Purple clay pot seal image generation system

Abstract

The invention relates to the technical field of image processing, in particular to a dark-red enameled pottery seal image generation system, which comprises an interaction module for acquiring a gray image and an HSV image of a dark-red enameled pottery seal image; the feature extraction module is used for performing feature extraction on the acquired dark-red enameled pottery image and judging the processing intensity of the dark-red enameled pottery image; the character enhancement module is used for analyzing the character enhancement scheme of the dark-red enameled pottery seal image; the texture enhancement module is used for analyzing the texture enhancement scheme of the dark-red enameled pottery seal image; the color adjusting module is used for analyzing the color adjusting scheme of the dark-red enameled pottery seal image; the image generation module is used for outputting the image generation scheme to a user; the quality evaluation module is used for evaluating the quality of the generated image; and the quality monitoring module is used for monitoring the quality of the image generated in the monitoring period according to the quality evaluation result of the image generated in the monitoring period. According to the invention, the seal image generation efficiency is improved.

Description

Technical Field

[0001] The invention relates to the technical field of image processing, and in particular to a purple clay teapot seal image generation system. Background Art

[0002] Zisha teapot is one of the important handicrafts in Chinese traditional culture, famous for its unique material and exquisite craftsmanship. Zisha teapot seal is not only a symbol of identity and quality, but also an important symbol of art appreciation. Existing seal image generation technology mainly relies on traditional image processing methods, such as Fourier transform, Laplace transform, etc., but it is insufficient in processing complex texture and detail information.

[0003] Chinese patent publication number CN116311272A discloses a method for generating a seal image and a seal recognition method. It relates to the field of artificial intelligence. The method includes: obtaining an initial template image containing an elliptical seal outline, an organization name and a base plate image, and determining the character size; for each character in the organization name, determining the target arc length corresponding to the character according to the preset total arc length and the target sequence of the character; obtaining the mapping relationship between the arc length and the eccentric angle of the elliptical seal outline, determining the target information of the character according to the target arc length and the mapping relationship, and arranging the characters in the base plate image according to the target information to obtain a target base plate image matching the characters; according to the target base plate image matched by each character, adding each character to the initial template image to obtain a seal image matching the organization name. The present invention solves the technical problem of low annotation efficiency in the prior art in the process of seal recognition, in which a real seal image is used as a training sample of a seal recognition model; it can be seen that when the scheme generates the seal image, the processing intensity of the seal image is not analyzed, and there is a problem of low seal image generation efficiency. Summary of the invention

[0004] The object of the present invention is to provide a purple clay teapot seal image generation system to solve at least one of the problems existing in the prior art.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] Interaction module, used to obtain the grayscale image and HSV image of the purple clay teapot seal image;

[0007] A feature extraction module is used to extract features from the grayscale image and the HSV image of the obtained purple clay teapot seal image, and to judge the processing intensity of the purple clay teapot seal image according to the extraction result;

[0008] A text enhancement module is used to analyze the text enhancement scheme of the purple clay teapot seal image according to the processing intensity of the purple clay teapot image;

[0009] A texture enhancement module is used to analyze the texture enhancement scheme of the purple clay teapot seal image according to the processing strength of the purple clay teapot image;

[0010] A color adjustment module is used to analyze the color adjustment scheme of the purple clay teapot seal image according to the processing intensity of the purple clay teapot image;

[0011] An image generation module, used to output the text enhancement scheme, the texture enhancement scheme and the color adjustment scheme as an image generation scheme to the user;

[0012] A quality assessment module is used to construct the detail similarity between the generated image and the purple clay teapot seal image, and to evaluate the quality of the generated image based on the result of the detail similarity construction;

[0013] The quality monitoring module is used to monitor the quality of the images generated within the monitoring period according to the results of the image quality assessment generated within the monitoring period.

[0014] Furthermore, the feature extraction module is provided with a clarity analysis unit, a contrast analysis unit, a noise analysis unit, a richness analysis unit, a saturation analysis unit and a processing intensity judgment unit; the clarity analysis unit constructs an image clarity index n1 according to the grayscale image of the purple clay teapot seal image obtained, and sets: in, It represents the result after the image I is processed by the Laplace operator. express The variance of

[0015] The contrast analysis unit constructs the image contrast index n2 according to the grayscale image of the acquired purple clay teapot seal image, and sets:

[0016]

[0017] Among them, H 均 is the average value of each pixel in the grayscale image of the purple clay teapot seal image, H(i) is the number of pixels with grayscale value i, i represents the grayscale value, and N is the number of pixels in the grayscale image;

[0018] The noise analysis unit divides the grayscale image of the purple clay teapot seal image into E parts, and constructs the image noise index n3, setting:

[0019]

[0020] Among them, Mj is the number of pixels of the j-th purple clay teapot seal image, Lm is the grayscale value of the m-th pixel of the j-th purple clay teapot seal image, and μj is the average grayscale value of each pixel of the j-th purple clay teapot seal image.

