An imaging attenuation simulation method, system, medium and computer device of a camera module

Abstract

The application discloses a kind of imaging attenuation simulation method, system, medium and computer equipment of camera module, comprising: constructing simulation camera module;Obtain the first attenuation data corresponding to the optical lens in simulation camera module, and the second attenuation data corresponding to the image sensor in simulation camera module;Select several spatial frequency points from the imaging spatial frequency range of simulation camera module;Determine the sampling attenuation data of several spatial frequency points respectively;According to the sampling attenuation data of several spatial frequency points respectively, first attenuation data, second attenuation data, determine the joint imaging attenuation data of several spatial frequency points respectively;According to the joint imaging attenuation data of all spatial frequency points, construct imaging attenuation characteristic curve;Imaging attenuation characteristic curve is used to analyze the imaging performance of simulation camera module.

Description

Technical Field

[0001] This invention relates to the field of optical imaging technology, and in particular to an imaging attenuation simulation method, system, medium, and computer equipment for a camera module. Background Technology

[0002] In the research and development and production of optical imaging modules such as mobile phone cameras and automotive imaging, the industry generally uses MTF (Modulation Transfer Function) and SFR (Spatial Frequency Response) to evaluate the frequency domain attenuation characteristics of imaging systems.

[0003] Traditional solutions rely on the theoretical MTF curve of the optical lens to evaluate imaging performance. They only refer to the optical attenuation characteristics of the optical lens itself and completely ignore the attenuation effect caused by the pixel sampling of the image sensor. This results in a large error between the theoretical optical MTF and the measured SFR, which can be as high as 15% to 20%. This makes it impossible to accurately reflect the true imaging level of the module and to support the precise optimization of the module structure and optical parameters.

[0004] To obtain accurate SFR attenuation data, physical imaging measurements are performed using the ISO 12233 test chart. However, this approach requires setting up a dedicated testing environment, capturing standard target images, and performing complex post-processing calculations. This not only results in a cumbersome testing process and high hardware costs, but also limits verification to after the module prototype is formed. It prevents the simulation and prediction of frequency domain attenuation characteristics from being completed in the early design phase, significantly lengthening the module's R&D iteration cycle and hindering the efficient and precise design and development of imaging modules.

[0005] Therefore, how to accurately predict the actual imaging performance of the camera module during the design phase is a technical problem that needs to be solved. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention provides a method, system, medium, and computer equipment for simulating the imaging attenuation of a camera module. By integrating end-to-end attenuation factors such as lens optical attenuation, sensor attenuation, and phase shift sampling attenuation in the simulated camera module, the constructed imaging attenuation characteristic curve can effectively match the actual measured SFR imaging performance of the camera module, and the actual imaging performance of the camera module can be accurately predicted during the design stage.

[0007] To address the aforementioned technical problems, a first aspect of the present invention discloses an imaging attenuation simulation method for a camera module, the method comprising: Construct a simulation camera module; Obtain first attenuation data corresponding to the optical lens in the simulated camera module, and second attenuation data corresponding to the image sensor in the simulated camera module; Select several spatial frequency points within the imaging spatial frequency range of the simulated camera module; Determine the sampling attenuation data for each of the aforementioned spatial frequency points; Based on the sampling attenuation data of each of the aforementioned spatial frequency points, the first attenuation data, and the second attenuation data, the joint imaging attenuation data of each of the aforementioned spatial frequency points is determined; An imaging attenuation characteristic curve is constructed based on the joint imaging attenuation data of all spatial frequency points; the imaging attenuation characteristic curve is used to analyze the imaging performance of the simulated camera module.

[0008] In the above scheme, the imaging spatial frequency range is [0, ... f NY ];in, f NY This indicates the Nyquist frequency.

[0009] In the above scheme, determining the sampling attenuation data for each of the plurality of spatial frequency points specifically includes: For each spatial frequency point, multiple phase offset sampling points corresponding to the spatial frequency point are obtained; wherein, the phase offset point is a sampling point formed by changing the relative position between the pixel in the image sensor and the sinusoidal brightness waveform relative to the spatial frequency point. For each phase offset sampling point, the sinusoidal brightness distribution value within each pixel is integrated using the integral calculation formula to obtain the pixel-level brightness data of each phase offset sampling point; Based on the pixel-level brightness data of each phase offset sampling point, the sampling attenuation data of the phase offset sampling point is obtained; The sampling attenuation data of the spatial frequency point is determined based on the sampling attenuation data of all phase offset sampling points.

