Speckle self-correlation imaging method and device under thermal light illumination

Abstract

The invention discloses a speckle self-correlation imaging method and a speckle self-correlation imaging device under thermal light illumination. According to the speckle self-correlation imaging method under the thermal light illumination, under the conditions that other factors are not changed and specified factors are regularly changed, sampling images of an object to be imaged under different specified factors are collected, the sampling images are sorted according to the sequence of the collected sampling images, and a sampling image set is obtained, the specified factor is the central wavelength of the filter or the distance between the sampling surface and the scattering medium; determining a first sampling image in the sampling image set as an effective sampling image; traversing the sampling image set in sequence, determining a correlation coefficient of the sampling image and a newly determined effective sampling image for each sampling image, and determining the sampling image as the effective sampling image when the correlation coefficient is not greater than a specified threshold value; and completing speckle self-correlation imaging of the object to be imaged by adopting the determined effective sampling images.

Description

Technical Field

[0001] The present invention relates to the technical field of autocorrelation imaging, and in particular to a speckle autocorrelation imaging method and device under thermal light illumination. Background Art

[0002] Benefiting from the advantages of non-invasiveness and high robustness, the speckle autocorrelation imaging technology has always been a key technology in the field of imaging through scattering media. The speckle autocorrelation imaging technology can not only image an object hidden behind a scattering medium without invading the interior of the system, but also track the motion ratio and relative position of the hidden object.

[0003] However, currently, the sampling requirement of the speckle autocorrelation imaging technology is narrowband incoherent light. Therefore, in some thermal light or other broadband illumination scenarios, a narrowband filter needs to be placed before the receiving end to limit the sampling bandwidth. In cases where the light source power is insufficient or the sampling distance is too large, sometimes the contrast and signal-to-noise ratio of the sampling image need to be improved by increasing the bandwidth of the filter. However, in this case, only the speckles near the optical axis can be used as effective samples, and the sampling area far from the optical axis will not be available, resulting in problems such as insufficient sample ratio, reduced imaging quality, or even direct inability to image.

[0004] Therefore, how to improve the imaging quality of the speckle autocorrelation imaging technology under light sources such as thermal light illumination that are not narrowband incoherent light is an urgent problem to be solved. Summary of the Invention

[0005] This specification provides a speckle autocorrelation imaging method and device under thermal light illumination to at least partially solve the above problems existing in the prior art.

[0006] This specification adopts the following technical solutions: This specification provides a speckle autocorrelation imaging method under thermal light illumination, including: Under the condition that other factors remain unchanged and a specified factor changes regularly, collect sampling images of the object to be imaged under different specified factors, and sort the sampling images in the order of collection to obtain a sampling image set, where the specified factor is the central wavelength of the filter or the distance between the sampling surface and the scattering medium; Determine the first sampling image in the sampling image set as the effective sampling image; Traverse the sampling image set in order. For each sampling image, determine the correlation coefficient between this sampling image and the latest determined effective sampling image, and when the correlation coefficient is not greater than a specified threshold, determine this sampling image as the effective sampling image; Complete the speckle autocorrelation imaging of the object to be imaged using the determined effective sampling images.

[0007] Optionally, the specified factor is the central wavelength of the filter; Under the condition that other factors remain unchanged and the specified factor changes regularly, sampling images of the object to be imaged under different specified factors are collected, specifically including: Under the condition that other factors remain unchanged and the central wavelength of the filter continuously increases by a specified amount, sampling images of the object to be imaged collected at each central wavelength are obtained.

[0008] Optionally, the specified factor is the distance between the sampling surface and the scattering medium; Under the condition that other factors remain unchanged and the specified factor changes regularly, sampling images of the object to be imaged under different specified factors are collected, specifically including: Under the condition that other factors remain unchanged and the distance between the sampling surface and the scattering medium continuously increases by a specified length, sampling images of the object to be imaged collected at each distance are obtained.

[0009] Optionally, determining the correlation coefficient between this sampling image and the latest determined valid sampling image specifically includes: Determining the pixel array size of each sampling image, where the pixel array sizes of all sampling images are the same; According to the distance between the sampling surface and the scattering medium when this sampling image is collected, the distance between the sampling surface and the scattering medium when the latest determined valid sampling image is collected, and the pixel array size, the latest determined valid sampling image is scaled to obtain a scaled valid sampling image; Performing a correlation operation on this sampling image and the scaled valid sampling image to obtain the correlation coefficient between this sampling image and the latest determined valid sampling image.

