Enhancement methods, apparatus, systems and storage media for digital prints

Abstract

This application discloses a method, apparatus, system, and storage medium for enhancing the quality of digital printed materials, relating to the field of digital enhancement technology. The method includes: acquiring a printed image of the material to be enhanced; segmenting and extracting multiple printed image features from the printed image; performing matching calculations on each printed image feature and a preset enhanced image matching template to obtain the mean absolute difference and weighted Euclidean distance between the printed image feature and the enhanced image matching template; when the mean absolute difference equals a preset absolute difference threshold and the weighted Euclidean distance equals a preset Euclidean distance threshold, using the printed image feature corresponding to the mean absolute difference and weighted Euclidean distance as the enhanced target image; determining the enhanced position based on the enhanced target image, and performing an enhanced operation on the enhanced position of the enhanced printed material. This application enables accurate matching between the enhanced image and the target image on the printed material, reducing registration errors and improving registration accuracy.

Description

Technical Field

[0001] This application relates to the field of digital enhancement technology, and in particular to a method, apparatus, system and storage medium for enhancing digital printed materials. Background Technology

[0002] Digital enhancement technology refers to a post-printing process that uses digital printing technology to achieve traditional decorative effects such as spot varnishing, matte finish, and embossed hot stamping. It is widely used in cosmetics, tobacco and food, customized gift packaging, cultural and creative products and other fields.

[0003] In related technologies, during the enhancement process of digital printing, it is crucial to ensure a high degree of consistency between the enhanced image and the target image on the printed material. This places extremely high demands on registration. However, due to the influence of temperature and humidity changes or external forces during transportation or storage after printing, the target image on the printed material may be distorted. Furthermore, this distortion is unpredictable; even within the same batch of printed materials, the target image may exhibit significant differences. If the enhancement operation is still performed according to the preset enhanced image original, the enhanced image will fail to match the target image on the printed material, resulting in substantial registration errors. Therefore, how to accurately match the enhanced image with the target image on the printed material and reduce registration errors is a pressing issue that needs to be discussed and resolved. Summary of the Invention

[0004] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a method, apparatus, system, and storage medium for enhancing digital printed materials, which enables the enhanced image to be accurately matched with the target image on the printed material, reducing registration errors and improving registration accuracy.

[0005] To solve the above-mentioned technical problems, the present invention proposes the following technical solution:

[0006] The first aspect of this application proposes a method for enhancing the efficiency of digital prints, comprising:

[0007] Obtain printed images of enhanced printed materials;

[0008] The printed image is segmented and extracted to obtain multiple printed image features;

[0009] Each printed image feature is matched with a preset enhanced image matching template to obtain the mean absolute difference and weighted Euclidean distance between the printed image feature and the enhanced image matching template.

[0010] When the mean absolute difference is equal to a preset absolute difference threshold and the weighted Euclidean distance is equal to a preset Euclidean distance threshold, the printed image features corresponding to the mean absolute difference and the weighted Euclidean distance are used as the enhancement target image.

[0011] The enhancement location is determined based on the enhancement target image, and the enhancement operation is performed on the enhancement location of the enhanced printed material.

[0012] The digital printing enhancement method according to the first aspect of this application has at least the following beneficial effects: This application can acquire the printed image of the current enhanced printed material in real time, preprocess the printed image to obtain multiple printed image features, and perform matching calculations on each preprocessed printed image feature and a preset enhanced image matching template to obtain printed image features that match the enhanced image matching template. The enhanced position is determined by the printed image features, and the enhanced position of the enhanced printed material is subjected to an enhanced operation, so that the enhanced image and the target image on the printed material are accurately matched, reducing the registration error and improving the registration accuracy.

[0013] According to some embodiments of the first aspect of this application, the preset enhanced image matching template is obtained through the following steps:

[0014] Obtain the original image for enhancement;

[0015] The enhanced original image is preprocessed to obtain the enhanced image features of the enhanced original image;

[0016] An enhanced image matching template is established based on the enhanced image features.