[0010] In the above scheme, the integral calculation formula is as follows:

[0011] in, This represents the attenuation data of the nth pixel; This represents the start and end coordinates of the edge at the nth pixel. , Represents the pixel phase change coefficient. Indicates the starting pixel position; This represents the sinusoidal brightness distribution value within the nth pixel.

[0012] In the above scheme, obtaining the sampling attenuation data of the phase offset sampling point based on the pixel-level brightness data of each phase offset sampling point specifically includes: The maximum and minimum brightness values ​​are determined from the pixel-level brightness data in the phase offset sampling points; Substitute the maximum brightness value and the minimum brightness value into the attenuation calculation formula. The modulation transfer function (MTF) value corresponding to the phase offset sampling point is calculated; where, This represents the MTF value corresponding to the phase offset sampling point; Indicates the maximum brightness value; Indicates the minimum brightness value; The MTF value corresponding to the phase offset sampling point is used as the sampling attenuation data of the phase offset sampling point.

[0013] In the above scheme, calculating the sampling attenuation data of the spatial frequency point based on the sampling attenuation data of all phase offset sampling points specifically includes: Take the average value of the sampling attenuation data corresponding to all phase offset sampling points; The average value is converted into a percentage to obtain the sampling attenuation data of the spatial frequency point.

[0014] In the above scheme, determining the joint imaging attenuation data for each of the plurality of spatial frequency points based on their respective sampling attenuation data, the first attenuation data, and the second attenuation data specifically includes: For each spatial frequency point, the sampled attenuation data of the spatial frequency point, the first attenuation data, and the second attenuation data are multiplied together to obtain the joint imaging attenuation data of the spatial frequency point.

[0015] A second aspect of the present invention discloses an imaging attenuation simulation system for a camera module, the system comprising: Build modules are used to construct simulation camera modules; The acquisition module is used to acquire first attenuation data corresponding to the optical lens in the simulated camera module, and second attenuation data corresponding to the image sensor in the simulated camera module; The selection module is used to select several spatial frequency points from the imaging spatial frequency range of the simulated camera module; The first determining module is used to determine the sampling attenuation data of each of the plurality of spatial frequency points; The second determining module is used to determine the joint imaging attenuation data of the plurality of spatial frequency points based on the sampling attenuation data of the plurality of spatial frequency points, the first attenuation data, and the second attenuation data. A construction module is used to construct an imaging attenuation characteristic curve based on the joint imaging attenuation data of all spatial frequency points; the imaging attenuation characteristic curve is used to analyze the imaging performance of the simulated camera module.

[0016] A third aspect of the present invention discloses a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the above-described method.

[0017] A fourth aspect of the present invention discloses a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described method.

[0018] Through one or more technical solutions of the present invention, the present invention has the following beneficial effects or advantages: This invention provides a method, system, medium, and computer equipment for simulating imaging attenuation in camera modules. By constructing a simulated camera module, it acquires lens optical attenuation data and sensor attenuation data, and fuses this data with phase sampling attenuation data at various spatial frequency points to obtain end-to-end joint imaging attenuation data, thereby constructing an imaging attenuation characteristic curve. Because it fully considers the multiple attenuation effects caused by optical loss, sensor sampling loss, and phase shift, it effectively compensates for the problems of large deviations and weak correlations between traditional single optical MTF evaluation and measured SFR. This allows the simulated curve to closely match the actual imaging performance of the module's measured SFR, enabling accurate prediction of the camera module's actual imaging performance during the design phase. This invention relies entirely on simulation modeling for performance prediction, eliminating the need for physical prototypes and standard test cards. It can quickly complete the frequency domain attenuation characteristic analysis of the module, effectively reducing R&D testing costs and shortening product iteration cycles. It provides reliable data support for the refined optimization design of the optical and structural parameters of camera modules, improving the overall imaging performance and R&D efficiency of the modules.

