WO2023226218A1

WO2023226218A1 - Axisymmetric optical imaging parallel simulation method and apparatus

Info

Publication number: WO2023226218A1
Application number: PCT/CN2022/114994
Authority: WO
Inventors: 季向阳; 魏恒璐; 杨楚皙; 连晓聪
Original assignee: 清华大学
Priority date: 2022-05-26
Filing date: 2022-08-26
Publication date: 2023-11-30
Also published as: CN114820581B; CN114820581A

Abstract

The present application relates to the technical field of optical imaging and image processing, and in particular, to an axisymmetric optical imaging parallel simulation method and apparatus. The method comprises: acquiring multispectral data of a multispectral scene; obtaining the distribution of the radiance of light at different wavelengths according to the multispectral data; and on the basis of the distribution of the radiance of the light at different wavelengths, using radiance, illuminance and/or an image at a target wavelength to simulate an image on an image plane according to the effect of an axisymmetric lens on the light, so as to obtain the illuminance of a spectrum at each wavelength on the image plane. Thus, the present application solves the technical problem in the related art of reduction in imaging accuracy caused by inconsistency with an actual image, distortion of a reference image or uncontrollable quality because the degree of approximation between the obtained image and the image generated by actual photographing cannot be controlled.

Description

Axisymmetric optical imaging parallel simulation method and device

Cross-references to related applications

This application is based on the Chinese patent application with application number 202210589502.7 and the filing date is May 26, 2022, and claims the priority of the Chinese patent application. The entire content of the Chinese patent application is hereby incorporated into this application as a reference.

Technical field

The present application relates to the technical fields of optical imaging and image processing, and in particular to an axisymmetric optical imaging parallel simulation method and device.

Background technique

In related technology, by using the image captured by a high-quality camera as an ideal reference and using the blurred reference image as an image, the user can directly obtain the voltage data with distortion and noise output by the sensor, and at the same time, the user can also obtain lossless The scene image and the noise-free image presented by the light passing through the lens on the sensor have become the current mainstream method of adding blurred analog images to high-quality images.

However, in the related art, since a reference image with blur added to a high-quality image is regarded as an image, it is impossible to control the resulting image to be close to the image produced by actual shooting, resulting in inconsistency with the actual imaging, distortion of the reference image, or uncontrollable quality. It reduces the accuracy of imaging and cannot meet the needs of axisymmetric optical imaging, which needs to be solved urgently.

Contents of the invention

This application is based on the inventor's understanding of the following issues:

As shown in Figure 1, an axially symmetric optical imaging system usually consists of three main modules: an optical lens, a sensor, and an image processing module. The optical lens maps the spectral information in the scene to the image plane, and the sensor is located on the image plane and transfers the light on the image plane. The signal is converted into a voltage signal, and the image processing module converts the voltage into an image suitable for human eyes. Since light passing through the lens will introduce distortion, and the voltage signal converted by the sensor also contains a lot of noise, the image processing module has an important impact on the quality of the final image.

In the process of designing image processing algorithms such as denoising and enhancement, a more ideal situation is to know the ideal image and the distorted image, especially for data-driven methods such as deep learning. Good comparison data can greatly improve the algorithm. performance. However, in the actual photographing process, the optical lens and the imaging sensor are inseparable, and the spectral data of the scene cannot be collected directly. The user can only directly obtain the voltage data output by the sensor with distortion and noise, but cannot obtain a lossless scene. Images and light passing through the lens appear on the sensor as a noise-free image.

Based on this, this application provides an axially symmetric optical imaging parallel simulation method, device, electronic equipment and storage medium to solve the problem of inconsistency between the image obtained in related technologies and the image produced by actual shooting, which results in inconsistency with the actual imaging. There are technical issues in which the reference image is distorted or has uncontrollable quality, thereby reducing the accuracy of imaging.

The first embodiment of the present application provides a parallel simulation method for axially symmetric optical imaging, which includes the following steps: obtaining multispectral data of a multispectral scene; obtaining the distribution of radiance of light at different wavelengths based on the multispectral data; based on The distribution of the radiance of the light at different wavelengths is simulated by using the radiance, illumination and/or image of the target wavelength on the image plane according to the effect of the axially symmetrical lens on the light to obtain the spectrum at each wavelength on the image plane. illumination on.