[0010] Optionally, performing a correlation operation on this sampling image and the scaled valid sampling image specifically includes: Centering on the optical axis of the scaled valid sampling image, performing a cropping operation on the scaled valid sampling image to obtain a cropped valid sampling image with a size of the pixel array size; Performing a correlation operation on this sampling image and the cropped valid sampling image.

[0011] Optionally, using the determined valid sampling images to complete the speckle autocorrelation imaging of the object to be imaged, specifically including: Performing a stitching operation on the determined valid sampling images to obtain a stitched valid sampling image; Using the stitched valid sampling image to complete the speckle autocorrelation imaging of the object to be imaged.

[0012] Optionally, performing a stitching operation on the determined valid sampling images specifically includes: Scale each determined effective sampling image to different degrees so that the scaled effective sampling images are at the same imaging magnification ratio; Perform a stitching operation on the scaled effective sampling images.

[0013] A speckle autocorrelation imaging device under thermo-optical illumination provided in this specification, the device includes: An acquisition module, configured to acquire sampling images of an object to be imaged under different specified factors under the condition that other factors remain unchanged and the specified factor changes regularly, and sort the sampling images according to the order of the acquired sampling images to obtain a sampling image set; A determination module, configured to determine the first sampling image in the sampling image set as an effective sampling image; A traversal module, configured to sequentially traverse the sampling image set, for each sampling image, determine the correlation coefficient between the sampling image and the latest determined effective sampling image, and when the correlation coefficient is not greater than a specified threshold, determine the sampling image as an effective sampling image; An imaging module, configured to complete the speckle autocorrelation imaging of the object to be imaged by using the determined effective sampling images.

[0014] This specification provides a computer-readable storage medium, the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned speckle autocorrelation imaging method under thermo-optical illumination is implemented.

[0015] This specification provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the above-mentioned speckle autocorrelation imaging method under thermo-optical illumination is implemented.

[0016] At least one of the above technical solutions adopted in this specification can achieve the following beneficial effects: In the speckle autocorrelation imaging method under thermo-optical illumination provided in this specification, under the condition that other factors remain unchanged and the specified factor changes regularly, sampling images of an object to be imaged under different specified factors are acquired, and the sampling images are sorted according to the order of the acquired sampling images to obtain a sampling image set, where the specified factor is the central wavelength of the filter or the distance between the sampling surface and the scattering medium; the first sampling image in the sampling image set is determined as an effective sampling image; the sampling image set is sequentially traversed, for each sampling image, the correlation coefficient between the sampling image and the latest determined effective sampling image is determined, and when the correlation coefficient is not greater than a specified threshold, the sampling image is determined as an effective sampling image; the speckle autocorrelation imaging of the object to be imaged is completed by using the determined effective sampling images.

[0017] When adopting the speckle autocorrelation imaging method under thermo-optical illumination provided in this specification, the central wavelength of the filter or the distance between the sampling surface and the scattering medium can be used as a specified factor. Sampling images under different specified factors when the specified factor changes regularly are collected, and the effective sampling images are determined using the correlation coefficients between the sampling images. Finally, the speckle autocorrelation imaging is completed using the effective sampling images. Through this method, under the condition of a thermal light source, the number of sampling samples can be effectively increased, thereby improving the imaging quality. At the same time, this method has great application prospects in underwater target positioning, fog-penetrating imaging, etc. Description of the Drawings

[0018] The drawings described herein are used to provide a further understanding of this specification and form a part of this specification. The schematic embodiments of this specification and their descriptions are used to explain this specification and do not constitute an improper limitation to this specification. In the drawings: Figure 1 It is a schematic flowchart of a speckle autocorrelation imaging method under thermo-optical illumination in this specification; Figure 2 It is a schematic structural diagram of a speckle autocorrelation imaging experimental system provided in this specification. The meanings of the marks in the figure are as follows: 1 - Light source, 2 - Object to be imaged, 3 - Scattering medium, 4 - Aperture, 5 - Filter, 6 - Sampling surface, 7 - Computing device; Figure 3 It is a schematic diagram of a speckle autocorrelation imaging device provided in this specification; Figure 4 Corresponding to Figure 1 Schematic diagram of the electronic device. Detailed Embodiments

[0019] Theoretically, the point spread function of the system is affected by the wavelength and the sampling distance. The point spread functions corresponding to different wavelengths gradually decohere as the wavelength interval increases, and the point spread functions at different sampling distances also gradually decohere as the sampling interval increases. Using this relationship, multiple samplings can be performed at different wavelengths where the wavelength interval exceeds the decoherence bandwidth and at positions where the position interval exceeds the decoherence distance, and the effective samplings are scaled and spliced according to the imaging magnification ratio, thereby effectively increasing the actual number of samples and helping to improve the speckle autocorrelation imaging quality. Based on the above idea, this application provides a speckle autocorrelation imaging method under thermo-optical illumination with excellent effects.