[0019] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0020] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1A flowchart of an imaging attenuation simulation method for a camera module according to an embodiment of the present invention is shown; Figure 2A A schematic diagram showing the relative positions between the pixel array and the waveform in a reference state with no phase shift according to an embodiment of the present invention is illustrated. Figure 2B A schematic diagram showing the relative positions between the pixel array and the waveform under a sampling state with a phase offset of π / 4, according to an embodiment of the present invention; Figure 2C A schematic diagram showing the relative positions between the pixel array and the waveform under a sampling state with a phase offset of π / 8, according to an embodiment of the present invention; Figure 3 A schematic diagram of the imaging attenuation characteristic curve of a camera module according to an embodiment of the present invention is shown; Figure 4 A schematic diagram of an imaging attenuation simulation system for a camera module according to an embodiment of the present invention is shown. Detailed Implementation

[0021] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0022] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0023] In a first aspect, embodiments of the present invention provide an imaging attenuation simulation method for a camera module. This method is used to simulate and construct the imaging attenuation characteristic curve of the entire link during the design stage of the camera module, thereby predicting the real imaging performance of the camera module and replacing the traditional physical testing and verification method of finished products.

[0024] See Figure 1 This is a flowchart of an imaging attenuation simulation method for a camera module according to an embodiment of the present invention, which includes the following steps: S101, construct a simulation camera module.

[0025] In this embodiment, a simulated camera module is constructed using simulation software to achieve integrated modeling and numerical simulation of the optical lens, image sensor, and back-end processing.

[0026] Specifically, the simulated camera module includes at least: an optical lens and an image sensor, arranged sequentially along the actual imaging optical path.

[0027] During the imaging process, the incident light is modulated by the optical lens and then projected onto the target surface of the image sensor. The image sensor performs pixel sampling and photoelectric conversion to generate an imaging electrical signal, which is finally received by the digital processor and processed in the backend imaging data.

[0028] Specifically, in the process of building the simulated camera module, the lens optical parameters, sensor pixel structure parameters, and photoelectric parameters of related devices are entered into the simulation software to complete the parametric construction of the simulated camera module.

[0029] S102, obtain the first attenuation data corresponding to the optical lens in the simulated camera module, and the second attenuation data corresponding to the image sensor in the simulated camera module.

[0030] The first attenuation data refers to the optical imaging attenuation data of the optical lens itself, for example: It represents the attenuation retention ratio of the incident imaging signal produced by the optical lens at different spatial frequencies. The higher the value, the smaller the attenuation. It is obtained by optical modeling and calculation through lens optical structure parameters (such as lens curvature, focal length, lens material, light aperture, etc.).

[0031] The second attenuation data is the attenuation data of the image sensor itself, for example... The value represents the proportion of image attenuation retention caused by the photoelectric response characteristics of the image sensor. The higher the value, the smaller the attenuation. It can be obtained directly from the sensor's factory calibration performance parameters, or it can be obtained through actual measurement, calibration and simulation by building a test platform.

[0032] S103, select several spatial frequency points from the imaging spatial frequency range of the simulation camera module.

[0033] In this embodiment, the imaging spatial frequency range of the simulated camera module refers to the complete spatial frequency range in which the simulated camera module can effectively respond to and transmit imaging signals.

[0034] The lower limit of the imaging spatial frequency range is zero frequency, and the upper limit is the Nyquist frequency, with units of lp / mm (line pairs / millimeter), specifically: [0, f NY ],in, f NY This indicates the Nyquist frequency.

[0035] Nyquist frequency f NYIt is the highest theoretical spatial frequency that an image sensor can resolve based on the pixel sampling principle.

[0036] Nyquist frequency f NY The calculation method is as follows:

[0037] in, p The pixel pitch of the image sensor is expressed in mm; 1 represents one line pair (1lp).

[0038] Furthermore, when selecting several spatial frequency points, they can be selected within the imaging spatial frequency range using methods such as preset frequency step size, random selection, or industry-standard characteristic frequency points. For example, the selected spatial frequency points are: 0, f NY / 2, f NY / 3, f NY / 4.

[0039] S104, determine the sampling attenuation data for several spatial frequency points.