Optionally, in one embodiment of the present application, the use of the radiance, illumination and/or image of the target wavelength to simulate the image on the image plane according to the effect of the axisymmetric lens on the light includes: deriving the axisymmetric lens The magnification at the target wavelength, the actual image height corresponding to the ideal image height in a radius direction; using the ideal image height as the independent variable and the actual image height as the dependent variable, perform a 9th-order polynomial fitting to obtain the first approximate and calculate the radiance of the pixel in the single spectrum of the target wavelength according to the first fitting result.

Optionally, in one embodiment of the present application, the use of the radiance, illumination and/or image of the target wavelength to simulate the image on the image plane according to the effect of the axisymmetric lens on the light includes: deriving the axisymmetric lens The relative brightness of the actual image height in a radius direction; using the actual image height as the independent variable and the relative brightness as the dependent variable, perform a fourth-order polynomial fitting to obtain a second fitting result; according to the second fitting result Calculate the illumination of the pixel in a single spectrum of the target wavelength.

Optionally, in an embodiment of the present application, the calculation formula of the illumination may be:

Among them, (i,j) represents the coordinates of the pixel point in the Cartesian coordinate system, ρ(i,j) represents the radius of the point in the polar coordinate system, and L ^'λ (i,j) represents the distortion spectrum in (i, j) The radiance of point, T _λ represents the transmittance of wavelength λ, f/# is the f number of the lens, m _λ represents the magnification of the lens at wavelength λ, and R _λ (ρ) represents the fourth-order polynomial fitting.

Optionally, in one embodiment of the present application, the use of the radiance, illuminance and/or image of the target wavelength to simulate the image on the image plane according to the effect of the axially symmetric lens on the light includes: deriving the image on the image plane The point spread function of discrete points of the target wavelength or the point spread function on a radius; the scattered points at the same position after all points in the multispectral scene are acted upon by the point spread function are used as a sub-image on the image surface; one by one Calculate the image plane illumination of each pixel in each sub-image to obtain the image of the light of the target wavelength on the image plane.

A second embodiment of the present application provides an axially symmetric optical imaging parallel simulation device, including: an acquisition module for acquiring multispectral data of a multispectral scene; and a processing module for obtaining the radiance of light according to the multispectral data. Distribution at different wavelengths; a simulation module for simulating the effect of the radiance of the light on the image surface based on the distribution of the radiance of the light at different wavelengths, using the radiance, illumination and/or image of the target wavelength on the light according to the axis-symmetric lens image, and obtain the illumination of the spectrum at each wavelength on the image surface.

Optionally, in one embodiment of the present application, the simulation module includes: a first derivation unit for deriving the magnification factor of the axisymmetric lens at the target wavelength, the actual image height corresponding to the ideal image height in a radial direction Image height; a first fitting unit for performing 9th-order polynomial fitting with the ideal image height as an independent variable and the actual image height as a dependent variable to obtain a first fitting result; a first calculation unit for Calculate the radiance of the pixel in the single spectrum of the target wavelength according to the first fitting result.

Optionally, in one embodiment of the present application, the simulation module further includes: a second derivation unit, used to derive the relative brightness of the actual image height in a radial direction of the axisymmetric lens; a second fitting unit, Used to perform fourth-order polynomial fitting with the actual image height as the independent variable and the relative brightness as the dependent variable to obtain a second fitting result; a second calculation unit used to calculate the target according to the second fitting result The illumination of a pixel in a single spectrum of wavelengths.

Optionally, in one embodiment of the present application, the simulation module further includes: a third derivation unit, used to derive the point spread function of discrete points of the target wavelength on the image plane or the point spread function on a radius. ; The processing unit is used to treat all points in the multispectral scene as a sub-image on the image surface as scattered points at the same position after being acted upon by the point spread function; the simulation unit is used to calculate each sub-image one by one The image plane illumination of the pixel point is used to obtain the image of the light of the target wavelength on the image plane.

A third embodiment of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the program to implement Axisymmetric optical imaging parallel simulation method as described in the above embodiment.

A fourth embodiment of the present application provides a computer-readable storage medium. The computer-readable storage medium stores computer instructions. The computer instructions are used to cause the computer to perform the axially symmetric optical imaging parallelism described in the above embodiments. Simulation method.