[0021] Further, the richness analysis unit is used to construct an image richness index n4 according to the grayscale image of the acquired purple clay teapot seal image, and set:

[0022]

[0023] Where Wq is the high-frequency coefficient of the qth wavelet transform, and Q is the number of high-frequency coefficients;

[0024] The saturation analysis unit constructs the saturation index n5 of the purple clay teapot seal image according to the acquired HSV image of the purple clay teapot seal image, and sets:

[0025]

[0026] Where N is the number of pixels in the HSV image of the purple clay teapot seal image, and St is the saturation value of the t-th pixel;

[0027] The processing intensity judgment unit constructs the image processing intensity index P according to the image clarity index, image contrast index, image noise index, image richness index and image saturation index, and sets: P = η×{x1×min{max[(k1-n1) / k1,0],1}+x2×min{max[(k2-n2) / k2,0],1}+x3×min{max[(k3-n3) / k3,0],1}+x4×min{max[(k4-n4) / k4,0],1}+x5×min{max[(k5-n5) / k5,0],1}}, wherein x1 is the clarity weight, k1 is the preset clarity threshold, x2 is the contrast weight, k2 is the preset contrast threshold, x3 is the noise weight, k3 is the preset noise threshold, x4 is the richness weight, k4 is the preset richness threshold, x5 is the saturation weight, k5 is the preset saturation threshold, and η is the preset proportional coefficient.

[0028] Furthermore, the text enhancement module analyzes the text sharpening strength of the purple clay teapot seal image according to the processing strength of the purple clay teapot image, and sets the text sharpening strength of the purple clay teapot seal image to R1, setting R1=P;

[0029] The text enhancement module analyzes the text contrast of the purple clay teapot seal image according to the processing strength of the purple clay teapot image, and sets the text contrast of the purple clay teapot seal image to R2, setting R2=1+exp(l gP),

[0030] The text enhancement module uses the analysis result of the text sharpening intensity of the purple clay teapot seal image and the analysis result of the text contrast as a text enhancement scheme.

[0031] Furthermore, the texture enhancement module analyzes the texture detail enhancement strength of the purple clay teapot seal image according to the processing strength of the purple clay teapot image, and sets the texture detail enhancement strength of the purple clay teapot seal image to Z1, setting Z1=3×P 2 -2×P 3 ;

[0032] The texture enhancement module analyzes the texture contrast of the purple clay teapot seal image according to the processing strength of the purple clay teapot image, and sets the texture contrast of the purple clay teapot seal image to Z2, setting Z2=0.8+α×P, where α is a first preset adjustment coefficient;

[0033] The texture enhancement module uses the analysis result of the texture detail enhancement strength and the analysis result of the texture contrast of the purple clay teapot seal image as a texture enhancement scheme.

[0034] Further, the color adjustment module analyzes the saturation of the purple clay teapot seal image according to the processing intensity of the purple clay teapot image, and sets the saturation of the purple clay teapot seal image to D1, setting D1=0.8+β×P, β is a second preset adjustment coefficient;

[0035] The color adjustment module analyzes the lightness of the purple clay teapot seal image according to the processing strength of the purple clay teapot image, and sets the lightness of the purple clay teapot seal image to D2, setting D2=0.9+γ×P, where β is a third preset adjustment coefficient;

[0036] The color adjustment module uses the analysis results of the saturation and brightness of the purple clay teapot seal image as a color adjustment solution.

[0037] Furthermore, the quality assessment module is provided with a similarity analysis unit and a quality assessment unit. The similarity analysis unit is used to obtain a grayscale image of the generated image and construct a detail similarity G between the generated image and the purple clay teapot seal image. The setting is:

[0038] G=(2H 均 ×μA+C1)×(2σIA+C2) / [(H 均 2 +μA 2 +C1)×(σI 2 +σ

[0039] A 2 +C2)];

[0040] Among them, μA is the average gray value of each pixel in the generated image, σI 2 is the variance of the grayscale value of each pixel in the purple clay pot seal image, σA 2is the variance of the grayscale value of each pixel in the generated image, σIA is the covariance between the purple clay teapot seal image and the generated image, C1 is the first preset constant, and C2 is the second preset constant.

[0041] Furthermore, the quality assessment unit is used to compare the detail similarity G with a preset similarity to assess the quality of the generated image, wherein:

[0042] If G≥G1, the quality assessment unit determines that the quality of the generated image is normal; if G<G1, the quality assessment unit determines that the quality of the generated image is abnormal, wherein G1 is a preset similarity.

[0043] Furthermore, the quality monitoring module is provided with a quality monitoring unit and a volatility analysis unit, wherein the quality monitoring unit monitors the quality of the image generated in the monitoring period according to the result of the image quality evaluation generated in the monitoring period, and optimizes the analysis process of the image generation scheme for the next monitoring period according to the quality monitoring result, wherein:

[0044] If B0 / B1≤b, the quality monitoring unit determines that the quality of the image generated in the current monitoring cycle is qualified and no optimization is performed; if B0 / B1>b, the quality monitoring unit determines that the quality of the image generated in the current monitoring cycle is unqualified, and optimizes the analysis process of the image generation plan for the next monitoring cycle, and sets the optimized preset proportional coefficient to η', where B0 is the number of images with abnormal quality generated in the monitoring cycle, B1 is the number of images generated in the monitoring cycle, and b is the preset abnormality ratio.