[0040] In this embodiment, the sampling attenuation data for each spatial frequency point is determined as follows: For each spatial frequency point, multiple phase offset sampling points are obtained; For each phase offset sampling point, the sinusoidal brightness distribution value in each pixel is integrated using the integral calculation formula to obtain the pixel-level brightness data in the phase offset sampling point. Based on the pixel-level brightness data of each phase offset sampling point, the sampling attenuation data of the phase offset sampling point is obtained; Based on the sampling attenuation data of all phase offset sampling points, determine the sampling attenuation data of the spatial frequency points.

[0041] Specifically, different spatial frequencies correspond to standard black and white alternating striped targets with different densities; after the rectangular bright and dark stripes are imaged by the optical lens, the edges of the bright and dark areas are softened by optical properties, and finally form a periodic brightness waveform with sinusoidal fluctuations at the image plane. Therefore, each spatial frequency point corresponds to a periodic brightness waveform with sinusoidal fluctuations (i.e., a sinusoidal brightness waveform).

[0042] Furthermore, the pixel array of the image sensor samples the sinusoidal brightness waveform output by the optical lens.

[0043] In the initial state when the pixel array of the image sensor samples the sinusoidal brightness waveform output by the optical lens, there is no phase shift, which can be called zero phase shift. At this time, the edges of individual pixels in the pixel array of the image sensor are aligned with the peaks or troughs of the waveform.

[0044] Based on spatial frequency points ( f NY For example, see / 2). Figure 2A This is a schematic diagram showing the relative positions between the pixel array and the waveform under a reference state with no phase shift.

[0045] exist Figure 2A middle: The first pixel: pix1, with its left and right edges aligned to 0 and π / 2 respectively.

[0046] The second pixel: pix2, with its left and right edges aligned to π / 2 and π, respectively.

[0047] The third pixel: pix3, with its left and right edges aligned to π and 3π / 2 respectively.

[0048] The 4th pixel: pix4, with its left and right edges aligned to 3π / 2 and 2π respectively.

[0049] The same principle applies to other pixels.

[0050] Of course, the spatial relative position between the image sensor pixel array and the sinusoidal brightness waveform can be adjusted as a whole, changing the corresponding arrangement of the pixel photosensitive area with the peaks and troughs of the waveform, thus forming a phase shift.

[0051] Among them, the phase offset point is the sampling point formed by changing the relative position between the pixel in the image sensor and the sinusoidal brightness waveform relative to the spatial frequency point of the sinusoidal brightness waveform.

[0052] In addition to zero phase offset, other typical phase offset sampling points can also be set.

[0053] For example, a sampling point with a phase offset of π / 4, that is: the center of a single pixel aligned with the peak or trough of a periodic waveform.

[0054] Based on spatial frequency points ( f NY For example, / 2). See Figure 2B This is a schematic diagram showing the relative positions between the pixel array and the waveform under a sampling state with a phase offset of π / 4.

[0055] exist Figure 2B middle: The first pixel: pix1, with its left and right edges aligned to -π / 4 and π / 4 respectively.

[0056] The second pixel: pix2, with its left and right edges aligned to π / 4 and 3π / 4 respectively.

[0057] The third pixel: pix3, with its left and right edges aligned to 3π / 4 and 5π / 4 respectively.

[0058] The 4th pixel: pix4, with its left and right edges aligned to 5π / 4 and 7π / 4 respectively.

[0059] The same principle applies to other pixels.

[0060] For example, a sampling point with a phase offset of π / 8, that is, a π / 8 phase of a periodic waveform aligned to the edge of a single pixel. Therefore, at a given spatial frequency point, there are multiple phase offset sampling points, including a zero-phase reference.

[0061] Based on spatial frequency points ( f NY For example, / 2). See Figure 2C This is a schematic diagram showing the relative positions between the pixel array and the waveform under a sampling state with a phase offset of π / 8.

[0062] exist Figure 2C middle: The first pixel: pix1, with its left and right edges aligned to -3π / 8 and π / 8 respectively.

[0063] The second pixel: pix2, with its left and right edges aligned to π / 8 and 5π / 8 respectively.

[0064] The third pixel: pix3, with its left and right edges aligned to 5π / 8 and 9π / 8 respectively.