[0020] To make the objectives, technical solutions, and advantages of this specification clearer, the technical solutions of this specification will be clearly and completely described below in conjunction with specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this specification without creative efforts fall within the scope of protection of this application.

[0021] The following will detail the technical solutions provided by each embodiment of this specification in conjunction with the drawings.

[0022] Figure 1 It is a schematic flowchart of a speckle autocorrelation imaging method under thermal light illumination in this specification, specifically including the following steps: S100: Under the condition that other factors remain unchanged and the specified factor changes regularly, collect the sampling images of the object to be imaged under each different specified factor, and sort the sampling images in the order of the collected sampling images to obtain a sampling image set, where the specified factor is the central wavelength of the filter or the distance between the sampling surface and the scattering medium.

[0023] All steps in the speckle autocorrelation imaging method provided by this specification can be implemented by any electronic device with computing functions, such as devices like terminals and servers.

[0024] Speckle autocorrelation imaging technology is a technology that uses the autocorrelation characteristics of speckle patterns to restore object images. In speckle autocorrelation imaging, the light source required for sampling is usually narrowband incoherent light. Therefore, in scenarios of other broadband light sources such as thermal light, a narrowband filter needs to be set to limit the sampling bandwidth.

[0025] Figure 2 It is a schematic structural diagram of a common speckle autocorrelation imaging experimental system provided by this specification. As Figure 2 shown, in a speckle autocorrelation imaging experimental system, it usually includes a light source, an object to be imaged, a scattering medium, a diaphragm, and a sampling surface. In the scenario of the thermal light source targeted by this method, a filter will be additionally included in the speckle autocorrelation imaging experimental system. Additionally, in the experiment, the photosensitive surface of the detector is usually used as the sampling surface, so a computing device connected to the detector and used to process the data received by the photosensitive surface of the detector can also be additionally included.

[0026] This method is mainly used to achieve better speckle autocorrelation imaging under a thermal light source. In speckle autocorrelation imaging technology, several light intensity distribution maps need to be collected as sampling images first. Based on this, in this step, the sampling images of the object to be imaged can be collected under the condition of controlling variables.

[0027] When sampling the object to be imaged in this step, only one specified factor changes in each round of sampling. After other factors are set before the start of a round of sampling, they do not change during this round of sampling. In this method, the specified factor that changes can be the central wavelength of the filter or the distance between the sampling surface and the scattering medium. At the same time, in this method, the change of the specified factor is regular, for example, it continuously increases or decreases according to a fixed value. The starting value, ending value, and the value of each change of the specified factor can be arbitrarily set according to the needs as long as they can meet the experimental requirements and achieve the experimental purpose. This specification does not make specific restrictions on this.

[0028] In one experiment, there can be multiple rounds of sampling. For each round of sampling, under the condition of regular change of the specified factor, sampling is performed once at each different value of each specified factor, and a number of sampling images p of the object to be imaged are obtained. 1 、p 2 、p 3 ……. Among them, p i is the sampling image obtained from the i-th sampling in this round of sampling.

[0029] Subsequently, according to the acquisition order of each sampling image, that is, the order from the earliest to the latest time of obtaining each sampling image, each sampling image is sorted to obtain a sampling image set { p 1 、p 2 、p 3 ……}.

[0030] Taking the two specified factors in this method as examples respectively for illustration. When the specified factor is the central wavelength of the filter, specifically during sampling, while keeping other factors unchanged, under the condition that the central wavelength of the filter continuously increases by a specified amount, the sampling images of the object to be imaged collected at each central wavelength are obtained. At this time, a sampling image set { 、 、 ……} can be obtained.

