Method for detecting CCD (Charge Coupled Device) performance of digital camera through Turing spot pattern structure

Abstract

The invention belongs to the technical field of digital camera CCD (charge coupled device) performance detection methods, and particularly discloses a method for detecting the performance of a digital camera CCD through a pattern structure. The method is used for solving the problem that a method for quantitatively and systematically evaluating the performance of the CCD image sensor through a specific physical phenomenon in the CCD image sensor is lacked at present. Comprising the following steps: (1) standardizing shooting setting, and performing image processing by applying a gray mode; (2) setting different types of standardized Qianling spot pattern structure sequences; (3) all the digital cameras participating in the test carry out shooting in the same experiment environment, and monochromatic areas of the shot images are amplified to obtain a light spot image structure of the images; and (4) manually comparing the Tailing spot pattern structure of the shot image with the standardized Tailing spot pattern structure sequence to obtain a corresponding performance score. According to the method, the cost and the operation complexity are reduced, and the objectivity of the evaluation standard and the wider applicability are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of digital camera CCD performance detection methods, and in particular relates to a method for detecting the CCD performance of a digital camera through a Turing pattern structure. Background Art

[0002] In recent years, with the rapid development of imaging technology and image processing algorithms, digital cameras are increasingly used in daily life and scientific research, especially in the field of high-precision imaging and data analysis. At the same time, charge-coupled devices (CCDs), as the core components of digital camera imaging, have become a hot topic of research. CCDs play an irreplaceable role in medical imaging, astronomical observation, scientific research, and consumer electronics. However, the performance of digital camera image sensors (such as sensitivity, signal-to-noise ratio, resolution, etc.) will directly affect the image quality of the camera. As an important form of image sensor implementation, how to detect CCD performance through effective methods to ensure the high performance of image sensors has become an important research direction.

[0003] Although existing digital cameras have high resolution and wide dynamic range, how to effectively evaluate and compare the CCD performance of different cameras, especially under the same environmental conditions, remains a technical challenge. Most performance evaluation methods focus on measuring parameters such as the camera's signal-to-noise ratio, dynamic range, and color accuracy. At present, there are two main types of detection methods for CCD performance: one is a direct measurement method based on hardware testing, which measures its performance by physically testing the charge migration process inside the CCD, such as dark current testing, dynamic range measurement, and quantum efficiency evaluation. However, such methods usually require expensive and complex test equipment, and the operation process is cumbersome and difficult to implement under normal conditions; the other type of method is an indirect measurement based on image analysis, which evaluates its performance by analyzing the quality of the image captured by the CCD. Such methods usually rely on indicators such as noise level, signal uniformity, and dynamic details in the image to judge the performance of the CCD. For example, the ISO12233 resolution test chart uses a specific test pattern, shoots the pattern, and analyzes the retention of details in the image (such as line clarity and resolution) to evaluate the performance of the CCD. However, most of these indicators require subjective judgment and lack systematic and scientific means to ensure the consistency and reliability of the evaluation.

[0004] In recent years, Turing Patterns, as a typical spatiotemporal ordered structure far from thermodynamic equilibrium, have gradually been widely used in many disciplines such as physics, chemistry and biology. The formation mechanism of Turing Patterns depends on the interaction between activators and inhibitors, which form a stable and ordered pattern structure in the system through competitive propagation of different diffusion rates. Although Turing Patterns have achieved remarkable results in explaining biology, chemical reactions and other aspects, the application research of Turing Patterns in the field of electronic engineering technology is still relatively limited.

[0005] In existing research, some attempts have been made to induce the generation of Turing patterns in semiconductor materials through specific physical conditions, and then use them to analyze the charge migration characteristics inside the materials. However, most of these studies are limited to experimental verification of organic semiconductor materials or liquid crystal materials. The required material costs are high and the experimental conditions are complex, making it difficult to promote and apply them in actual imaging devices. CCD in digital cameras is a commonly used semiconductor device, and the complexity of its internal microscopic charge distribution makes it possible for the formation of Turing patterns. However, the generation and utilization of Turing patterns in CCDs are still very limited.

[0006] In summary, there is currently a lack of a convenient, objective and universal method for testing the performance of CCDs in digital cameras, especially a method for quantitatively and systematically evaluating the performance of CCD image sensors through specific physical phenomena (such as Turing patterns) inside them. In the related research using Turing patterns, most of the research focuses on theoretical analysis and experimental verification under specific conditions, and has not yet explored how to induce and utilize Turing patterns through the actual shooting conditions of digital cameras in conventional environments. This makes the existing schemes ineffective for performance comparisons between different CCDs, and cannot be applied to the performance testing of daily digital cameras. Summary of the invention

[0007] The purpose of the present invention is to provide a method for detecting the performance of a digital camera CCD through a Turing pattern structure, which effectively solves the current problem of lack of a method for quantitatively and systematically evaluating the performance of a CCD image sensor through specific physical phenomena (such as the Turing pattern) inside the CCD image sensor.