[0065] The 4th pixel: pix4, with its left and right edges aligned to 9π / 8 and 13π / 8 respectively.

[0066] The same principle applies to other pixels.

[0067] In this way, multiple phase offset points corresponding to each spatial frequency point can be obtained, including zero phase offset.

[0068] For each phase-off sampling point, each pixel in the pixel array of the image sensor has a sinusoidal brightness distribution value. Therefore, by integrating the sinusoidal brightness distribution value in each pixel using an integral calculation formula, the pixel-level brightness data of each phase-off sampling point can be obtained.

[0069] The integral calculation formula is as follows:

[0070] in, This represents the attenuation data of the nth pixel; This represents the start and end coordinates of the edge at the nth pixel. , Represents the pixel phase change coefficient, a dimensionless coefficient derived from spatial frequency points, used to define the phase span of a single pixel. Indicates the starting pixel position; This represents the sinusoidal brightness distribution value within the nth pixel.

[0071] In this embodiment, pixel phase change coefficient k With spatial frequency f The following relationship must be satisfied:

[0072] If the spatial frequency point is 0, then k=0; if the spatial frequency point is... f NY If k = 1 / 2, then k = 1 / 2.

[0073] In this integral calculation formula, by integrating the continuously changing sinusoidal brightness distribution value within each pixel region by region, the average effective light intensity actually received by each pixel can be accurately calculated, thereby eliminating the error caused by local point values ​​and truly restoring the imaging light-sensing response characteristics of each pixel.

[0074] Furthermore, after obtaining the pixel-level brightness data of each phase-offset sampling point, the maximum and minimum brightness values ​​are determined from the pixel-level brightness data of each phase-offset sampling point; the maximum and minimum brightness values ​​are then substituted into the attenuation calculation formula to calculate the MTF (Modulation Transfer Function) value corresponding to the phase-offset sampling point.

[0075] The attenuation calculation formula is as follows:

[0076] in, This represents the MTF value corresponding to the phase offset sampling point; Indicates the maximum brightness value; This indicates the minimum brightness value.

[0077] The MTF value corresponding to the phase offset sampling point is used as the sampling attenuation data of the phase offset sampling point.

[0078] Furthermore, based on the sampling attenuation data of all phase offset sampling points, the sampling attenuation data of the spatial frequency points are calculated.

[0079] During the calculation, the average value of the sampling attenuation data corresponding to all phase offset sampling points is taken; the average value is converted into a percentage to obtain the sampling attenuation data of the spatial frequency points.

[0080] Based on spatial frequency points ( f NY For example, see Table 1 for / 2).

[0081] Table 1

[0082] Table 1 lists the spatial frequency points ( f NY The sampling attenuation data (MTF value) corresponding to each phase offset sampling point under / 2) is calculated using the above attenuation calculation formula. The average value of the sampling attenuation data corresponding to each phase offset sampling point is then converted to a percentage to obtain the spatial frequency point ( f NY / 2) The corresponding sampling attenuation data is 80% in Table 1.

[0083] As shown in Table 1, due to phase shift, at the spatial frequency point ( f NY / 2) Under these conditions, the MTF value will fluctuate between 0.6366 and 0.9003. By averaging the sampling attenuation data under multi-phase offset, the sampling error caused by the relative position offset between the pixel and the sine stripe can be offset, and stable and reliable sampling attenuation data at this spatial frequency point can be obtained.

[0084] Based on spatial frequency points ( f NY For example, see Table 2 for / 3).

[0085] Table 2

[0086] Table 2 lists the spatial frequency points ( f NY The sampling attenuation data (MTF value) corresponding to each phase offset sampling point under / 3) is calculated using the above attenuation calculation formula. The average value of the sampling attenuation data corresponding to each phase offset sampling point is then converted to a percentage to obtain the spatial frequency point ( f NY / 3) The corresponding sampling attenuation data is 91% in Table 2.

[0087] As shown in Table 2, due to phase shift, at the spatial frequency point ( f NY / 3) Under these conditions, the MTF value will fluctuate between 0.8270 and 0.9549. By averaging the sampling attenuation data under multi-phase offset, the sampling error caused by the relative position offset between the pixel and the sine stripe can be offset, and stable and reliable sampling attenuation data at this spatial frequency point can be obtained.