[0045] Furthermore, the volatility analysis unit generates the image detail similarity analysis results within the monitoring period to construct the volatility coefficient BD, and sets And according to the analysis results, the optimization of the analysis process of the image generation scheme for the next monitoring cycle is adjusted, including:

[0046] If BD≤bd, the volatility analysis unit determines that the volatility of the detail similarity of the image generated in the current monitoring period is normal and does not make any adjustments; if BD>bd, the volatility analysis unit determines that the volatility of the detail similarity of the image generated in the current monitoring period is abnormal, and adjusts the optimization of the image generation scheme analysis process for the next monitoring period, and sets the adjusted preset abnormality ratio to b', where Gg is the detail similarity of the g-th generated image in the monitoring period.

[0047] The beneficial effects of the present invention are as follows: the feature extraction module quantifies multiple factors such as clarity, contrast, noise, richness and saturation to better understand the overall quality of the image and then make precise adjustments. This comprehensive analysis capability not only improves the scientificity and rationality of image processing, but also ensures that the text on the purple clay teapot seal is clear and easy to read by enhancing the text, thereby improving the efficiency and accuracy of information transmission. The text sharpening allows the subtle text features to be retained, and the enhanced contrast makes the level and position of the text in the image more prominent. The texture enhancement module allows the delicate sense of the pattern to be reflected and can also provide users with a deeper visual experience. The color adjustment module optimizes the saturation and brightness to make the entire image more visually harmonious and beautiful, thereby improving the user's image experience. The quality assessment module optimizes the image processing process by analyzing the similarity between the generated image and the original image with feedback, continuously improves the standard and quality of image generation, thereby improving the efficiency of seal image generation and improving the user experience. The quality monitoring module can adjust the processing parameters in real time by analyzing abnormal images, improve the success rate and quality level of image generation, and ultimately improve the efficiency of seal image generation and improve the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 Schematic diagram of the structure of the purple clay teapot seal image generation system of this embodiment.

[0050] Figure 2 Schematic diagram of the structure of the feature extraction module of this embodiment.

[0051] Figure 3 Schematic diagram of the structure of the quality assessment module of this embodiment.

[0052] Figure 4 Schematic diagram of the structure of the quality monitoring module of this embodiment. DETAILED DESCRIPTION

[0053] In order to more clearly illustrate the present invention, the present invention is further described below in conjunction with preferred embodiments and accompanying drawings. Similar components in the accompanying drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the scope of protection of the present invention.

[0054] It should be noted that, although the terms first, second, third, etc. may be used to describe in the embodiments of the present application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0055] See also Figure 1 As shown, it is a structural schematic diagram of the purple clay teapot seal image generation system of this embodiment, and the system includes:

[0056] An interactive module is used to obtain a grayscale image and an HSV image of a purple clay teapot seal image; in this embodiment, the method for obtaining the grayscale image and the HSV image of the purple clay teapot seal image is not specifically limited, and those skilled in the art can freely set it, as long as the grayscale image and the HSV image of the purple clay teapot seal image are satisfied. The grayscale image and the HSV image of the purple clay teapot seal image can be obtained through OpenCV and PIL in Python, and the purple clay teapot seal image can be obtained through user interaction;

[0057] The feature extraction module is used to extract features from the grayscale image and HSV image of the purple clay teapot seal image, and judge the processing intensity of the purple clay teapot image according to the extraction result. The feature extraction module is connected to the interactive module; please refer to Figure 2 As shown, the feature extraction module includes:

[0058] The clarity analysis unit is used to construct an image clarity index n1 according to the grayscale image of the purple clay teapot seal image obtained to improve the accuracy of feature extraction of the purple clay teapot seal image.

[0059] The image clarity index n1 is constructed as follows:

[0060] in, It represents the result after the image I is processed by the Laplace operator. express The clarity analysis unit constructs an image clarity index to enhance the adaptability to images of different qualities and improve the robustness and stability of the system in practical applications.

[0061] Specifically, in this embodiment, The acquisition method is specifically limited, and those skilled in the art can freely set it, as long as it meets The acquisition requirements are as follows, where It can be obtained using the convolution function of OpenCV and sc ik it-image.

[0062] Please continue to refer to 2, the feature extraction module also includes a contrast analysis unit, which constructs an image contrast index n2 according to the grayscale image of the purple clay teapot seal image obtained to improve the accuracy of the purple clay teapot seal image feature extraction, and the contrast analysis unit is connected to the clarity analysis unit;

[0063] The image contrast index n2 is constructed as follows:

[0064]

[0065] Among them, H 均 is the average value of each pixel in the grayscale image of the purple clay teapot seal image, H(i) is the number of pixels with grayscale value i, i represents the grayscale value, and N is the number of pixels in the grayscale image; the contrast analysis unit can better identify the details and features in the image by analyzing the contrast of the image. The contrast analysis can reveal the subtle structure and texture changes of the seal, thereby improving the accuracy of feature extraction.

[0066] Specifically, in this embodiment, there is no specific limitation on the method for obtaining the number of pixels with a gray value of i and the number of pixels in a gray image. Those skilled in the art can set it freely, and it is only necessary to meet the requirements for obtaining the number of pixels with a gray value of i and the number of pixels in a gray image. Among them, the number of pixels with a gray value of i can be obtained through the np.hi stogram() function in the NumPy library, and the number of pixels in a gray image can be obtained through OpenCV.

