is displayed on a display unit 17. These data and the like are transmitted and received via a system bus 18. The data may also be transmitted and received externally via the Internet 19.

[0014] FIG. 2 is a diagram showing the software configuration of the compact optical device 1 of this embodiment, which is made up of a CPU 13, a ROM 14, a RAM 15, an external memory 16, and a system bus 18. The CPU 13 is a microprocessor unit that controls the entire compact optical device 1 in accordance with a predetermined program. The system bus 18 is a data communication path for transmitting and receiving data between the CPU 13 and each part in the compact optical device 1.

[0015] ROM (Read Only Memory) 14 is a memory that stores basic operating programs such as an operating system and other application programs. For example, a rewritable ROM such as an EEPROM (Electrically Erasable Programmable ROM) or flash ROM is used, and by updating the programs stored in ROM 14, it is possible to upgrade or expand the functionality of the basic operating programs and other application programs.

[0016] Random Access Memory (RAM) 15 stores basic operation programs such as an operating system and other application programs. Rewritable ROMs, such as EEPROMs (Electrically Erasable Programmable ROMs) and flash ROMs, are used. Updating the programs stored in ROM 14 allows for version upgrades and functional expansion of the basic operation programs and other application programs. RAM 15 serves as a work area for executing the basic operation programs and other application programs. Specifically, for example, a basic operation program 14a stored in ROM 14 is loaded into RAM 15, and the CPU 13 executes the loaded basic operation program to form a basic operation execution unit 15a. For simplicity's sake, the following description will be given assuming that the basic operation execution unit 15a controls each component by the CPU 13 loading and executing the basic operation program 14a stored in ROM 14 into RAM 15. Similar descriptions will be used for other application programs.

[0017] The light intensity processing execution unit 15b performs various software processes on the electrical signals converted from the light intensities of the long-wavelength-compatible photodetector 8a and the short-wavelength-compatible photodetector 8b, such as correcting the light intensity expressed as the electrical signal level, and performing edge processing and noise removal to make the converted electrical signals clearer. The optical correlation calculation execution unit 15c, deep learning calculation execution unit 15d, and reconstruction calculation execution unit 15e, which will be described in detail later, perform various light propagation calculations on the light intensities obtained by the long-wavelength-compatible photodetector 8a and the short-wavelength-compatible photodetector 8b, thereby performing calculations to create two optical correlation signals generated from each photodetector of the target object 4 in new detail. The deep learning calculation execution unit 15d uses the two optical correlation signals obtained by the optical correlation calculation execution unit 14c and processes the two optical correlation signals by inputting them into deep learning appropriate for each optical correlation signal. Regarding various data collection in deep learning, various necessary data is obtained from the Internet 19 via the system bus 18 and calculations are performed based on that data. This data does not need to be always obtained via the Internet 19, and may be recorded in advance in external memory 16. New data may also be re-registered through learning. Reconstruction calculation execution unit 15e performs processing to reconstruct an image of the original object using the results obtained by deep learning execution unit 15d. Setting value execution unit 15f for various components controls various setting values ​​and sets initial values ​​for long wavelength compatible photodetector 8a, short wavelength compatible photodetector 8b, condenser lenses 9a and 9b, lens 9c, DM D10, display unit 17, etc. Image display execution unit 15g performs processing to display on display unit 17 a detailed image of the target object reconstructed from the data calculated by reconstruction calculation execution unit 15e (described later). Temporary storage area 15h is an area for temporarily storing data and the like during each of the above-mentioned processes.

[0018] As shown in FIG. 2 , the operation of the compact optical device 1 of this embodiment is controlled by the light intensity calculation execution unit 15b, the optical correlation calculation execution unit 15c, the deep learning calculation execution unit 15d, the reconstruction calculation execution unit 15e, the various component setting value execution unit 15f, and the image display execution unit 15g, which are mainly stored in the external memory 16 and expanded in the RAM 15. The CPU 13 then executes the light intensity calculation execution unit 15b, the optical correlation calculation execution unit 15c, the deep learning calculation execution unit 15d, the reconstruction calculation execution unit 15e, the various component setting value execution unit 15f, and the image display execution unit 15g. The various information / data storage area 16a is an area for storing initial values, etc., of various functions. The light intensity calculation execution unit 15b, the optical correlation calculation execution unit 15c, the deep learning calculation execution unit 15d, the reconstruction calculation execution unit 15e, and the image display execution unit 15g may be implemented in part or in whole by hardware blocks. The ROM 14 and RAM 15 may be integrated with the CPU 13. The ROM 14 may not be an independent configuration as shown in Fig. 2, but may use a partial storage area within the external memory 16. The RAM 15 is also provided with a temporary storage area for temporarily storing data as necessary when various application programs are executed.