[0088] Based on spatial frequency points ( f NY For example, see Table 3 for / 4).

[0089] Table 3

[0090] Table 3 lists the spatial frequency points ( f NY The sampling attenuation data (MTF value) corresponding to each phase offset sampling point under ( / 4) is calculated using the above attenuation calculation formula. The average value of the sampling attenuation data corresponding to each phase offset sampling point is then converted to a percentage to obtain the spatial frequency point ( f NY / 4) The corresponding sampling attenuation data is 95% in Table 3.

[0091] As shown in Table 3, due to phase shift, at the spatial frequency point ( f NY Under / 4), the MTF value will fluctuate between 0.9003 and 0.9745. By averaging the sampling attenuation data under multi-phase offset, the sampling error caused by the relative position offset between the pixel and the sine stripe can be offset, and stable and reliable sampling attenuation data at this spatial frequency point can be obtained.

[0092] S105, based on the sampling attenuation data, first attenuation data, and second attenuation data of several spatial frequency points, determine the joint imaging attenuation data of several spatial frequency points.

[0093] Specifically, for each spatial frequency point, the sampled attenuation data, the first attenuation data, and the second attenuation data of the spatial frequency point are multiplied together, and the product is used as the joint imaging attenuation data of the spatial frequency point.

[0094] The calculation formula is as follows:

[0095] in, This represents the combined imaging attenuation data, used to characterize the attenuation retention ratio of the simulated camera module at different spatial frequencies. The higher the value, the smaller the attenuation. This indicates the first attenuation data; This indicates the second attenuation data; This represents the sampling attenuation data.

[0096] The sampling attenuation data of all spatial frequency points are processed in the above manner to obtain their respective joint imaging attenuation data.

[0097] S106. Based on the joint imaging attenuation data of all spatial frequency points, construct the imaging attenuation characteristic curve.

[0098] By fitting the combined imaging attenuation data of all spatial frequency points to a curve, the imaging attenuation characteristic curve can be obtained.

[0099] Imaging attenuation characteristic curves are used to analyze the imaging performance of simulated camera modules.

[0100] See Figure 3 This is a schematic diagram of the imaging attenuation characteristic curve.

[0101] in, Figure 3 The horizontal axis represents spatial frequency, and the vertical axis represents joint imaging attenuation data (displayed as a percentage).

[0102] After obtaining the joint imaging attenuation data for all spatial frequency points, a quadratic polynomial fitting operation is performed to obtain the imaging attenuation characteristic curve.

[0103] The imaging attenuation characteristic curve, using a quadratic polynomial equation as an example, is shown below: y = -0.72x² - 0.06x + 1.01 Where x represents the spatial frequency point and y represents the joint imaging attenuation data.

[0104] This curve illustrates the image attenuation pattern of the camera module at different spatial frequency points. For example, f NY / 4、 f NY / 3、 f NY The corresponding joint imaging attenuation data for each of / 2 are 95%, 91%, and 80%, respectively, indicating that the joint imaging attenuation data decreases with increasing spatial frequency. MTF mod The overall trend is downward, with the attenuation increasing from 5% to 9%, and then to 20%, indicating that the attenuation effect is gradually intensifying.

[0105] The joint imaging attenuation data in the imaging attenuation characteristic curve constructed by this invention has a relative error of less than 5% with the measured attenuation data at the corresponding spatial frequency points of the actual attenuation characteristic curve. This allows for direct analysis of imaging performance during the product design phase, obtaining analysis results, and adjusting the parameters of the simulated camera module based on these results. Once the adjustments are complete, the simulated camera module can be used as a reference for actual development, thereby saving on the processes of physical prototype manufacturing and standard test card testing, significantly reducing physical verification steps, and effectively shortening the overall development cycle of the camera module.

[0106] Of course, the imaging attenuation characteristic curve constructed by this invention can also be used for imaging evaluation of camera module products. For example, by comparing the actual attenuation characteristic curve of the camera module product with the imaging attenuation characteristic curve constructed by this invention, the imaging performance of the camera module product can be evaluated.