[0017] According to some embodiments of the first aspect of this application, the segmentation and extraction of the printed image to obtain multiple printed image features includes:

[0018] The printed image is segmented to obtain multiple segmented images;

[0019] Perform grayscale transformation on the segmented image to obtain a grayscale image;

[0020] Perform image normalization on the grayscale image to obtain a normalized image;

[0021] The normalized image is subjected to image filtering and denoising operations to obtain the printed image features.

[0022] According to some embodiments of the first aspect of this application, the step of performing matching calculations on each printed image feature and a preset enhanced image matching template to obtain the mean absolute difference and weighted Euclidean distance between the printed image feature and the enhanced image matching template includes:

[0023] The weighted Euclidean distance is calculated based on the pixel value of the first pixel in the enhanced image matching template and the pixel value of the second pixel in the printed image feature; wherein the first pixel and the second pixel are in the same position in the image matrix;

[0024] The mean absolute difference is calculated based on the number of rows of the printed image features, the number of columns of the printed image features, the pixel value of the first pixel, and the pixel value of the second pixel.

[0025] According to some embodiments of the first aspect of this application, the step of determining the enhancement position based on the enhancement target image and performing an enhancement operation on the enhancement position of the enhanced printed material includes:

[0026] Based on the target image for enhancement, homography transformation is performed on the original image for enhancement to obtain an optimized image for enhancement.

[0027] The optimized and enhanced image is subjected to image rasterization processing to obtain dot matrix data;

[0028] Determine the enhancement location based on the enhancement target image;

[0029] The enhancement operation is performed on the enhancement position of the enhancement print based on the dot matrix data.

[0030] According to some embodiments of the first aspect of this application, the step of performing homography transformation on the original image for enhancement based on the enhancement target image to obtain an optimized enhancement image includes:

[0031] The second homography transformation parameters are obtained by calculating the enhanced target image and the enhanced original image according to the random sampling consistency algorithm;

[0032] The original image for enhancement is subjected to homography transformation according to the second homography transformation parameters to obtain an optimized image for enhancement.

[0033] According to some embodiments of the first aspect of this application, the step of calculating the second homography transformation parameters by means of a random sampling consistency algorithm on the enhanced target image and the enhanced original image includes:

[0034] By comparing the position matrix information of the enhancement original image and the enhancement target image, the feature corner points in the enhancement original image that correspond one-to-one with the enhancement target image are obtained, and a feature corner point sample set is obtained.

[0035] At least five feature corner points with different locations are selected from the feature corner point sample set to obtain the first feature corner point group;

[0036] The first homography transformation parameters and the initial homography transformation model are calculated based on the first set of feature corner points.

[0037] Based on the initial homography transformation model, multiple second feature corner points are subjected to homography transformation to obtain multiple transformed corner points, wherein the second feature corner points are the remaining feature corner points in the feature corner point sample set excluding the first feature corner point group;

[0038] Calculate the Euclidean distance between the transformed corner point and the enhanced target feature corner point to obtain the corner point Euclidean distance. When the corner point Euclidean distance is greater than a preset corner point Euclidean distance threshold, the second feature corner point corresponding to the corner point Euclidean distance is taken as an interior point. The enhanced target feature corner point is the feature corner point in the enhanced target image that corresponds to the position matrix information of the second feature corner point.

[0039] Calculate the number of interior points. When the number of interior points is greater than or equal to a preset interior point threshold, use the first homography transformation parameter as the second homography transformation parameter.

[0040] A second aspect of this application provides an enhancement device for digital prints, comprising:

[0041] An image acquisition unit is used to acquire images of the printed materials used for enhancing print quality.