[0019] The external memory 16 may store various operational setting values ​​of the compact optical device 1, position information and various other information of the compact optical device 1, some or all of the data of images and information captured by the compact optical device 1, and other programs.

[0020] A portion of the external memory 16 may replace all or part of the functions of the ROM 14. Furthermore, the external memory 16 needs to retain the stored information even when power is not supplied to the compact optical device 1. Therefore, devices such as flash ROM, SSD (Solid State Drive), and HDD (Hard Disc Drive) may be used.

[0021] Next, the basic technology of the present invention will be explained using mathematical formulas and Figure 3. The optical correlation process is composed of a Maksutov-Cassegrain telescope 2, a DMD 10, a long-wavelength photodetector 8a, a short-wavelength photodetector 8b, two focusing lenses 9a and 9b, and a lens 9c. In Figure 3, optical signals from the target object 4 enter the long-wavelength photodetector 8a and the short-wavelength photodetector 8b through the telescope 2, and the output signals from each detector form a short-wavelength optical correlation signal S1 and a long-wavelength optical correlation signal S2. In the optical system for the optical correlation process, lens 9c is inserted between the focal plane of the Maksutov-Cassegrain telescope 2 and the DMD 10. This lens 9c is movable, allowing the size of the object image illuminated on the DMD 10 to be adjusted as desired. This control is performed by the CPU 13. Taking advantage of the fact that light incident on the DMD 10 is reflected in two directions, a long-wavelength photodetector 8a and a short-wavelength photodetector 8b are installed at the ends of each reflection.

[0022] The deep learning-based SPI system in Figure 1 consists of an optical correlation process using an optical system equipped with SPI 3 on a telescope 2, and a deep learning-based reconstruction process. In the optical correlation process, a distant target object 4 is captured by the telescope 2, and the object image is irradiated onto an encoded pattern to obtain optical correlation signals using a long-wavelength compatible photodetector 8a and a short-wavelength compatible photodetector 8b. In addition to a method using deep learning, the reconstruction process can also use the computational reconstruction method inherent to SPI. In the optical correlation process, the optical correlation signals obtained from the two photodetectors are used to perform calculations appropriate for each optical correlation signal. The computational reconstruction method is expressed by the following equation.

[0023]

number

[0024] Here, S1 and S2 are the optical correlation signal S1 acquired by the short wavelength photodetector and the optical correlation signal S2 acquired by the long wavelength photodetector, respectively. Also, O1 and O2 represent the object images reconstructed from the photodetectors in the two directions, and H represents the encoding pattern. A specific example will be presented next to demonstrate the effectiveness of this patent. The experimental system for the proof-of-principle experiment is shown below. A monitor was placed 10 m away from the optical system of an astronomical telescope equipped with SPI, and an object was displayed on it. 2048 MNIST (Mixed National Institute of Standards and Technology database) binary images of handwritten characters were used as the target object. 1024 Hadamard patterns were used as the encoding pattern. The object light displayed on the monitor propagated 10 m through air, entered the aperture plane of the telescope, and was magnified. The image was then formed on a DMD installed at the telescope's direct focal plane. 1024 Hadamard patterns, which are orthogonal basis matrices, were displayed on the DMD. The light reflected by the DMD was collected by a lens, and an optical correlation signal was obtained using a PMT (Photomultiplier Tube). Reconstruction was then performed using the reconstruction calculation formula (1). Ten images randomly selected from the 2048 reconstruction results are shown in Figure 4. It can be seen that the characters can be recognized in the image reconstructed from the optical correlation signals acquired in both directions.