[0107] The above is the complete technical solution of this invention. By constructing a simulated camera module, lens optical attenuation data and sensor attenuation data are obtained, and combined with phase sampling attenuation data at various spatial frequency points to obtain end-to-end joint imaging attenuation data, thereby constructing an imaging attenuation characteristic curve. Because it fully considers the multiple attenuation effects caused by optical loss, sensor sampling loss, and phase shift, it effectively compensates for the problems of large deviation and weak correlation between traditional single optical MTF evaluation and measured SFR. This allows the simulated curve to closely match the actual imaging performance of the module's measured SFR, enabling accurate prediction of the camera module's actual imaging performance during the design phase. This invention relies entirely on simulation modeling to complete performance prediction, without relying on physical prototypes and standard test cards for actual testing. It can quickly complete the frequency domain attenuation characteristic analysis of the module, effectively reducing R&D testing costs and shortening product iteration cycles. It can provide reliable data support for the refined optimization design of the camera module's optical and structural parameters, improving the overall imaging performance and R&D efficiency of the module.

[0108] Secondly, based on the same inventive concept as the imaging attenuation simulation method for camera modules provided in the first aspect of the embodiments described above, the present invention also provides an imaging attenuation simulation system for camera modules, see below. Figure 4 The system includes: Module 401 is used to build a simulation camera module; wherein, the simulation camera module is built through simulation software to realize integrated modeling and numerical simulation of optical lens, image sensor and back-end processing.

[0109] The acquisition module 402 is used to acquire first attenuation data corresponding to the optical lens in the simulated camera module, and second attenuation data corresponding to the image sensor in the simulated camera module; The selection module 403 is used to select several spatial frequency points from the imaging spatial frequency range of the simulation camera module; The first determining module 404 is used to determine the sampling attenuation data of each of the plurality of spatial frequency points; The second determining module 405 is used to determine the joint imaging attenuation data of the plurality of spatial frequency points based on the sampling attenuation data of the plurality of spatial frequency points, the first attenuation data, and the second attenuation data. The construction module 406 is used to construct an imaging attenuation characteristic curve based on the joint imaging attenuation data of all spatial frequency points; the imaging attenuation characteristic curve is used to analyze the imaging performance of the simulation camera module.

[0110] It should be noted that the imaging attenuation simulation system for the camera module provided in the embodiments of the present invention has been described in detail in the method embodiments provided in the first aspect above. The specific implementation process can be referred to the method embodiments provided in the first aspect above, and will not be described in detail here.

[0111] Thirdly, based on the same inventive concept as the imaging attenuation simulation method for the camera module provided in the first aspect of the embodiments, the present invention also discloses a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0112] Fourthly, based on the same inventive concept as the imaging attenuation simulation method for the camera module provided in the first aspect embodiment, this embodiment of the invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of any of the methods described above.

[0113] Through one or more embodiments of the present invention, the present invention has the following beneficial effects or advantages: This invention provides a method, system, medium, and computer equipment for simulating imaging attenuation in camera modules. By constructing a simulated camera module, it acquires lens optical attenuation data and sensor attenuation data, and fuses this data with phase sampling attenuation data at various spatial frequency points to obtain end-to-end joint imaging attenuation data, thereby constructing an imaging attenuation characteristic curve. Because it fully considers the multiple attenuation effects caused by optical loss, sensor sampling loss, and phase shift, it effectively compensates for the problems of large deviations and weak correlations between traditional single optical MTF evaluation and measured SFR. This allows the simulated curve to closely match the actual imaging performance of the module's measured SFR, enabling accurate prediction of the camera module's actual imaging performance during the design phase. This invention relies entirely on simulation modeling for performance prediction, eliminating the need for physical prototypes and standard test cards. It can quickly complete the frequency domain attenuation characteristic analysis of the module, effectively reducing R&D testing costs and shortening product iteration cycles. It provides reliable data support for the refined optimization design of the optical and structural parameters of camera modules, improving the overall imaging performance and R&D efficiency of the modules.