Embodiments of the present application can obtain the distribution of the radiance of the camera's light at different wavelengths based on the multispectral data of the multispectral scene, and use the radiance, illumination and/or image of the target wavelength to simulate the effect of the axially symmetric lens on the light. The image on the surface is obtained to obtain the illumination of the spectrum at each wavelength on the image surface, making it closer to the image produced by actual shooting. In particular, the algorithm itself is highly parallel and is convenient for the CPU (Central Processing Unit). Multi-threading and GPU (Graphics Processing Unit, Graphics Processing Unit) acceleration are implemented, which effectively improves the accuracy and efficiency of imaging and effectively meets simulation needs. This solves the technical problem in related technologies that it is impossible to control the approximation between the image obtained and the image produced by actual shooting, resulting in inconsistency with the actual imaging, distortion of the reference image or uncontrollable quality, thereby reducing the accuracy of imaging.

Additional aspects and advantages of the application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application.

Description of the drawings

The above and/or additional aspects and advantages of the present application will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

Figure 1 is a schematic diagram of the principle of axially symmetric optical imaging in related technologies;

Figure 2 is a flow chart of a parallel simulation method for axially symmetric optical imaging provided according to an embodiment of the present application;

Figure 3 is a flow chart of parallel simulation of axially symmetric optical imaging according to a specific embodiment of the present application;

Figure 4 is a schematic diagram of PSF (Point Spread Function) sampling and interpolation according to a specific embodiment of the present application;

Figure 5 is a schematic diagram of sub-picture boundary filling according to a specific embodiment of the present application;

Figure 6 is a schematic diagram of simulating MCC (Macbeth Color Checker, Macbeth Color Checker) and converting multispectral images into sRGB (standard Red Green Blue, universal color standard) color space according to a specific embodiment of the present application;

Figure 7 is a schematic structural diagram of an axisymmetric optical imaging parallel simulation device according to an embodiment of the present application;

FIG. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed ways

The embodiments of the present application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present application, but should not be construed as limiting the present application.

The following describes the axially symmetric optical imaging parallel simulation method and device according to the embodiments of the present application with reference to the accompanying drawings. In view of the above-mentioned background technology center, the related technology cannot control the approximation between the obtained image and the image produced by actual shooting, resulting in inconsistency with the actual imaging, distortion of the reference image or uncontrollable quality, thus reducing the accuracy of imaging. , this application provides a parallel simulation method for axially symmetric optical imaging. In this method, embodiments of this application can obtain the distribution of the radiance of the camera's light at different wavelengths based on the multispectral data of the multispectral scene, and use the target The radiance, illumination and/or image of the wavelength simulate the image on the image plane based on the effect of the axially symmetric lens on the light, thereby obtaining the illumination of the spectrum at each wavelength on the image plane, making it closer to the image produced by actual shooting. In particular, the algorithm itself is highly parallel, which facilitates CPU multi-threading and GPU acceleration, thereby effectively improving the accuracy and efficiency of imaging and effectively meeting simulation needs. This solves the technical problem in related technologies that it is impossible to control the approximation between the image obtained and the image produced by actual shooting, resulting in inconsistency with the actual imaging, distortion of the reference image or uncontrollable quality, thereby reducing the accuracy of imaging.

Specifically, FIG. 2 is a schematic flowchart of an axisymmetric optical imaging parallel simulation method provided by an embodiment of the present application.

As shown in Figure 2, the parallel simulation method of axially symmetric optical imaging includes the following steps:

In step S201, multispectral data of the multispectral scene is obtained.

During actual execution, the embodiments of the present application can obtain multispectral data of a multispectral scene. For example, the multispectral data of a multispectral scene can be obtained through physics-based ray tracing or high-precision multispectral cameras or RGB image estimation, and can be obtained through The multispectral data of the multispectral scene is collected to obtain a lossless scene image and a noiseless image presented by the light passing through the lens on the sensor.

Furthermore, as shown in Figure 3, the embodiment of the present application can perform three-step operations of distortion, shading and blurring on each single spectrum of the multispectral scene. The specific steps will be described in detail below, thereby obtaining the corresponding single spectrum image. Due to the algorithm It is inherently highly parallelized, where each step can take advantage of CPU multi-threading or GPU acceleration.

In step S202, the distribution of the radiance of light at different wavelengths is obtained based on the multispectral data.

Specifically, embodiments of the present application can obtain the distribution of the radiance of the camera's light at different wavelengths based on the multispectral data of the multispectral scene. For visible light imaging, the wavelength range is usually 380nm ~ 720nm, thereby improving the efficiency of acquiring light. Radiance efficiency, reducing redundant operations.