[0067] Please continue reading Figure 2 As shown, the feature extraction module also includes a noise analysis unit, which is used to divide the grayscale image of the acquired purple clay teapot seal image into E parts, and construct an image noise index n3 to improve the accuracy of purple clay teapot seal image feature extraction, and the noise analysis unit is connected to the contrast analysis unit;

[0068] The image noise index n3 is constructed as follows:

[0069]

[0070] Among them, Mj is the number of pixels of the j-th purple clay teapot seal image, Lm is the grayscale value of the m-th pixel of the j-th purple clay teapot seal image, and μj is the average grayscale value of each pixel of the j-th purple clay teapot seal image; the noise analysis unit divides the image grayscale image into E parts and calculates the image noise index n3, which can more accurately identify and extract features in the purple clay teapot seal image, which helps to reduce the interference of noise on feature extraction, thereby improving the accuracy of the extraction result.

[0071] Specifically, in this embodiment, there is no specific limitation on the method for obtaining Lm and Mj, and those skilled in the art can set it freely, as long as the requirements for obtaining Lm and Mj are met, wherein Mj can be obtained through the size attribute of numpy, and Lm can be obtained through the traversal of the numpy array; in this embodiment, there is no specific limitation on the method for segmenting the grayscale image of the purple clay teapot seal image, and those skilled in the art can set it freely, as long as the requirements for segmenting the grayscale image of the purple clay teapot seal image are met, wherein the grayscale image of the purple clay teapot seal image can be divided into 8 equal parts.

[0072] Please continue reading Figure 2 As shown, the feature extraction module also includes a richness analysis unit, which is used to construct an image richness index n4 according to the grayscale image of the purple clay teapot seal image obtained to improve the accuracy of feature extraction of the purple clay teapot seal image, and the richness analysis unit is connected to the noise analysis unit;

[0073] The image richness index n4 is constructed as follows:

[0074]

[0075] Wherein, Wq is the high-frequency coefficient of the qth wavelet transform, and Q is the number of high-frequency coefficients; the richness analysis unit can reduce the interference of irrelevant information on feature extraction through accurate richness analysis, thereby improving the overall efficiency of the system.

[0076] Specifically, the high-frequency coefficients of the wavelet transform described in this embodiment are a set of coefficients obtained in the wavelet transform process, including the horizontal detail coefficients, vertical detail coefficients and diagonal detail coefficients of the grayscale image. In this embodiment, the method for obtaining Wq and Q is not specifically limited, and technical personnel in this field can set them freely as long as the requirements for obtaining Wq and Q are met. Among them, Wq and Q can be obtained through the PyWave l ets library in Python.

[0077] Please continue reading Figure 2 As shown, the feature extraction module also includes a saturation analysis unit, which is used to construct a saturation index n5 of the purple clay teapot seal image according to the acquired HSV image of the purple clay teapot seal image to improve the accuracy of the purple clay teapot seal image feature extraction, and the saturation analysis unit is connected to the richness analysis unit;

[0078] The image saturation index n5 is constructed as follows:

[0079] Wherein, N is the number of pixels in the HSV image of the purple clay teapot seal image, and St is the saturation value of the t-th pixel; the saturation analysis unit constructs a saturation index to more accurately capture the color information of the purple clay teapot seal image, especially those subtle color difference changes, which helps to more accurately extract and identify features in the image, thereby improving the accuracy of image recognition.

[0080] Specifically, in this embodiment, the method for obtaining St is not specifically limited, and technical personnel in this field can set it freely as long as the requirements for obtaining St are met. Among them, St can obtain the saturation value of the pixel through the OpenCV library in the Python language. In OpenCV, the image is converted from the BGR color space to the HSV color space through the function cv2.cvtColor(), and then the saturation value of each pixel in the HSV image can be directly accessed.

[0081] Please continue reading Figure 2 As shown, the feature extraction module also includes a processing strength judgment unit, which constructs an image processing strength index P according to an image clarity index, an image contrast index, an image noise index, an image richness index, and an image saturation index to better understand the overall quality of the image and then make precise adjustments. The processing strength judgment unit is connected to the saturation analysis unit;

[0082] The processing strength index P is constructed as follows: P = η × {x1 × min {max [(k1-n1) / k1, 0], 1} + x2 × min {max [(k2-n2) / k2, 0], 1} + x3 × min {max [(k3-n3) / k3, 0], 1} + x4 × min {max [(k4-n4) / k4, 0], 1} + x5 × min {max [(k5-n5) / k5, 0], 1}}, where x1 is the clarity weight, k1 is the preset clarity threshold, x2 is the contrast weight, and k2 is the preset contrast. threshold, x3 is the noise weight, k3 is the preset noise threshold, x4 is the richness weight, k4 is the preset richness threshold, x5 is the saturation weight, k5 is the preset saturation threshold, η is the preset proportional coefficient, x1+x2+x3+x4+x5=1; the processing intensity judgment unit quantifies multiple factors such as clarity, contrast, noise, richness and saturation to better understand the overall quality of the image, and then make precise adjustments. This comprehensive analysis capability not only improves the scientificity and rationality of image processing, but also ultimately improves the efficiency of seal image generation, while also improving the user experience.