[0114] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0115] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for simulating imaging attenuation in a camera module, characterized in that, The method includes: Construct a simulation camera module; Obtain first attenuation data corresponding to the optical lens in the simulated camera module, and second attenuation data corresponding to the image sensor in the simulated camera module; Select several spatial frequency points within the imaging spatial frequency range of the simulated camera module; Determine the sampling attenuation data for each of the aforementioned spatial frequency points; Based on the sampling attenuation data of each of the aforementioned spatial frequency points, the first attenuation data, and the second attenuation data, the joint imaging attenuation data of each of the aforementioned spatial frequency points is determined; An imaging attenuation characteristic curve is constructed based on the joint imaging attenuation data of all spatial frequency points; the imaging attenuation characteristic curve is used to analyze the imaging performance of the simulated camera module.

2. The method as described in claim 1, characterized in that, The imaging spatial frequency range is [0, ..., ... f NY ];in, f NY This indicates the Nyquist frequency.

3. The method as described in claim 1, characterized in that, The determination of the sampling attenuation data for each of the plurality of spatial frequency points specifically includes: For each spatial frequency point, multiple phase offset sampling points corresponding to the spatial frequency point are obtained; wherein, the phase offset point is a sampling point formed by changing the relative position between the pixel in the image sensor and the sinusoidal brightness waveform relative to the spatial frequency point. For each phase offset sampling point, the sinusoidal brightness distribution value within each pixel is integrated using the integral calculation formula to obtain the pixel-level brightness data of each phase offset sampling point; Based on the pixel-level brightness data of each phase offset sampling point, the sampling attenuation data of the phase offset sampling point is obtained; The sampling attenuation data of the spatial frequency point is determined based on the sampling attenuation data of all phase offset sampling points.

4. The method as described in claim 3, characterized in that, The integral calculation formula is as follows: in, This represents the attenuation data of the nth pixel; This represents the start and end coordinates of the edge at the nth pixel. , Represents the pixel phase change coefficient. Indicates the starting pixel position; This represents the sinusoidal brightness distribution value within the nth pixel.

5. The method as described in claim 3, characterized in that, The step of obtaining the sampling attenuation data of the phase offset sampling point based on the pixel-level brightness data of each phase offset sampling point specifically includes: The maximum and minimum brightness values ​​are determined from the pixel-level brightness data in the phase offset sampling points; Substitute the maximum brightness value and the minimum brightness value into the attenuation calculation formula. The modulation transfer function (MTF) value corresponding to the phase offset sampling point is calculated; where, This represents the MTF value corresponding to the phase offset sampling point; Indicates the maximum brightness value; Indicates the minimum brightness value; The MTF value corresponding to the phase offset sampling point is used as the sampling attenuation data of the phase offset sampling point.

6. The method as described in claim 3, characterized in that, The step of calculating the sampling attenuation data of the spatial frequency point based on the sampling attenuation data of all phase offset sampling points specifically includes: Take the average value of the sampling attenuation data corresponding to all phase offset sampling points; The average value is converted into a percentage to obtain the sampling attenuation data of the spatial frequency point.

7. The method as described in claim 1, characterized in that, The step of determining the joint imaging attenuation data for each of the plurality of spatial frequency points based on the sampling attenuation data of each of the plurality of spatial frequency points, the first attenuation data, and the second attenuation data specifically includes: For each spatial frequency point, the sampled attenuation data of the spatial frequency point, the first attenuation data, and the second attenuation data are multiplied together to obtain the joint imaging attenuation data of the spatial frequency point.

8. An imaging attenuation simulation system for a camera module, characterized in that, The system includes: Build modules are used to construct simulation camera modules; The acquisition module is used to acquire first attenuation data corresponding to the optical lens in the simulated camera module, and second attenuation data corresponding to the image sensor in the simulated camera module; The selection module is used to select several spatial frequency points from the imaging spatial frequency range of the simulated camera module; The first determining module is used to determine the sampling attenuation data of each of the plurality of spatial frequency points; The second determining module is used to determine the joint imaging attenuation data of the plurality of spatial frequency points based on the sampling attenuation data of the plurality of spatial frequency points, the first attenuation data, and the second attenuation data. A construction module is used to construct an imaging attenuation characteristic curve based on the joint imaging attenuation data of all spatial frequency points; the imaging attenuation characteristic curve is used to analyze the imaging performance of the simulated camera module.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-7.

10. A computer 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, it implements the steps of the method according to any one of claims 1-7.

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