[0031] When the specified factor is the distance between the sampling surface and the scattering medium, specifically during sampling, while keeping other factors unchanged, under the condition that the distance between the sampling surface and the scattering medium continuously increases by a specified length, the sampling images of the object to be imaged collected at each distance are obtained. At this time, a sampling image set { 、 、 ……} can be obtained.

[0032] Among them, both the specified amount and the specified distance can be set according to specific needs.

[0033] S102: Determine the first sampling image in the sampling image set as the valid sampling image.

[0034] After obtaining the sampling atlas in step S100, the first sampling image in the sampling atlas can be determined as the valid sampling image in this step. Since in this method, the order of the sampling images in the sampling atlas is the order in which each sampling image is acquired, the valid sampling image determined in this step is the first acquired sampling image p in this round of sampling. 1 This valid sampling image is denoted as p. e1 .

[0035] S104: Traverse the sampling atlas in order. For each sampling image, determine the correlation coefficient between this sampling image and the latest determined valid sampling image, and when the correlation coefficient is not greater than the specified threshold, determine this sampling image as the valid sampling image.

[0036] After determining the first valid sampling image in step S102, the sampling atlas can be traversed in this step to determine other valid sampling images. When reconstructing the image of the object to be imaged using the valid sampling images, the valid sampling images need to be uncorrelated. Therefore, subsequent valid sampling images can be determined based on this condition in this step.

[0037] When determining other valid sampling images, each sampling image in the sampling atlas can be traversed in order, and the same operation can be performed on each sampling image. The specific operation is to determine the correlation coefficient between this sampling image and the latest determined valid sampling image. When the determined correlation coefficient is not greater than the specified threshold, this sampling image can be determined as the valid sampling image, and at this time this sampling image becomes the latest determined valid sampling image. Among them, the specified threshold can be set according to specific requirements. For example, it can usually be set to a value such as 0.4, and this specification does not make specific restrictions on this.

[0038] For example, in the sampling atlas, the first sampling image p 1 is the first valid sampling image p e1 , and at this time p e1 is the latest determined valid sampling image. Subsequently, traverse backward in the order of the sampling atlas, starting from the sampling image p 2 . Calculate the correlation coefficient between the sampling image p 2 and the sampling image p 1 . Suppose the calculated correlation coefficient is greater than the specified threshold, then discard the sampling image p 2 , and continue to traverse backward to the sampling image p 3 . Calculate the correlation coefficient between the sampling image p 3 and the sampling image p 1 . Suppose the calculated correlation coefficient is not greater than the specified threshold, then the sampling image p 3 can be determined as the second valid sampling image p e2 , and at this time the valid sampling image pe2 Becomes the latest determined effective sampling image. Continue to traverse the sampling image p backward 4 、p 5 、p 6 …… And determine whether each sampling image can become an effective sampling image according to the calculated correlation coefficient until all sampling images in the sampling image set are traversed.

[0039] Furthermore, in the case where the specified factor that has changed is the distance between the sampling surface and the scattering medium, since the acquisition positions of each sampling image are different, when calculating the correlation coefficient between a sampling image and the latest determined effective sampling image, the sampling images acquired at different positions can be scaled to the same imaging ratio to ensure the accuracy of the calculation. Specifically, the pixel array size of each sampling image can be determined, where the pixel array sizes of each sampling image are the same; according to the distance between the sampling surface and the scattering medium when acquiring this sampling image, the distance between the sampling surface and the scattering medium when acquiring the latest determined effective sampling image, and the pixel array size, perform a scaling process on the latest determined effective sampling image to obtain a scaled effective sampling image; perform a correlation operation on this sampling image and the scaled effective sampling image to obtain the correlation coefficient between this sampling image and the latest determined effective sampling image.

[0040] During one round of acquisition process, although the position of the sampling surface changes, the parameters of the sampling surface itself do not change. Therefore, the pixel array sizes of the acquired sampling images are the same. Assume that the pixel array size of each sampling image is m×n, that is, the sampling image has m pixels in the horizontal direction and n pixels in the vertical direction. When performing the correlation operation, the distance from the sampling surface to the scattering medium when the latest determined effective sampling image is acquired is z 1 ,and the distance from the sampling surface to the scattering medium when the currently traversed sampling image is acquired is z 2 . At this time, the shorter one of z 1 and z 2 , usually the latest determined effective sampling image, can be scaled. Perform interpolation processing on the effective sampling image to enlarge the original pixel array size of m×n to × , to obtain a scaled effective sampling image. At this time, the imaging magnification ratios of the scaled effective sampling image and the currently traversed sampling image are the same.