In step S203, based on the distribution of the radiance of the light at different wavelengths, the radiance, illuminance and/or image of the target wavelength are used to simulate the image on the image plane according to the effect of the axially symmetric lens on the light, and the image at each wavelength is obtained. The illumination of the spectrum on the image plane.

It can be understood that the embodiments of the present application can obtain the distribution of the radiance of the camera's light at different wavelengths based on the multispectral data of the multispectral scene, and use the radiance, illumination and/or image of the target wavelength according to the axially symmetrical lens pair. The effect of light simulates the image on the image surface, thereby obtaining the illumination of the spectrum at each wavelength on the image surface, effectively improving the similarity between the simulation results and the actual image produced.

Further, in one embodiment of the present application, the radiance, illumination and/or image of the target wavelength are used to simulate the image on the image plane according to the effect of the axisymmetric lens on the light, including: deriving the image of the axisymmetric lens at the target wavelength. Magnification, the actual image height corresponding to the ideal image height in a radius direction; use the ideal image height as the independent variable and the actual image height as the dependent variable to perform 9th-order polynomial fitting to obtain the first fitting result; according to the first fitting result Calculate the radiance of a pixel in a single spectrum at the target wavelength.

As a possible implementation method, the embodiment of the present application can derive the magnification factor of the axisymmetric lens at the target wavelength for a given axisymmetric lens, such as using optical simulation software such as Zemax or CodeV to derive the magnification factor of the lens at the target wavelength. Magnification, the actual image height corresponding to the ideal image height in a radius direction, and use the ideal image height as the independent variable and the actual image height as the dependent variable to perform 9th-order polynomial fitting to obtain the first fitting result, then the center point is Origin, the radiance of the pixel in the single spectrum of the target wavelength under the Cartesian coordinate system. The Cartesian coordinates after distortion are consistent with those before distortion. The radiance of the distorted spectrum at the pixel is calculated through bilinear interpolation. Among them, the pixels are mutually Standalone, can be done via CPU multi-threading or GPU.

Further, in one embodiment of the present application, the radiance, illumination and/or image of the target wavelength are used to simulate the image on the image plane according to the effect of the axially symmetrical lens on the light, including: deriving the actual radiance of the axially symmetrical lens in one radial direction. The relative brightness of the image height; perform a fourth-order polynomial fitting with the actual image height as the independent variable and the relative brightness as the dependent variable to obtain the second fitting result; calculate the illuminance of the pixel in the single spectrum of the target wavelength based on the second fitting result .

In the actual execution process, the embodiments of the present application can derive the relative brightness of the actual image height of the axis-symmetric lens in a radial direction for a given axially symmetrical lens. For example, using optical simulation software such as Zemax or CodeV to derive the actual relative brightness of the lens in a radial direction. The relative brightness of the image height, and use the actual image height as the independent variable and the relative brightness as the dependent variable to perform a fourth-order polynomial fitting to obtain the second fitting result. The illumination of the pixel on the image surface after lens shading can be calculated according to the following Illumination formula calculation, the process of calculating illumination E ^λ (i,j) is independent of each other and can be completed by CPU multi-threading or GPU.

Among them, in one embodiment of the present application, the calculation formula of illumination is:

Further, in one embodiment of the present application, the radiance, illumination and/or image of the target wavelength are used to simulate the image on the image plane according to the effect of the axis-symmetric lens on the light, including: deriving discrete points of the target wavelength on the image plane point spread function or a point spread function on a radius; all points in the multispectral scene are scattered at the same position after being acted upon by the point spread function as a sub-image on the image surface; each sub-image is calculated one by one The image plane illumination of the pixel is used to obtain the image of the target wavelength of light on the image plane.

As a possible implementation method, the embodiment of the present application can use optical simulation software such as Zemax or CodeV to derive the PSF of discrete points of wavelength on the image surface for a given axisymmetric lens, where the points are uniformly sampled in polar coordinates. , or only export the PSF on one radius, and the PSF of other sampling points are calculated using rotation invariance, thereby mapping these PSFs to the multispectral scene, so that the distance between two adjacent points in the horizontal and vertical directions of each PSF is equal to the pixel The physical width of the point.