[0008] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for detecting the CCD performance of a digital camera through a Turing pattern structure, comprising the following steps: S1, standardized shooting settings: all digital cameras participating in the test are set to the same ISO value, and use a uniform model of lens to ensure that each camera captures the same amount of light and has the same exposure time, control exposure parameters and shooting distance, ensure that internal parameters and external environmental conditions are consistent, and apply grayscale mode for image processing.

[0009] S2. Set different types of standardized Turing pattern structure sequences to evaluate the performance of digital cameras under different detail and contrast conditions.

[0010] S3. All digital cameras participating in the test were used to shoot in the same experimental environment, and the monochrome area of ​​the captured images was enlarged to obtain the Turing pattern structure of the captured images.

[0011] S4. Manually compare the Turing pattern structures of the images taken by each digital camera obtained in step S3 with the standardized Turing pattern structure sequence set in step S2, analyze the closest structure type between the Turing pattern structure of the taken image and the standardized Turing pattern structure sequence, and obtain corresponding performance scores.

[0012] Furthermore, in step S1, the ISO value is set to 200, and the lens is a 50mm f / 1.8S lens.

[0013] Furthermore, in step S1, the aperture is set to f / 8 and the shutter speed is set to 1 / 60 second; the digital camera is set in shutter priority mode, without exposure compensation, and shooting is performed in evaluative metering mode to ensure that each camera captures the same amount of light and has the same exposure time.

[0014] Furthermore, in step S2, the standardized Turing pattern structure sequence is obtained by numerical simulation, and the Turing pattern structure gradually transitions from light spots to maze stripes and finally to dark spot structures, and the size of the same type of spot patterns gradually decreases.

[0015] Furthermore, in shutter priority mode, the relationship between ISO and current is expressed as: 0 =K log 2 ISO+b, where j 0 is the current passing through the CCD, K and b are constants.

[0016] Considering CCD as a two-dimensional plane system in the horizontal direction, a mathematical model is established for the charge transfer process of CCD in shutter priority mode: Where a represents the charge density of free electrons trapped in the potential trap in the CCD, u represents the normalized voltage applied to the metal gate in the CCD, T represents the normalized tunneling current coefficient, α represents the ratio of the CCD capacitance to the total capacitance of the camera, and D a represents the diffusion coefficient of a, D u represents the diffusion coefficient of u, and Δ represents the Laplace operator.

[0017] A mathematical model is used to describe how CCD controls the transfer of charges through the electric field in shutter priority mode and the influence of ISO value on the charge transfer process. The transfer process involves the spatial diffusion of free charges, which ultimately affects the structure type of Turing pattern in the image.

[0018] Furthermore, in step S4, the scoring criteria are: the score gradually decreases as the pattern structure type changes from light spots to a mixed structure of bright spots and stripes, and as the score decreases, the number and length of the stripes increase; the score gradually decreases as the pattern structure type transitions from a bright maze stripe structure to a dark maze stripe structure; the score gradually decreases as the pattern structure type changes from a mixed structure of dark spots and stripes to a dark spot structure, and as the score decreases, the number and length of the stripes decrease; if the pattern structure types are all dark spot structures, then as the score decreases, the size of the dark spots becomes smaller and the number decreases.

[0019] Compared with the prior art, the beneficial technical effects of the present invention are: (1) Reducing costs and operational complexity: The present invention uses an existing digital camera to directly acquire images to evaluate CCD performance, eliminating the traditional hardware testing requirements that require professional equipment and a high-cost laboratory environment. This simplification significantly reduces the complexity and cost of the test process, making CCD performance evaluation faster and more economical.

[0020] (2) Convenience of operation: The present invention allows CCD performance testing to be performed in a daily shooting environment, and operators can use standard shooting techniques to perform the test without professional technical training. This improves the convenience of operation, allowing more photographers and technology enthusiasts to evaluate camera performance on their own without the support of professional equipment.

[0021] (3) Objectivity of evaluation criteria: The present invention uses Turing patterns to evaluate CCD performance based on the structural type of the Turing pattern in the image, which is different from subjective evaluation or methods that rely on specific hardware parameters. The Turing pattern provides an objective evaluation criterion that can accurately evaluate the CCD performance of digital cameras, thereby ensuring the objectivity and repeatability of the test results.