[0041] Further, after scaling the currently traversed sampling image and the latest determined valid sampling image to the same imaging magnification ratio, the pixel array size of the scaled valid sampling image will become larger. Therefore, the scaled valid sampling image can be cropped to obtain a cropped valid sampling image, so that its pixel array size returns to the original size, that is, m×n. Specifically, the scaled valid sampling image can be centered on the optical axis of the scaled valid sampling image and cropped to obtain a cropped valid sampling image with a size of the pixel array size; a correlation operation is performed on this sampling image and the cropped valid sampling image.

[0042] At this time, the imaging magnification ratio and pixel array size of the currently traversed sampling image and the cropped valid sampling image are the same, and a better correlation coefficient calculation result can be obtained.

[0043] In the above manner, by traversing each sampling image in the sampling image set, each available valid sampling image can be screened out.

[0044] S106: Complete the speckle autocorrelation imaging of the object to be imaged using the determined valid sampling images.

[0045] Finally, in this step, the determined valid sampling images can be used for image reconstruction to complete the speckle autocorrelation imaging of the object to be imaged.

[0046] Additionally, when performing image reconstruction, the determined valid sampling images can be stitched to obtain a stitched valid sampling image; the stitched valid sampling image is used to complete the speckle autocorrelation imaging of the object to be imaged. By stitching, the determined valid sampling images can have stronger spatio-temporal correlation, and finally an image with higher quality can be reconstructed.

[0047] Further, when the specified factor is the distance between the sampling surface and the scattering medium, when stitching the determined valid sampling images, the determined valid sampling images can still be scaled so that the imaging magnification ratios of the determined valid sampling images are the same. Specifically, each determined valid sampling image can be scaled to different degrees so that the scaled determined valid sampling images are at the same imaging magnification ratio; the scaled determined valid sampling images are stitched.

[0048] Since the specified factor, the distance between the sampling surface and the scattering medium, is different when each valid sampling image is collected, the scaling ratios required for each valid sampling image are also different. Usually, the last determined valid sampling image has the largest imaging magnification ratio. Therefore, the last determined valid sampling image can be used as a standard to scale the other valid sampling images to the same imaging magnification ratio as this valid sampling image.

[0049] The specific scaling method is the same as the scaling method introduced in step S104. Continuing with the previous specific embodiment, assume that the original pixel array size of each sampled image is still m×n. Assume that the distance from the sampling plane to the scattering medium when the last determined valid sampled image is sampled is z x , and the distance from the sampling plane to the scattering medium when the currently needed valid sampled image to be scaled is sampled is z y . Then, interpolation processing can be performed on the valid sampled image to be scaled to scale its pixel array size to × . Performing the above scaling operation on each valid sampled image can make the imaging magnification ratios of all valid sampled images the same.

[0050] Of course, further cropping operations can be performed on each scaled valid sampled image to make the pixel size array of each scaled valid sampled image return to m×n, and then stitching operations can be performed to obtain a better reconstruction effect.

[0051] When adopting the speckle autocorrelation imaging method under thermal light illumination provided in this specification, the central wavelength of the filter or the distance between the sampling plane and the scattering medium can be used as a specified factor. Sampled images under different specified factors when the specified factor changes regularly are collected, and valid sampled images are determined using the correlation coefficients between the sampled images. Finally, speckle autocorrelation imaging is completed using the valid sampled images. Through this method, the number of sampling samples can be effectively increased under the condition of a thermal light source, thereby improving the imaging quality. At the same time, this method has great application prospects in aspects such as underwater target positioning and fog-penetrating imaging.

[0052] The above is the speckle autocorrelation imaging method under thermal light illumination provided in this specification. Based on the same idea, this specification also provides a corresponding speckle autocorrelation imaging device, as shown in Figure 3 .

[0053] Figure 3 is a schematic diagram of a speckle autocorrelation imaging device under thermal light illumination provided in this specification, specifically including: An acquisition module 200, configured to collect sampled images of an object to be imaged under different specified factors under the condition that other factors remain unchanged and the specified factor changes regularly, and sort the sampled images in the order of the collected sampled images to obtain a sampled image set; A determination module 202, configured to determine the first sampled image in the sampled image set as a valid sampled image; A traversal module 204, configured to sequentially traverse the sampled image set, for each sampled image, determine the correlation coefficient between the sampled image and the latest determined valid sampled image, and when the correlation coefficient is not greater than a specified threshold, determine the sampled image as a valid sampled image; The imaging module 206 is configured to perform speckle autocorrelation imaging of the object to be imaged using each determined effective sampling image.