Furthermore, in this embodiment of the present application, for any pixel point in the multispectral scene, its PSF can be calculated using bilinear interpolation based on the four known PSFs of its neighbors, so that all points in the multispectral scene are processed by the PSF Scattered points at the same position are regarded as a sub-image on the image surface. Then the image surface illumination of each pixel point in each sub-image is calculated one by one, and all sub-images are merged into the actual illumination map on the image surface to obtain the target wavelength. The image of light on the image surface.

The working principle of the method in the embodiment of the present application will be described in detail below with a specific embodiment.

For example, assume that the number of horizontal pixels in the scene multispectrum is w, the number of vertical pixels is h, the physical width of the pixel is l, the center point of the spectrum is the origin of the coordinate axis, the x coordinate axis direction is to the right, and the y coordinate axis direction is to the right. Above; (i,j) represents the coordinates of the pixel point in the Cartesian coordinate system, ρ(i,j) represents the radius of the point in the polar coordinate system, and θ(i,j) represents the radius of the point in the polar coordinate system. Angle; L ^λ (i, j) represents the radiance of pixel point (i, j) in a single spectrum with wavelength λ. The detailed process of the three steps is as follows:

Step S1: Distortion of single spectrum.

Step S1.1: For a given axisymmetric lens, use optical simulation software such as Zemax or CodeV to derive the magnification m _λ of the lens at wavelength λ, the actual image height corresponding to the ideal image height in a radius direction, and use the ideal image The height/|m _λ |/l is used as the independent variable, and the actual image height/|m _λ |/l is the dependent variable for 9th-order polynomial fitting, which is recorded as D _λ (ρ).

Step S1.2: Taking the center point as the origin, the distorted rectangular coordinates of L ^λ (i, j) in the rectangular coordinate system are x = D _λ (ρ (i, j))·cos (θ (i, j)) , y=D _λ (ρ(i,j))·sin(θ(i,j)), the radiance of this point remains the same as before distortion, that is, L ^λ (i,j), during the above process of calculating distortion The pixels are independent of each other and can be completed through CPU multi-threading or GPU. Finally, the radiance of the distorted spectrum at point (i, j) is calculated through bilinear interpolation, which is recorded as L ^'λ (i, j). The above calculation The pixels in the process of L ^'λ (i, j) are independent of each other and can be completed by CPU multi-threading or GPU.

Step S2: Single spectrum shading.

For a given axisymmetric lens, use optical simulation software such as Zemax or CodeV to derive the relative brightness of the actual image height in one radius direction of the lens, and use the actual image height/|m _λ |/l as the independent variable and the relative brightness as the dependent variable. Perform a fourth-order polynomial fitting, denoted as R _λ (ρ). The illumination of point (i, j) on the image plane after lens shading is calculated according to the lighting formula. During the above process of calculating E ^λ (i, j), the illuminance between pixels are independent of each other and can be completed through CPU multi-threading or GPU. The lighting formula is as follows:

Step S3: Blurring of single spectrum.

Step S3.1: As shown in Figure 4, for a given axisymmetric lens, use optical simulation software such as Zemax or CodeV to derive the point spread function PSF of discrete points when the wavelength is λ on the image surface, where the points are uniform in polar coordinates Sampling; or only export the PSF on one radius, and the PSF of other sampling points are calculated using rotation invariance. These PSFs are mapped to the multispectral scene so that the distance between two adjacent points in the horizontal and vertical directions of each PSF is equal to l , denoted as P _ρ,θ , for any pixel point (i, j) in the multispectral scene, its PSF can be calculated using bilinear interpolation based on the four known PSFs of its neighbors, denoted as P _{ρ(i, j),θ(i,j)} .

Step S3.2: Assume that PSF has s sampling points in both the horizontal and vertical directions (s is an odd number), that is, a square of size s × s. Then a point in the multispectral scene becomes s × s points after the action of PSF. Scattered on the image surface, all scattered points in the multispectral scene at the same position after the action of PSF are regarded as a sub-image on the image surface, then the actual illumination on the image surface can be regarded as the s×s sub-image Superposition, the embodiment of this application first calculates each sub-image one by one, and finally merges all sub-images into the actual illumination image on the imaging surface. For the (m, n)th sub-image with wavelength λ

The calculation process of the illumination value of the (i,j)th point is as follows:

Among them, P _{ρ (i, j), θ (i, j)} represents the point spread function of any pixel, and E ^λ (i, j) represents the illumination of the pixel in a single spectrum of the target wavelength.