[0083] Specifically, in this embodiment, there is no specific limitation on the values ​​of clarity weight, preset clarity threshold, contrast weight, preset contrast threshold, noise weight, preset noise threshold, richness weight, preset richness threshold, saturation weight, preset saturation threshold and preset proportional coefficient. Those skilled in the art can set them freely as long as the value requirements of clarity weight, preset clarity threshold, contrast weight, preset contrast threshold, noise weight, preset noise threshold, richness weight, preset richness threshold, saturation weight, preset saturation threshold and preset proportional coefficient are met. Among them, the best value of k1 is 25, the best value of k2 is 60, the best value of k3 is 12, the best value of k4 is 0.6, the best value of k5 is 0.5, the best value of x1 is 0.25, the best value of x2 is 0.2, the best value of x3 is 0.18, the best value of x4 is 0.2, the best value of x5 is 0.17, and the best value of η is 0.95.

[0084] Please continue reading Figure 1 As shown, the system further includes a text enhancement module, which is connected to the feature extraction module and is used to analyze the text sharpening strength of the purple clay teapot seal image according to the processing strength of the purple clay teapot image, so that the subtle text features can be retained, and the text sharpening strength of the purple clay teapot seal image is set to R1, and R1=P;

[0085] The text enhancement module analyzes the text contrast of the purple clay teapot seal image according to the processing strength of the purple clay teapot image, so as to make the level and position of the text in the image more prominent, and sets the text contrast of the purple clay teapot seal image to R2, setting R2=1+exp(l gP),

[0086] The text enhancement module uses the analysis results of the text sharpening intensity of the purple clay teapot seal image and the analysis results of the text contrast as a text enhancement scheme; the text enhancement module can ensure that the text on the purple clay teapot seal is clear and easy to read by enhancing the text, thereby improving the efficiency and accuracy of information transmission. Text sharpening preserves subtle text features, while enhanced contrast makes the layers and positions of the text in the image more prominent, ultimately improving the efficiency of seal image generation while improving the user experience.

[0087] Please continue reading Figure 1 As shown, the system also includes a texture enhancement module, which is connected to the text enhancement module; the texture enhancement module is used to analyze the texture detail enhancement strength of the purple clay teapot seal image according to the processing strength of the purple clay teapot image, so that the delicate feeling of the pattern can be reflected, and the texture detail enhancement strength of the purple clay teapot seal image is set to Z1, and Z1=3×P 2 -2×P 3 ;

[0088] The texture enhancement module analyzes the texture contrast of the purple clay teapot seal image according to the processing strength of the purple clay teapot image to provide the user with a deeper visual experience, and sets the texture contrast of the purple clay teapot seal image to Z2, setting Z2=0.8+α×P, α is the first preset adjustment coefficient, 0.3<α<0.4;

[0089] The texture enhancement module uses the analysis results of the texture detail enhancement intensity and the texture contrast of the purple clay teapot seal image as a texture enhancement scheme; the texture enhancement module enables the delicate feeling of the pattern to be reflected, and can also provide users with a deeper visual experience, ultimately improving the efficiency of seal image generation while improving the user experience.

[0090] Specifically, in this embodiment, the setting of the first preset adjustment coefficient is not specifically limited, and those skilled in the art can set it freely as long as the setting requirements of the first preset adjustment coefficient are met, wherein the optimal value of α is 0.32.

[0091] Please continue reading Figure 1 As shown, the system further includes a color adjustment module, which is connected to the texture enhancement module; the color adjustment module is used to analyze the saturation of the purple clay teapot seal image according to the processing intensity of the purple clay teapot image, so as to make the whole image more harmonious and beautiful visually, and set the saturation of the purple clay teapot seal image to D1, setting D1=0.8+β×P, β is a second preset adjustment coefficient, 0.28<β<0.4;

[0092] The color adjustment module analyzes the brightness of the purple clay teapot seal image according to the processing strength of the purple clay teapot image to improve the user's image experience, and sets the brightness of the purple clay teapot seal image to D2, setting D2=0.9+γ×P, β is the third preset adjustment coefficient, 0.15<γ<0.2;

[0093] The color adjustment module uses the analysis results of the saturation and brightness of the purple clay teapot seal image as a color adjustment scheme; the color adjustment module can make the entire image more visually harmonious and beautiful by optimizing the saturation and brightness, improve the user's image experience, and ultimately improve the efficiency of seal image generation while improving the user experience.

[0094] Specifically, in this embodiment, there is no specific limitation on the setting of the second preset adjustment coefficient and the third preset adjustment coefficient. Technical personnel in this field can set them freely as long as the setting requirements of the second preset adjustment coefficient and the third preset adjustment coefficient are met. Among them, the optimal value of β is 0.35, and the optimal value of γ is 0.18.

[0095] Please continue reading Figure 1As shown, the system further includes an image generation module, which is used to output the text enhancement scheme, the texture enhancement scheme and the color adjustment scheme as image generation schemes to the user, and the image generation module is connected to the color adjustment module.