[0025] Next, an overview of the deep learning reconstruction process in an atmospheric turbulence environment is shown in Figure 5. This network consists of five transposed convolutional layers and three convolutional layers, and performs learning to reduce the effects of atmospheric turbulence so as to approach the target image. However, the deep learning network used in the reconstruction process can be flexibly selected. In the proof-of-principle experiment, we used 2048 MNIST images as the target objects and 1024 Hadamard patterns as the encoding patterns. MNIST is a 28 x 28 pixel dataset consisting of the digits 0 to 9, totaling 60,000 images. For evaluation, we enlarged the MNIST images to 32 x 32 pixels and performed binarization. We also generated a spatial phase distribution of atmospheric turbulence based on Kolmogorov turbulence theory and varied it over time to represent atmospheric turbulence. We simulated observations with a 51 cm telescope at the summit of Mauna Kea in Hawaii. Figure 6 shows the reconstruction results of this imaging system. O1 and O2 represent the results reconstructed using each photodetector. For comparison, we also show the reconstruction results using the SPI reconstruction method. While the characters were not visible in the SPI reconstructed image, they were visible in the deep neural network (DNN) reconstruction, confirming the effectiveness of this technology. In the above description, deep learning appropriate for each optical correlation signal is performed, and it is described that each deep learning is performed independently, but this is not limited to this, and each deep learning may be performed in a mutually complementary manner.

[0026] Next, another application example of the compact optical device 1 of the present invention, a compact optical device 1 using a prism or TFT elements, will be described with reference to FIG. 7. FIG. 7(a) is a diagram illustrating a method for clearly acquiring information on light of a wider variety of wavelengths using a prism 21 and three short-wavelength photodetectors 8b1, 8b2, and 8b3. The optical signal from the DMD 10 is split by the prism 21. The light with the smallest refractive index, which is closer to the long wavelength, enters short-wavelength photodetector A (8b1), the light with the next highest refractive index enters short-wavelength photodetector B (8b2), and the light with the highest refractive index enters short-wavelength photodetector C (8b3). As a result, each short-wavelength photodetector detects the light as an optical signal of the respective wavelength, which is then detected by the three short-wavelength photodetectors A, B, and C in descending order of wavelength. Therefore, by performing a reconstruction process using the optical correlation signals formed from these three optical signals, a reconstructed image of the target object at three wavelengths can be generated. The light from the object can reveal the internal state of the object and its constituent materials based on the wavelength of the light. For example, infrared light wavelengths can be used to determine the temperature distribution of an object. Furthermore, shorter wavelengths can be used to view the internal structure of an object. In this way, by generating a reconstructed image using light of multiple wavelengths, it is possible to obtain detailed information about an object at once, in addition to simply its surface structure.

[0027] The wavelength of light incident on the three short-wavelength photodetectors 1, 2, and 3 can be split into the three primary colors of light: red, green, and blue. This allows each short-wavelength photodetector to detect RGB optical signals. Therefore, by using the RGB optical correlation signals formed from these three optical signals for the reconstruction process, the color reproducibility of the target object can be improved, resulting in the creation of high-resolution images. Figure 7(b) shows another application example, in which a TFT element 22 and an RGB filter 23 are used to improve the accuracy of light color information. The optical signal from the DM D10 enters the TFT element and, similar to a TFT LCD panel, is selectively incident on each of the red, green, and blue filters of the RGB filter by switching, and each RGB optical signal is detected by a single short-wavelength photodetector 8b. In this case, only light passing through the R, G, and B filters is incident on the short-wavelength photodetector 8b by switching the TFT element. Therefore, the R, G, and B optical signals are incident on the short-wavelength-compatible photodetector 8b at the appropriate switching timing, and the reconstruction process is performed using the RGB optical correlation signal formed from the three optical signals, as described above. This improves the color reproducibility of the target object and enables the creation of highly accurate images. The switching interval can be determined arbitrarily, and switching can be performed according to the amount of light, or it can be performed at high speed to minimize the effects of fluctuations and noise. This method allows for a single color photodetector, contributing to the miniaturization and cost reduction of the compact optical device 1. These switching controls are performed by the CPU 13. Furthermore, while the above description focuses on still images, this is not limited to this. It is also possible to reproduce moving images of the target object by generating images at, for example, 30 or 60 frames per second using switching control. Furthermore, while the above description uses an RGB filter after the TFT element, this is not limited to this; wavelength filters can be placed separately to transmit only light in the required wavelength range. Furthermore, while a prism is used in Figure 7(a), this is not limited to this; a diffraction grating can also be used. The use of a diffraction grating makes it possible to make the device even more compact.

[0028] Deep learning is performed in image reconstruction, and in the above-mentioned system, the programs and data for deep learning are stored in ROM 14 or external memory 16, and are assumed to be expanded into RAM during learning and calculations performed, but this is not limited to this, and external data may be used via the Internet 19, or the deep learning data may be updated.