[0022] (4) Wider applicability: The present invention is not limited to a specific type or brand of camera and can be widely applied to various CCD digital cameras, including consumer cameras, mobile phone cameras, and professional cameras. The universality of this method makes it an ideal tool for evaluating the performance of different cameras, especially for camera evaluation organizations, consumers, and camera manufacturers for product quality control and R&D.

[0023] (5) Promote product quality control and improvement: The present invention provides an accurate and reliable performance evaluation method, and camera manufacturers can use this data to optimize the design and manufacturing process of their products. This feedback mechanism helps to improve the quality and market competitiveness of the final product. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a standardized Turing pattern structure sequence.

[0025] Figure 2 It is the type of Turing pattern structure produced by cameras of different performance at the same ISO setting.

[0026] Figure 3 These are the Turing patterns (A1, B1, C1 and D1) photographed by digital cameras A, B, C and D in Example 1 and the corresponding standardized Turing pattern structures (A2, B2, C2 and D2). DETAILED DESCRIPTION

[0027] Example 1: A method for detecting the CCD performance of a digital camera using a Turing pattern structure, comprising the following steps: S1, standardized shooting settings: (1) All digital cameras participating in the test are set to the same ISO value (e.g., ISO200) and use a uniform lens model (e.g., a 50mm f / 1.8S lens). This standardized operation ensures that performance evaluation is performed under the same technical conditions.

[0028] (2) The aperture was set to f / 8 and the shutter speed was set to 1 / 60 s. The digital cameras were set in shutter priority mode, without exposure compensation, and shot using evaluative metering mode to ensure that each camera captured the same amount of light and had the same exposure time.

[0029] (3) Each camera is tested at the same photography location. The camera is fixed on a tripod and kept at a constant distance (e.g., 2 meters) from the Turing pattern test pattern. The exposure parameters and shooting distance are controlled to ensure that the internal parameters and external environmental conditions are consistent. At the same time, the grayscale mode is applied for image processing.

[0030] S2. Set different types of standardized Turing pattern structure sequences to evaluate the performance of digital cameras under different detail and contrast conditions.

[0031] The Turing pattern is a visual pattern with complex geometric shapes and contrast characteristics. The applicant has obtained a set of standardized Turing pattern structure sequences through numerical simulation, such as Figure 1 As shown in the figure, the structure of the pattern gradually transitions from light spots to maze stripes and finally to dark spots, and the size of the same type of spot patterns gradually decreases. Figure 1 The performance of digital cameras decreases from (a) to (t).

[0032] The ISO setting of a digital camera is used to adjust the camera's sensitivity to light. Specifically, the ISO value affects the camera's response to light, which is achieved through electronic signal amplification technology. Each ISO value corresponds to a different gain level, ISO 100 is set as the standard value, and increasing the ISO value is equivalent to increasing the gain of the signal. In shutter priority mode, the camera automatically adjusts the aperture size to ensure that the exposure of the photos taken is consistent. Therefore, under the same shooting conditions, increasing the ISO value will result in a decrease in the current passing through the CCD.

[0033] In shutter priority mode, the relationship between ISO and current is expressed as: 0 =K log 2 ISO+b, where j 0 is the current passing through the CCD, K and b are constants (e.g. K = -0.3 and b = 5.4). To better understand this relationship, it can be considered that the increase in ISO value is equivalent to amplifying the signal at a certain standard gain level. In addition, the CCD of a digital camera adjusts the electric field distribution inside the material through an external driving voltage, thereby guiding non-uniform charge transfer.

[0034] CCD can be regarded as a two-dimensional plane system in the horizontal direction. A mathematical model is established for the charge transfer process of CCD in shutter priority mode: Where a represents the charge density of free electrons trapped in the potential trap in the CCD, u represents the normalized voltage applied to the metal gate in the CCD, T represents the normalized tunneling current coefficient, α represents the ratio of the CCD capacitance to the total capacitance of the camera, and D a represents the diffusion coefficient of a, D u represents the diffusion coefficient of u, and Δ represents the Laplace operator.

[0035] The above mathematical model can be used to describe how the CCD controls the transfer of charge through the electric field in shutter priority mode and the influence of ISO value on the charge transfer process. This process involves the spatial diffusion of free charge, which ultimately affects the structure type of the Turing pattern in the image. Numerical simulation results show that when the ISO is the same, the Turing pattern structure types of cameras with different performances are as follows: Figure 2 As shown, Figure 2 The camera performance decreases from (I) to (VI).