Step S3.3: As shown in Figure 5, place

The upper, lower, left, and right parts are filled with n-1 rows, sn rows, m-1 columns, and sm column 0 respectively, and the new subgraph after filling is obtained.

Superimpose s × s new sub-images to obtain the image of light with wavelength λ on the image surface in the multispectral scene.

Calculated as follows:

in,

Represents the (m, n)th subgraph with wavelength λ.

The above process of calculating the image plane illumination of each pixel in each sub-image is independent of each other and can be completed by CPU multi-threading or GPU.

Step S3.4: As shown in Figure 6, the multispectral image obtained above can be converted into the color space of interest according to the specific application. For the chromatic aberration correction problem in the sRGB color space, the multispectral image can first be converted into CIE- XYZ color space, and then convert from CIE-XYZ color space to sRGB color space, and use PBRT to perform ray tracing on the MCC 24 color card to obtain multispectral data of the multispectral scene. The wavelength range is 380nm ~ 720nm, and the wavelength interval is 10nm, thereby calculating the image of each spectrum passing through the double Gaussian lens

And convert the multispectral image to sRGB color space.

According to the parallel simulation method of axially symmetric optical imaging proposed by the embodiment of the present application, the distribution of the radiance of the camera's light at different wavelengths can be obtained based on the multispectral data of the multispectral scene, and the radiance, illumination and/or radiance of the target wavelength can be obtained. The image simulates the image on the image plane based on the effect of the axially symmetric lens on the light, thereby obtaining the illumination of the spectrum at each wavelength on the image plane, making it closer to the image produced by actual shooting, especially since the algorithm itself is highly parallel. It facilitates the implementation of CPU multi-threading and GPU acceleration, thereby effectively improving the accuracy and efficiency of imaging and effectively meeting simulation needs.

Next, the axially symmetric optical imaging parallel simulation device proposed according to the embodiment of the present application will be described with reference to the accompanying drawings.

Figure 7 is a block diagram of a parallel simulation device for axially symmetric optical imaging according to an embodiment of the present application.

As shown in FIG. 7 , the axis-symmetric optical imaging parallel simulation device 10 includes: an acquisition module 100 , a processing module 200 and a simulation module 300 .

Specifically, the acquisition module 100 is used to acquire multispectral data of a multispectral scene.

The processing module 200 is used to obtain the distribution of the radiance of light at different wavelengths based on the multispectral data.

The simulation module 300 is used to simulate the image on the image surface based on the distribution of the radiance of light at different wavelengths, using the radiance, illuminance and/or image of the target wavelength according to the effect of the axially symmetric lens on the light, to obtain the image at each wavelength. The illumination of the spectrum on the image plane.

Optionally, in one embodiment of the present application, the simulation module 300 includes: a first derivation unit, a first fitting unit and a first calculation unit.

Among them, the first derivation unit is used to derive the magnification of the axisymmetric lens at the target wavelength and the actual image height corresponding to the ideal image height in a radial direction.

The first fitting unit is used to perform 9th-order polynomial fitting with the ideal image height as the independent variable and the actual image height as the dependent variable to obtain the first fitting result.

The first calculation unit is used to calculate the radiance of the pixel in the single spectrum of the target wavelength according to the first fitting result.

Optionally, in one embodiment of the present application, the simulation module 300 further includes: a second derivation unit, a second fitting unit and a second calculation unit.

Among them, the second derivation unit is used to derive the relative brightness of the actual image height in one radial direction of the axis-symmetric lens.

The second fitting unit is used to perform fourth-order polynomial fitting using the actual image height as the independent variable and relative brightness as the dependent variable to obtain the second fitting result.

The second calculation unit is used to calculate the illumination of the pixel in the single spectrum of the target wavelength according to the second fitting result.

Optionally, in one embodiment of the present application, the calculation formula of illumination is:

Optionally, in one embodiment of the present application, the simulation module 300 further includes: a third derivation unit, a processing unit and a simulation unit.

Among them, the third derivation unit is used to derive the point spread function of discrete points of the target wavelength on the image surface or the point spread function on a radius.

The processing unit is used to convert all points in the multispectral scene into scattered points at the same position after being acted upon by the point spread function as a sub-image on the image surface.

The simulation unit is used to calculate the image plane illumination of each pixel in each sub-image one by one to obtain the image of the target wavelength of light on the image plane.

It should be noted that the foregoing explanation of the embodiment of the axisymmetric optical imaging parallel simulation method is also applicable to the axisymmetric optical imaging parallel simulation device of this embodiment, and will not be described again here.