[0054] Optionally, the specified factor is the central wavelength of the filter. The acquisition module 200 is specifically configured to obtain sampling images of the object to be imaged collected at each central wavelength under the condition that other factors remain unchanged and the central wavelength of the filter increases continuously by a specified amount.

[0055] Optionally, the specified factor is the distance between the sampling surface and the scattering medium. The acquisition module 200 is specifically configured to obtain sampling images of the object to be imaged collected at each distance under the condition that other factors remain unchanged and the distance between the sampling surface and the scattering medium increases continuously by a specified length.

[0056] Optionally, the traversal module 204 is specifically configured to determine the pixel array size of each sampling image, where the pixel array sizes of all sampling images are the same; perform scaling processing on the latest determined effective sampling image according to the distance between the sampling surface and the scattering medium when collecting this sampling image, the distance between the sampling surface and the scattering medium when collecting the latest determined effective sampling image, and the pixel array size, to obtain a scaled effective sampling image; perform a correlation operation on this sampling image and the scaled effective sampling image to obtain the correlation coefficient between this sampling image and the latest determined effective sampling image.

[0057] Optionally, the traversal module 204 is specifically configured to perform a cropping operation on the scaled effective sampling image with the optical axis of the scaled effective sampling image as the center to obtain a cropped effective sampling image with a size of the pixel array size; perform a correlation operation on this sampling image and the cropped effective sampling image.

[0058] Optionally, the imaging module 206 is specifically configured to perform a stitching operation on each determined effective sampling image to obtain a stitched effective sampling image; perform speckle autocorrelation imaging of the object to be imaged using the stitched effective sampling image.

[0059] Optionally, the imaging module 206 is specifically configured to perform different degrees of scaling on each determined effective sampling image so that the scaled effective sampling images are at the same imaging magnification ratio; perform a stitching operation on the scaled effective sampling images.

[0060] This specification also provides a computer-readable storage medium storing a computer program that can be used to execute the above Figure 1 Speckle autocorrelation imaging method under thermal light illumination provided.

[0061] This specification also providesFigure 4 Schematic structural diagram of the electronic device shown. As Figure 4 described, at the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include other hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the above Figure 1 speckle autocorrelation imaging method under thermo-optical illumination described. Of course, in addition to the software implementation method, this specification does not exclude other implementation methods, such as logical devices or a combination of software and hardware, etc. That is to say, the execution subject of the following processing flow is not limited to each logical unit, and can also be hardware or logical devices.

[0062] Improvements to a technology can be clearly distinguished as either hardware improvements (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software improvements (improvements to method flows). However, with the development of technology, many improvements to method flows today can be regarded as direct improvements to hardware circuit structures. Almost all designers obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement to a method flow cannot be implemented with a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. The designer can program by himself to "integrate" a digital system on a single PLD, without having to ask a chip manufacturer to design and fabricate a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly implemented using "logic compiler" software, which is similar to the software compiler used in program development and writing. The original code before compilation also has to be written in a specific programming language, which is called a hardware description language (HDL), and there is not only one kind of HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones currently are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also be aware that as long as the method flow is slightly logically programmed with the above-mentioned several hardware description languages and programmed into the integrated circuit, it is easy to obtain the hardware circuit that implements the logical method flow.

[0063] The controller can be implemented in any suitable manner. For example, the controller can take the form of, for example, a microprocessor or a processor and a computer-readable medium storing computer-readable program code (such as software or firmware) executable by the (micro)processor, logic gates, switches, an application specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of the controller include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in the form of pure computer-readable program code, it is entirely possible to make the controller implement the same function in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be regarded as the structures within the hardware component. Or even, the devices for implementing various functions can be regarded as either software modules for implementing the method or structures within the hardware component.

[0064] The systems, devices, modules, or units illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0065] For the convenience of description, when describing the above devices, they are described separately as various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0066] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0067] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, as well as the combination of flows and / or blocks in the flowchart and / or block diagram. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0068] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0069] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0070] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and a memory.