[0036] S3. All digital cameras participating in the test were used to shoot in the same experimental environment, and the monochrome area of ​​the captured images was enlarged to obtain the Turing pattern structure of the captured images.

[0037] S4. Manually compare the Turing pattern structures of the images taken by each digital camera obtained in step S3 with the standardized Turing pattern structure sequence set in step S2, analyze the closest structure type between the Turing pattern structure of the taken image and the standardized Turing pattern structure sequence, and obtain corresponding performance scores.

[0038] The scoring criteria are: (1) Figure 1 The scores of (a)-(f) are 100, 95, 90, 85, 80, and 75, respectively. That is, the score of the pattern structure type decreases from light spots to a mixed structure of bright spots and stripes, and as the score decreases, the number and length of the stripes increase.

[0039] (2) Figure 1 The scores of (g)-(j) are 70, 65, 60, and 55, respectively. That is, the pattern structure type transitions from light maze stripes to dark maze stripes, and the scores gradually decrease.

[0040] (3) Figure 1 The scores of (k)-(o) are 50 points, 45 points, 40 points, 35 points, and 30 points, respectively. That is, the pattern structure type changes from a mixture of dark spots and stripes to a dark spot structure, and the score gradually decreases, and as the score decreases, the number and length of the stripes decrease.

[0041] (4) Figure 1 The scores of (p)-(t) are 25 points, 20 points, 15 points, 10 points, and 5 points. If the pattern structure types are all dark spot structures, the size of the dark spots will become smaller and the number will decrease as the score decreases.

[0042] The above scoring criteria are based on the standardized Turing pattern structure sequence, and the core basis of the scoring is the performance type of cameras with different performance in the Turing pattern. Specifically, the image score is closely related to the change of the Turing pattern structure type, especially the transition between spots, stripes and maze structures, as well as their pattern size characteristics under the same structure (such as the number and size of spots, the number and length of stripes, etc.). As the score decreases from 100 points to 5 points, the performance of the camera is gradually decreasing.

[0043] The above scoring criteria can fairly and effectively evaluate the imaging quality of each digital camera in a standardized environment. The final scoring result is used to reflect the overall performance of the digital camera CCD.

[0044] The core of the evaluation of the performance of the digital camera in the present invention is to compare the image quality captured by the camera under the same external environment and setting conditions at the same ISO value. During the evaluation process, the monochrome area of ​​the image is mainly compared with the standardized Turing pattern structure sequence to obtain a scoring score. The setting of the ISO value is particularly critical in this process because it directly affects the sensitivity of the camera. As the ISO value increases, the sensitivity of the camera increases, but it also brings about the problem of increased noise and decreased image quality.

[0045] Therefore, cameras that can capture high-quality images in dim environments at lower ISO settings usually have better sensor quality and image processing algorithms. Such cameras can effectively capture light under low ISO conditions and maintain image clarity and details without over-amplifying the signal (such high-performance cameras often correspond to the bright spot structure in the Turing pattern, such as Figure 2 (I)). On the contrary, if the camera cannot provide sufficient clarity in low light environments at low ISO, it often means that there are certain limitations in its sensor performance and signal processing capabilities (such low-performance cameras often correspond to dark spot structures in the Turing pattern, such as Figure 2 (VI)).

[0046] This ability to maintain image quality in low light environments is the key basis for evaluating the performance of a camera's CCD. An excellent CCD can provide high-quality shooting results without significantly increasing the ISO, relying on its excellent light sensitivity and efficient noise suppression technology. Therefore, high-quality performance at low ISO settings is an important indicator of camera performance.

[0047] Example 1: In order to demonstrate the application of the method described in Example 1, four digital cameras of different prices are selected in this example and tested by the method of detecting the CCD performance of digital cameras by using the Turing pattern structure described in Example 1. They are marked as A, B, C and D, and their prices are 18,000 yuan, 10,000 yuan, 5,000 yuan and 2,000 yuan respectively. The four cameras are tested in the same environment to ensure that all test conditions are properly controlled.

[0048] First, prepare for the test: set up a standardized shooting environment, set the experimental illumination to 100LX, and provide soft indoor lighting to simulate general scenes in daily life. All cameras use the same 50mm f / 1.8S lens, set to shutter priority mode, shoot in evaluative metering mode, and process images in grayscale mode. Each camera is mounted on a tripod with the same height and angle to ensure the same shooting angle and distance.

[0049] Secondly, shooting and data collection: each camera shoots the same object, and all the images are saved in RAW format to preserve the original information of the image. Then, the monochrome area of ​​the captured image is enlarged to obtain the Turing pattern structure in the photo.