According to the axially symmetric optical imaging parallel simulation device proposed in the embodiment of the present application, the distribution of the radiance of the camera's light at different wavelengths can be obtained based on the multispectral data of the multispectral scene, and the radiance, illumination and/or The image simulates the image on the image plane based on the effect of the axially symmetric lens on the light, thereby obtaining the illumination of the spectrum at each wavelength on the image plane, making it closer to the image produced by actual shooting, especially since the algorithm itself is highly parallel. It facilitates the implementation of CPU multi-threading and GPU acceleration, thereby effectively improving the accuracy and efficiency of imaging and effectively meeting simulation needs.

FIG. 8 is a schematic structural diagram of an electronic device provided by an embodiment of the present application. The electronic device may include:

Memory 801, processor 802, and a computer program stored on memory 801 and executable on processor 802.

When the processor 802 executes the program, it implements the axially symmetric optical imaging parallel simulation method provided in the above embodiment.

Furthermore, electronic equipment also includes:

Communication interface 803 is used for communication between the memory 801 and the processor 802.

Memory 801 is used to store computer programs that can run on the processor 802.

The memory 801 may include high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.

If the memory 801, the processor 802 and the communication interface 803 are implemented independently, the communication interface 803, the memory 801 and the processor 802 can be connected to each other through a bus and complete communication with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of presentation, only one thick line is used in Figure 8, but it does not mean that there is only one bus or one type of bus.

Optionally, in terms of specific implementation, if the memory 801, the processor 802 and the communication interface 803 are integrated on one chip, the memory 801, the processor 802 and the communication interface 803 can communicate with each other through the internal interface.

The processor 802 may be a central processing unit (Central Processing Unit, CPU for short), or an Application Specific Integrated Circuit (ASIC for short), or one or more processors configured to implement the embodiments of the present application. integrated circuit.

This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above parallel simulation method of axially symmetric optical imaging is implemented.

In the description of this specification, reference to the terms "one embodiment," "some embodiments," "an example," "specific examples," or "some examples" or the like means that specific features are described in connection with the embodiment or example. , structures, materials or features are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the specific features, structures, materials or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, those skilled in the art may combine and combine different embodiments or examples and features of different embodiments or examples described in this specification unless they are inconsistent with each other.

In addition, the terms “first” and “second” are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of indicated technical features. Therefore, features defined as "first" and "second" may explicitly or implicitly include at least one of these features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically limited.

Any process or method descriptions in flowcharts or otherwise described herein may be understood to represent modules, segments, or portions of code that include one or more executable instructions for implementing customized logical functions or steps of the process. , and the scope of the preferred embodiments of the present application includes additional implementations in which functions may be performed out of the order shown or discussed, including in a substantially simultaneous manner or in the reverse order, depending on the functionality involved, which shall It should be understood by those skilled in the technical field to which the embodiments of this application belong.

The logic and/or steps represented in the flowcharts or otherwise described herein, for example, may be considered a sequenced list of executable instructions for implementing the logical functions, and may be embodied in any computer-readable medium, For use by, or in combination with, instruction execution systems, devices or devices (such as computer-based systems, systems including processors or other systems that can fetch instructions from and execute instructions from the instruction execution system, device or device) or equipment. For the purposes of this specification, a "computer-readable medium" may be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer readable media include the following: electrical connections with one or N wires (electronic device), portable computer disk cartridges (magnetic device), random access memory (RAM), Read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium may even be paper or other suitable medium on which the program may be printed, as the paper or other medium may be optically scanned, for example, and subsequently edited, interpreted, or otherwise suitable as necessary. process to obtain the program electronically and then store it in computer memory.

It should be understood that various parts of the present application can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if it is implemented in hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: discrete logic gate circuits with logic functions for implementing data signals; Logic circuits, application specific integrated circuits with suitable combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.

Those of ordinary skill in the art can understand that all or part of the steps involved in implementing the methods of the above embodiments can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium. The program can be stored in a computer-readable storage medium. When executed, one of the steps of the method embodiment or a combination thereof is included.

In addition, each functional unit in various embodiments of the present application can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above integrated modules can be implemented in the form of hardware or software function modules. If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

The storage media mentioned above can be read-only memory, magnetic disks or optical disks, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above-mentioned embodiments are illustrative and cannot be understood as limitations of the present application. Those of ordinary skill in the art can make modifications to the above-mentioned embodiments within the scope of the present application. The embodiments are subject to changes, modifications, substitutions and variations.