[0029] Next, the arrangement and configuration of optical elements to achieve further cost reduction in the compact optical device 1 of the present invention will be described using Figure 8. Figure 8(a) shows the basic configuration shown in Figure 1, consisting of a DM D10, two focusing lenses, a long-wavelength photodetector 8a, and a short-wavelength photodetector 8b. Here, each detector is assumed to respond to a specific wavelength band, and filters have been eliminated. On the other hand, Figure 8(b) shows a compact, expensive device 1 using an optical system with a structure in which concave mirrors 1:24a and 2:24b are arranged instead of two focusing lenses. Focusing lenses are generally more expensive than concave mirrors, and their optical path length makes them unsuitable for miniaturization. Therefore, the configuration using concave mirrors in Figure 8(b) is suitable for miniaturization and low cost. [Explanation of symbols]

[0030] 1: Compact optical device, 2: Telescope, 3: SPI, 4: Target object, 5: Condenser lens, 6: Primary mirror, 7: Secondary mirror, 8a: Long wavelength compatible photodetector, 8b: Short wavelength compatible photodetector, 9a: Condenser lens 1, 9b: Condenser lens 2, 9c: Lens 3, 10: DMD, 11: Optical system, 12: System control and processing function, 13: CPU, 14: ROM, 15: RAM, 16: External memory, 17: Display unit, 18: System bus, 19: Internet, 20a: Long wavelength filter, 20b: Short wavelength filter, 21: Prism, 22: TFT element (liquid crystal element), 23: RGB filter, 24a: Concave mirror 1, 24b: Concave mirror 2, 8b1: ​​Short wavelength compatible photodetector A, 8b2: Short wavelength compatible photodetector B, 8b3: Short wavelength compatible photodetector C

Claims

1. A compact optical device having the function of receiving an optical signal from a target object using an optical system composed of multiple lenses or mirrors, etc., and at least two or more photodetectors, acquiring the light intensity of the target object from the two or more photodetectors as an optical correlation signal, and reconstructing an image of the target object based on the obtained optical correlation signal, characterized in that in reconstructing the image, the two or more photodetectors each detect light of different wavelengths, and the image is reconstructed from the light intensities of the two or more wavelengths.

2. A compact optical device that has the function of receiving an optical signal from a target object using an optical system consisting of multiple lenses or mirrors, etc., and at least two or more photodetectors, acquiring the light intensity of the target object from the two or more photodetectors as an optical correlation signal, and reconstructing an image of the target object based on the obtained optical correlation signal, characterized in that, in reconstructing the image, noise components of the target object contained in the image are removed using deep learning.

3. 2. A compact optical device according to claim 1, wherein one of the two or more different wavelengths is in the long wavelength region and the other wavelengths are in the short wavelength region.

4. 4. A compact optical device according to claim 1, wherein the long wavelength region is a wavelength region of 800 nm or more, and the short wavelength region is a region ranging from 370 nm to 780 nm.

5. A compact optical device according to claims 1 and 2, characterized in that the light intensity detected by the photodetector in the long wavelength region is mainly used to reconstruct information on the shape of an object, and the light intensity detected by the photodetector in the short wavelength region is mainly used to reconstruct information on the color of the object.

6. A compact optical device according to any one of claims 1 to 5, characterized in that light in the short wavelength region is separated using a prism or a diffraction grating, and the intensity of the light is detected by each photodetector to reconstruct information on the light of each wavelength.

7. A compact optical device according to any one of claims 1 to 5, characterized in that light in the short wavelength region is separated into red, green and blue light, which are the three primary colors of light, using a prism or a diffraction grating, and the intensity of the light is detected by each photodetector to reconstruct information on each color.

8. A compact optical device according to any one of claims 1 to 5, characterized in that light in the short wavelength region is incident on a device composed of a TFT element and a specific wavelength filter, light of each wavelength is incident on a photodetector, the intensity of each light is detected, and information on the wavelength of each light is reconstructed.

9. A compact optical device according to any one of claims 1 to 5, characterized in that light in the short wavelength region is incident on a device composed of a TFT element and an RGB filter, and light of the three primary colors of light, red, green, and blue, is incident on a photodetector, and the intensity of each light is detected to reconstruct information on each color.

10. 6. A compact optical device according to any one of claims 1 to 5, wherein a concave mirror is used as an element for focusing light onto each photodetector.

Images