[0096] Please continue reading Figure 1 As shown, the system also includes a quality assessment module, which is used to construct the detail similarity between the generated image and the purple clay teapot seal image, and evaluate the quality of the generated image according to the construction result of the detail similarity. The quality assessment module is connected to the image generation module; please refer to Figure 3 As shown, the quality assessment module includes a similarity analysis unit, which obtains a grayscale image of the generated image and constructs a detail similarity G between the generated image and the purple clay teapot seal image to optimize the image processing process by feedback;

[0097] The construction of detail similarity G is as follows:

[0098] G=(2H 均 ×μA+C1)×(2σIA+C2) / [(H 均 2 +μA 2 +C1)×(σI 2 +σ

[0099] A 2 +C2)];

[0100]

[0101] Among them, vy is the gray value of the yth pixel in the purple clay pot seal image, fy is the gray value of the yth pixel in the generated image, μA is the average gray value of each pixel in the generated image, σI 2 is the variance of the grayscale value of each pixel in the purple clay pot seal image, σA 2 is the variance of the grayscale value of each pixel in the generated image, σIA is the covariance between the purple clay teapot seal image and the generated image, C1 is the first preset constant, and C2 is the second preset constant; the similarity analysis unit optimizes the image processing process by analyzing the similarity between the generated image and the original image through feedback, continuously improves the standard and quality of image generation, thereby improving the efficiency of seal image generation and improving the user experience.

[0102] Specifically, the generated image described in this embodiment is a purple clay teapot seal image processed by an image generation scheme; the generated image described in this embodiment is obtained through user interaction, and the method for obtaining the relevant parameters of the generated image is the same as the method for obtaining the parameters of the purple clay teapot seal image, which will not be repeated in this embodiment. In this embodiment, no specific limitation is made on the values ​​of C1 and C2, and those skilled in the art can set them freely as long as the value requirements of C1 and C2 are met. Among them, when the image is an 8-bit image, the best value of C1 is (0.01×255)2, and the best value of C2 is (0.03×255)2.

[0103] Please continue reading Figure 3 As shown, the quality assessment module further includes a quality assessment unit, which compares the detail similarity G with a preset similarity to assess the quality of the generated image, and the quality assessment unit is connected to the similarity analysis unit;

[0104] The quality of the generated images is evaluated as follows:

[0105] When G≥G1, the quality assessment unit determines that the quality of the generated image is normal;

[0106] When G<G1, the quality assessment unit determines that the quality of the generated image is abnormal;

[0107] Among them, G1 is the preset similarity.

[0108] Specifically, this embodiment does not impose any specific limitation on the setting of the preset similarity, and those skilled in the art can set it freely as long as the setting requirements of the preset similarity are met. Among them, the best value of G1 is 0.8.

[0109] Please continue reading Figure 1 As shown, the system also includes a quality monitoring module, which is used to monitor the quality of the image generated during the monitoring period according to the result of the image quality evaluation generated during the monitoring period, and the quality monitoring module is connected to the quality evaluation module; please refer to Figure 4 As shown, the quality monitoring module includes a quality monitoring unit, which is used to monitor the quality of the image generated in the monitoring period according to the result of the image quality evaluation generated in the monitoring period, so as to analyze the abnormal image, and optimize the analysis process of the image generation scheme of the next monitoring period according to the quality monitoring result, so as to adjust the processing parameters in real time;

[0110] The optimization scheme for the analysis process of the image generation scheme for the next monitoring cycle is as follows:

[0111] When B0 / B1≤b, the quality monitoring unit determines that the quality of the image generated in the current monitoring period is qualified and does not perform optimization;

[0112] When B0 / B1>b, the quality monitoring unit determines that the quality of the image generated in the current monitoring period is unqualified, and optimizes the analysis process of the image generation scheme for the next monitoring period, and sets the optimized preset proportional coefficient as η', setting η'=η×[1-(e (B0 / B1-b) / (B0 / B1+b)-1 ) / (e-1)], where e is the natural logarithm, B0 is the number of images with abnormal quality generated within the monitoring period, B1 is the number of images generated within the monitoring period, and b is the preset abnormal ratio; the quality monitoring unit can adjust the processing parameters in real time by analyzing the abnormal images, thereby improving the success rate and quality level of image generation, and ultimately improving the efficiency of seal image generation while improving the user experience.

[0113] Specifically, in this embodiment, there is no specific limitation on the setting of the monitoring period, and those skilled in the art can set it freely, as long as the setting requirements of the monitoring period are met, wherein the monitoring period can be set to 20 days, 30 days, etc.; in this embodiment, there is no specific limitation on the setting of the preset abnormality ratio, and those skilled in the art can set it freely, as long as the setting requirements of the preset abnormality ratio are met, wherein the optimal value of b is 0.05.