[0071] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0072] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0073] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0074] It should be understood by those skilled in the art that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0075] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0076] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the differences between each embodiment and other embodiments are emphasized. In particular, for system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and reference can be made to the relevant parts of the method embodiments for the relevant content.

[0077] The above description is only for the embodiments of this specification and is not intended to limit this specification. For those skilled in the art, various modifications and changes can be made to this specification. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this specification shall be included within the scope of the claims of this application.

Claims

1. A speckle autocorrelation imaging method under thermal light illumination, characterized in that: include: Under the condition that other factors remain unchanged and the designated factor changes regularly, sampling images of the object to be imaged under different designated factors are collected, and the sampling images are sorted in the order in which the sampling images are collected to obtain a sampling image set, wherein the designated factor is the central wavelength of the filter or the distance between the sampling surface and the scattering medium; Determine the first sampling graph in the sampling graph set as a valid sampling graph; Traversing the sampling graph set in order, determining, for each sampling graph, a correlation coefficient between the sampling graph and a most recently determined valid sampling graph, and determining the sampling graph as a valid sampling graph when the correlation coefficient is not greater than a specified threshold; The determined effective sampling images are used to complete the speckle autocorrelation imaging of the object to be imaged.

2. The method according to claim 1, characterized in that The specified factor is the center wavelength of the filter; Under the condition that other factors remain unchanged and the specified factors change regularly, the sampling diagram of the object to be imaged under different specified factors is collected, including: Under the condition that other factors remain unchanged and the central wavelength of the filter is continuously increased according to a specified size, a sampling image of the object to be imaged collected at each central wavelength is obtained.

3. The method according to claim 1, characterized in that The specified factor is the distance between the sampling surface and the scattering medium; Under the condition that other factors remain unchanged and the specified factors change regularly, the sampling diagram of the object to be imaged under different specified factors is collected, including: Under the condition that other factors remain unchanged and the distance between the sampling surface and the scattering medium is continuously increased according to a specified length, a sampling image of the object to be imaged collected at each distance is obtained.

4. The method according to claim 3, characterized in that Determine the correlation coefficient between the sampling map and the most recently determined valid sampling map, specifically including: Determine the pixel array size of each sampling image, wherein the pixel array size of each sampling image is the same; According to the distance between the sampling plane and the scattering medium when the sampling plane is collected, the distance between the sampling plane and the scattering medium when the latest effective sampling plane is collected, and the pixel array size, scaling the latest effective sampling plane to obtain a scaled effective sampling plane; A correlation operation is performed on the sampling graph and the scaled effective sampling graph to obtain a correlation coefficient between the sampling graph and the most recently determined effective sampling graph.

5. The method according to claim 4, characterized in that The sampling graph and the scaled effective sampling graph are subjected to correlation operation, specifically comprising: Taking the optical axis of the scaled effective sampling image as the center, performing a cropping operation on the scaled effective sampling image to obtain a cropped effective sampling image having a size equal to the size of the pixel array; A correlation operation is performed on the sampling graph and the cropped effective sampling graph.

6. The method according to claim 1, characterized in that The determined effective sampling images are used to complete the speckle autocorrelation imaging of the object to be imaged, specifically including: Performing a splicing operation on each determined valid sampling graph to obtain a spliced ​​valid sampling graph; The spliced ​​effective sampling image is used to complete the speckle autocorrelation imaging of the object to be imaged.

7. The method according to claim 6, characterized in that The determined valid sampling graphs are spliced, specifically including: Scaling each determined valid sampling image to different degrees, so that each scaled valid sampling image is at the same imaging magnification ratio; The scaled valid sampling images are stitched together.

8. A speckle autocorrelation imaging device under thermal light illumination, characterized in that: include: The acquisition module is used to acquire sampling images of the object to be imaged under different specified factors under the condition that other factors remain unchanged and the specified factors change regularly, and sort the sampling images in the order in which they are acquired to obtain a sampling image set; A determination module, used for determining the first sampling graph in the sampling graph set as a valid sampling graph; A traversal module, used for traversing the sampling graph set in order, determining, for each sampling graph, a correlation coefficient between the sampling graph and a newly determined valid sampling graph, and determining the sampling graph as a valid sampling graph when the correlation coefficient is not greater than a specified threshold; An imaging module is used to complete the speckle autocorrelation imaging of the object to be imaged by using the determined effective sampling images.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of claims 1 to 7 is implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method described in any one of claims 1 to 7 is implemented.

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