Claims

A parallel simulation method for axially symmetric optical imaging, which is characterized by including the following steps:

Obtain multispectral data of multispectral scenes;

Obtain the distribution of the radiance of light at different wavelengths based on the multispectral data; and

Based on the distribution of the radiance of the light at different wavelengths, the radiance, illumination and/or image of the target wavelength are used to simulate the image on the image surface according to the effect of the axially symmetric lens on the light, and the spectrum at each wavelength is obtained on the image. illumination on the surface.
The method according to claim 1, characterized in that the use of the radiance, illumination and/or image of the target wavelength to simulate the image on the image plane according to the effect of the axially symmetrical lens on the light includes:

Derive the magnification of the axisymmetric lens at the target wavelength and the actual image height corresponding to the ideal image height in a radial direction;

Perform 9th order polynomial fitting with the ideal image height as the independent variable and the actual image height as the dependent variable to obtain the first fitting result;

Calculate the radiance of the pixel in the single spectrum of the target wavelength according to the first fitting result.
The method according to claim 1, characterized in that the use of the radiance, illumination and/or image of the target wavelength to simulate the image on the image plane according to the effect of the axially symmetrical lens on the light includes:

Derive the relative brightness of the actual image height in one radial direction of the axisymmetric lens;

Perform fourth-order polynomial fitting with the actual image height as the independent variable and the relative brightness as the dependent variable to obtain a second fitting result;

Calculate the illumination of the pixel in a single spectrum of the target wavelength according to the second fitting result.
The method according to claim 3, characterized in that the calculation formula of the illumination is:

Among them, (i,j) represents the coordinates of the pixel point in the Cartesian coordinate system, ρ(i,j) represents the radius of the point in the polar coordinate system, and L 'λ (i,j) represents the distortion spectrum in (i, j) The radiance of point, T λ represents the transmittance of wavelength λ, f/# is the fnumber of the lens, m λ represents the magnification of the lens at wavelength λ, and R λ (ρ) represents the fourth-order polynomial fitting.
The method according to claim 1, characterized in that the use of the radiance, illumination and/or image of the target wavelength to simulate the image on the image plane according to the effect of the axially symmetrical lens on the light includes:

Derive the point spread function of discrete points of the target wavelength on the image surface or the point spread function of a radius;

The scattered points at the same position after all points in the multispectral scene are acted upon by the point spread function are used as a sub-image on the image surface;

Calculate the image plane illumination of each pixel point in each sub-image one by one to obtain the image of the light of the target wavelength on the image plane.
An axisymmetric optical imaging parallel simulation device, which is characterized by including:

The acquisition module is used to acquire multispectral data of multispectral scenes;

A processing module configured to obtain the distribution of the radiance of light at different wavelengths based on the multispectral data; and

A simulation module for simulating the image on the image plane based on the distribution of the radiance of the light at different wavelengths, using the radiance, illuminance and/or image of the target wavelength according to the effect of the axially symmetric lens on the light, to obtain each wavelength The illumination of the spectrum below on the image plane.
The device according to claim 6, characterized in that the simulation module includes:

The first derivation unit is used to derive the magnification of the axisymmetric lens at the target wavelength and the actual image height corresponding to the ideal image height in a radial direction;

The first fitting unit is used to perform 9th order polynomial fitting with the ideal image height as the independent variable and the actual image height as the dependent variable to obtain the first fitting result;

A first calculation unit configured to calculate the radiance of pixels in a single spectrum of the target wavelength according to the first fitting result.
The device according to claim 6, characterized in that the simulation module further includes:

The second derivation unit is used to derive the relative brightness of the actual image height in one radial direction of the axisymmetric lens;

The second fitting unit is used to perform fourth-order polynomial fitting using the actual image height as the independent variable and the relative brightness as the dependent variable to obtain a second fitting result;

The second calculation unit is used to calculate the illumination of the pixel in a single spectrum of the target wavelength according to the second fitting result.
An electronic device, characterized in that it includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the method of claim 1 -Axisymmetric optical imaging parallel simulation method according to any one of 5.
A computer-readable storage medium with a computer program stored thereon, characterized in that the program is executed by a processor to implement the parallel simulation method of axially symmetric optical imaging according to any one of claims 1-5.