[0114] Please continue reading Figure 4 As shown, the quality monitoring module also includes a volatility analysis unit, which performs a volatility analysis on the analysis results of the similarity of image details generated within the monitoring period to identify potential quality problems, and adjusts the optimization of the image generation scheme analysis process for the next monitoring period according to the analysis results, and the volatility analysis unit is connected to the quality monitoring unit;

[0115] The process of constructing the volatility coefficient BD is as follows:

[0116]

[0117] The optimization and adjustment plan for the analysis process of the image generation plan for the next monitoring cycle is as follows:

[0118] When BD≤bd, the volatility analysis unit determines that the volatility of the similarity of the image details generated in the current monitoring period is normal and does not make any adjustments;

[0119] When BD>bd, the volatility analysis unit determines that the volatility of the detail similarity of the image generated in the current monitoring period is abnormal, and adjusts the optimization of the image generation scheme analysis process for the next monitoring period, and sets the adjusted preset abnormal ratio as b', setting b'=b×exp[-(BD-bd)], bd is the preset volatility coefficient, Gg is the detail similarity of the g-th generated image in the monitoring period, G 均 is the mean value of the similarity of image details generated during the monitoring period, The fluctuation analysis unit can identify potential quality problems and optimize abnormal conditions in the image generation process by monitoring the fluctuation of detail similarity. Fluctuation monitoring can ensure the consistency and stability of image generation, ultimately improving the efficiency of seal image generation and improving user experience.

[0120] Specifically, in this embodiment, the setting of the preset fluctuation coefficient is not specifically limited, and those skilled in the art can set it freely as long as the setting requirements of the preset fluctuation coefficient are met. Among them, the optimal value of bd is 0.15.

[0121] Specifically, the purple clay teapot seal image generation system described in this embodiment is applied to the field of image processing. By performing multi-dimensional feature analysis on the original purple clay teapot seal image, intelligent judgment of processing intensity is achieved. The system adaptively generates text enhancement, texture enhancement and color adjustment schemes according to the processing intensity, and performs real-time evaluation and monitoring of the generated image quality, thereby improving the efficiency of seal image generation.

[0122] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the embodiments here. All obvious changes or modifications derived from the technical solution of the present invention are still within the protection scope of the present invention.

Claims

1. A purple clay teapot seal image generation system, characterized in that: include, Interaction module, used to obtain the grayscale image and HSV image of the purple clay teapot seal image; A feature extraction module is used to extract features from the grayscale image and the HSV image of the obtained purple clay teapot seal image, and to judge the processing intensity of the purple clay teapot seal image according to the extraction result; A text enhancement module is used to analyze the text enhancement scheme of the purple clay teapot seal image according to the processing intensity of the purple clay teapot image; A texture enhancement module is used to analyze the texture enhancement scheme of the purple clay teapot seal image according to the processing strength of the purple clay teapot image; A color adjustment module is used to analyze the color adjustment scheme of the purple clay teapot seal image according to the processing intensity of the purple clay teapot image; An image generation module, used to output the text enhancement scheme, the texture enhancement scheme and the color adjustment scheme as an image generation scheme to the user; A quality assessment module is used to construct the detail similarity between the generated image and the purple clay teapot seal image, and to evaluate the quality of the generated image based on the result of the detail similarity construction; The quality monitoring module is used to monitor the quality of the images generated within the monitoring period according to the results of the image quality assessment generated within the monitoring period.

2. The purple clay teapot seal image generation system according to claim 1, characterized in that: The feature extraction module is provided with a clarity analysis unit, a contrast analysis unit, a noise analysis unit, a richness analysis unit, a saturation analysis unit and a processing intensity judgment unit; the clarity analysis unit constructs an image clarity index n1 according to the grayscale image of the purple clay teapot seal image obtained, and sets: in, It represents the result after the image I is processed by the Laplace operator. express The variance of The contrast analysis unit constructs the image contrast index n2 according to the grayscale image of the acquired purple clay teapot seal image, and sets: Among them, H 均 is the average value of each pixel in the grayscale image of the purple clay teapot seal image, H(i) is the number of pixels with grayscale value i, i represents the grayscale value, and N is the number of pixels in the grayscale image; The noise analysis unit divides the grayscale image of the purple clay teapot seal image into E parts, and constructs the image noise index n3, setting: Among them, Mj is the number of pixels of the j-th purple clay teapot seal image, Lm is the grayscale value of the m-th pixel of the j-th purple clay teapot seal image, and μj is the average grayscale value of each pixel of the j-th purple clay teapot seal image.

3. The purple clay teapot seal image generation system according to claim 2, characterized in that: The richness analysis unit is used to construct the image richness index n4 according to the grayscale image of the acquired purple clay teapot seal image, and set: Where Wq is the high-frequency coefficient of the qth wavelet transform, and Q is the number of high-frequency coefficients; The saturation analysis unit constructs the saturation index n5 of the purple clay teapot seal image according to the acquired HSV image of the purple clay teapot seal image, and sets: Where N is the number of pixels in the HSV image of the purple clay teapot seal image, and St is the saturation value of the t-th pixel; The processing intensity judgment unit constructs the image processing intensity index P according to the image clarity index, image contrast index, image noise index, image richness index and image saturation index, and sets: P = η×{x1×min{max[(k1-n1) / k1,0],1}+x2×min{max[(k2-n2) / k2,0],1}+x3×min{max[(k3-n3) / k3,0],1}+x4×min{max[(k4-n4) / k4,0],1}+x5×min{max[(k5-n5) / k5,0],1}}, wherein x1 is the clarity weight, k1 is the preset clarity threshold, x2 is the contrast weight, k2 is the preset contrast threshold, x3 is the noise weight, k3 is the preset noise threshold, x4 is the richness weight, k4 is the preset richness threshold, x5 is the saturation weight, k5 is the preset saturation threshold, and η is the preset proportional coefficient.