[0050] Finally, the digital camera performance evaluation: the Turing pattern captured by each digital camera is compared with Figure 1 The standardized Turing pattern structure sequences were manually aligned, such as Figure 3 The photographed Turing pattern is analyzed to find the closest structure type to the standardized Turing pattern structure sequence, and the corresponding performance score is obtained accordingly. The score results are shown in Table 1. The higher the score, the stronger the CCD performance of the digital camera.

[0051] Table 1 CCD performance scores of each digital camera in Example 1

[0052] Camera No. A B C D Fraction 100 85 55 25

[0053] The present invention evaluates CCD performance by acquiring images taken by different cameras under the condition of consistent external conditions and the same internal parameters, and analyzing the structural characteristics of the Turing pattern generated by the images. This method not only provides an objective evaluation method based on physical patterns, but also can achieve effective comparison and detection of CCD performance in a conventional environment by using existing shooting equipment. Compared with existing detection methods, the present invention has the advantages of low cost, simple operation and more objective and consistent evaluation results.

[0054] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by technicians in this technical field within the essential scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for detecting CCD performance of a digital camera by using a Turing pattern structure, characterized in that: The following steps are involved: S1. Standardized shooting settings: All digital cameras participating in the test are set to the same ISO value and use the same lens model to ensure that each camera captures the same amount of light and has the same exposure time, control exposure parameters and shooting distance, ensure that internal parameters and external environmental conditions are consistent, and apply grayscale mode for image processing; S2. Setting different types of standardized Turing pattern structure sequences to evaluate the performance of digital cameras under different detail and contrast conditions; S3. All digital cameras participating in the test were used to shoot in the same experimental environment, and the monochrome area of ​​the captured images was enlarged to obtain the Turing pattern structure of the captured images; S4. Manually compare the Turing pattern structures of the images taken by each digital camera obtained in step S3 with the standardized Turing pattern structure sequence set in step S2, analyze the closest structure type between the Turing pattern structure of the taken image and the standardized Turing pattern structure sequence, and obtain corresponding performance scores.

2. The method for detecting the CCD performance of a digital camera by using a Turing pattern structure according to claim 1, characterized in that: In step S1, the ISO value is set to 200, and the lens is a 50mm f / 1.8S lens.

3. The method for detecting the CCD performance of a digital camera by using a Turing pattern structure according to claim 2, characterized in that: In step S1, the aperture is set to f / 8 and the shutter speed is set to 1 / 60 second; the digital camera is set in shutter priority mode, without exposure compensation, and uses evaluative metering mode to shoot, ensuring that each camera captures the same amount of light and has the same exposure time.

4. The method for detecting the CCD performance of a digital camera by using a Turing pattern structure according to claim 3, characterized in that: In step S2, the standardized Turing pattern structure sequence is obtained by numerical simulation, and the Turing pattern structure gradually transitions from light spots to maze stripes and finally to dark spot structures, and the size of the same type of spot patterns gradually decreases.

5. The method for detecting the CCD performance of a digital camera by using a Turing pattern structure according to claim 4, characterized in that: In shutter priority mode, the relationship between ISO and current is expressed as: j0 = K log2ISO + b, where j0 is the current passing through the CCD, and K and b are constants; Considering CCD as a two-dimensional plane system in the horizontal direction, a mathematical model is established for the charge transfer process of CCD in shutter priority mode: Where a represents the charge density of free electrons trapped in the potential trap in the CCD, u represents the normalized voltage applied to the metal gate in the CCD, T represents the normalized tunneling current coefficient, α represents the ratio of the CCD capacitance to the total capacitance of the camera, and D a represents the diffusion coefficient of a, D u represents the diffusion coefficient of u, Δ represents the Laplace operator; A mathematical model is used to describe how CCD controls the transfer of charges through the electric field in shutter priority mode and the influence of ISO value on the charge transfer process. The transfer process involves the spatial diffusion of free charges, which ultimately affects the structure type of Turing pattern in the image.

6. The method for detecting the performance of a digital camera CCD by using a Turing pattern structure according to claim 5, characterized in that: In step S4, the scoring criteria are: when the pattern structure type changes from light spots to a mixed structure of bright spots and stripes, the score gradually decreases, and as the score decreases, the number and length of the stripes increase; when the pattern structure type transitions from a bright maze stripe structure to a dark maze stripe structure, the score gradually decreases; when the pattern structure type changes from a mixed structure of dark spots and stripes to a dark spot structure, the score gradually decreases, and as the score decreases, the number and length of the stripes decrease; if the pattern structure types are all dark spot structures, as the score decreases, the size of the dark spots becomes smaller and the number decreases.

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