4. The purple clay teapot seal image generation system according to claim 3, characterized in that: The text enhancement module analyzes the text sharpening strength of the purple clay teapot seal image according to the processing strength of the purple clay teapot image, and sets the text sharpening strength of the purple clay teapot seal image to R1, setting R1=P; The text enhancement module analyzes the text contrast of the purple clay teapot seal image according to the processing strength of the purple clay teapot image, and sets the text contrast of the purple clay teapot seal image to R2, setting R2=1+exp(lgP). The text enhancement module uses the analysis result of the text sharpening intensity of the purple clay teapot seal image and the analysis result of the text contrast as a text enhancement scheme.

5. The purple clay teapot seal image generation system according to claim 4, characterized in that: The texture enhancement module analyzes the texture detail enhancement strength of the purple clay teapot seal image according to the processing strength of the purple clay teapot image, and sets the texture detail enhancement strength of the purple clay teapot seal image to Z1, setting Z1=3×P 2 -2×P 3 ; The texture enhancement module analyzes the texture contrast of the purple clay teapot seal image according to the processing strength of the purple clay teapot image, and sets the texture contrast of the purple clay teapot seal image to Z2, setting Z2=0.8+α×P, where α is a first preset adjustment coefficient; The texture enhancement module uses the analysis result of the texture detail enhancement strength and the analysis result of the texture contrast of the purple clay teapot seal image as a texture enhancement scheme.

6. The purple clay teapot seal image generation system according to claim 5, characterized in that: The color adjustment module analyzes the saturation of the purple clay teapot seal image according to the processing intensity of the purple clay teapot image, and sets the saturation of the purple clay teapot seal image to D1, setting D1=0.8+β×P, where β is a second preset adjustment coefficient; The color adjustment module analyzes the lightness of the purple clay teapot seal image according to the processing strength of the purple clay teapot image, and sets the lightness of the purple clay teapot seal image to D2, setting D2=0.9+γ×P, where β is a third preset adjustment coefficient; The color adjustment module uses the analysis results of the saturation and brightness of the purple clay teapot seal image as a color adjustment solution.

7. The purple clay teapot seal image generation system according to claim 6, characterized in that: The quality assessment module is provided with a similarity analysis unit and a quality assessment unit. The similarity analysis unit is used to obtain a grayscale image of the generated image and construct a detail similarity G between the generated image and the purple clay pot seal image. The setting is: G=(2H 均 ×μA+C1)×(2σIA+C2) / [(H 均 2 +μA 2 +C1)×(σI 2 +s A 2 +C2)]; Among them, μA is the average gray value of each pixel in the generated image, σI 2 is the variance of the grayscale value of each pixel in the purple clay pot seal image, σA 2 is the variance of the grayscale value of each pixel in the generated image, σIA is the covariance between the purple clay teapot seal image and the generated image, C1 is the first preset constant, and C2 is the second preset constant.

8. The purple clay teapot seal image generation system according to claim 7, characterized in that: The quality assessment unit is used to compare the detail similarity G with a preset similarity to assess the quality of the generated image, wherein: If G≥G1, the quality assessment unit determines that the quality of the generated image is normal; if G<G1, the quality assessment unit determines that the quality of the generated image is abnormal, wherein G1 is a preset similarity.

9. The purple clay teapot seal image generation system according to claim 8, characterized in that: The quality monitoring module is provided with a quality monitoring unit and a volatility analysis unit. The quality monitoring unit monitors the quality of the image generated in the monitoring period according to the result of the image quality evaluation generated in the monitoring period, and optimizes the analysis process of the image generation scheme of the next monitoring period according to the quality monitoring result, wherein: If B0 / B1≤b, the quality monitoring unit determines that the quality of the image generated in the current monitoring cycle is qualified and no optimization is performed; if B0 / B1>b, the quality monitoring unit determines that the quality of the image generated in the current monitoring cycle is unqualified, and optimizes the analysis process of the image generation plan for the next monitoring cycle, and sets the optimized preset proportional coefficient to η', where B0 is the number of images with abnormal quality generated in the monitoring cycle, B1 is the number of images generated in the monitoring cycle, and b is the preset abnormality ratio.

10. The purple clay teapot seal image generation system according to claim 9, characterized in that: The fluctuation analysis unit constructs the fluctuation coefficient BD based on the analysis results of the image detail similarity generated during the monitoring period, and sets And according to the analysis results, the optimization of the analysis process of the image generation scheme for the next monitoring cycle is adjusted, including: If BD≤bd, the volatility analysis unit determines that the volatility of the detail similarity of the image generated in the current monitoring period is normal and does not make any adjustments; if BD>bd, the volatility analysis unit determines that the volatility of the detail similarity of the image generated in the current monitoring period is abnormal, and adjusts the optimization of the image generation scheme analysis process for the next monitoring period, and sets the adjusted preset abnormality ratio to b', where Gg is the detail similarity of the g-th generated image in the monitoring period.

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