[0042] An image processing unit is used to segment and extract the printed image to obtain multiple printed image features;

[0043] The calculation matching unit is used to perform matching calculations on each printed image feature and a preset enhancement image matching template to obtain the mean absolute difference and weighted Euclidean distance between the printed image feature and the enhancement image matching template; when the mean absolute difference is equal to a preset absolute difference threshold and the weighted Euclidean distance is equal to a preset Euclidean distance threshold, the printed image feature corresponding to the mean absolute difference and the weighted Euclidean distance is used as the enhancement target image;

[0044] A digital enhancement unit is used to determine the enhancement position based on the enhancement target image and perform enhancement operation on the enhancement position of the enhanced printed material.

[0045] A third aspect of this application provides an enhancement system for digital prints, comprising:

[0046] At least one memory;

[0047] At least one processor;

[0048] At least one program;

[0049] The at least one program is stored in the at least one memory, and the at least one processor executes the at least one program to implement:

[0050] The method for enhancing digital prints as described in any of the first aspects of this application.

[0051] A fourth aspect of this application provides a computer-readable storage medium storing a computer-executable program for executing:

[0052] The method for enhancing digital prints as described in any of the first aspects of this application.

[0053] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0054] Additional aspects and advantages of this application will become apparent and readily understood in conjunction with the following description of the embodiments, in which:

[0055] Figure 1 The main flowchart of a method for enhancing digital prints according to an embodiment of this application;

[0056] Figure 2 A flowchart for establishing an enhanced image matching template is provided as an embodiment of this application;

[0057] Figure 3 A flowchart for obtaining multiple printed image features is provided as an embodiment of this application;

[0058] Figure 4 A flowchart illustrating the matching calculation of each printed image feature and a preset enhanced image matching template, provided as an embodiment of this application;

[0059] Figure 5 A flowchart illustrating the enhancement operation of the enhancement location on the enhancement printhead, provided as an embodiment of this application;

[0060] Figure 6 A flowchart illustrating homography transformation of an enhancement original image based on an enhancement target image, provided as an embodiment of this application;

[0061] Figure 7 A flowchart illustrating the calculation of the enhancement target image and the enhancement original image according to a random sampling consensus algorithm, provided as an embodiment of this application;

[0062] Figure 8 A block diagram of a digital printing enhancement device provided in one embodiment of this application;

[0063] Figure 9 A block diagram of a digital printing enhancement system provided in one embodiment of this application. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0065] It should be noted that although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown in the flowchart. The terminology in the specification, claims, and the foregoing figures is used to distinguish similar objects and is not necessarily used to describe a specific order or sequence.

[0066] In the description of this application, the use of "first" and "second" is for the purpose of distinguishing technical features only, and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or the order of the technical features indicated.

[0067] In the description of this application, unless otherwise expressly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0068] Digital enhancement technology refers to a post-press process that uses digital printing technology to achieve traditional decorative effects on printed materials, such as spot varnishing, matte finish, and embossed hot stamping. It is widely used in cosmetics, tobacco and food products, customized gift packaging, and cultural and creative products. In the enhancement process of digital printing, it is crucial to ensure a high degree of consistency between the enhanced image and the target image on the printed material. This places extremely high demands on registration. However, due to the influence of temperature and humidity changes or external forces during transportation or storage after printing, the target image on the printed material may be deformed. This deformation is unpredictable; even within the same batch of printed materials, the target image may vary significantly. If the enhancement operation is performed according to the preset enhanced image original, the enhanced image will fail to match the target image on the printed material, resulting in significant registration errors. How to accurately match the enhanced image with the target image on the printed material and reduce registration errors is a problem that urgently needs to be discussed and solved.

[0069] Based on this, the digital printing enhancement method of this application enables the enhanced image to be accurately matched with the target image on the printing, reducing registration error and improving registration accuracy.

[0070] Reference Figure 1 In the first aspect, embodiments of this application provide a method for enhancing the efficiency of digital prints, including but not limited to steps S110, S120, S130, S140, and S150.

[0071] S110, Obtain the printed image of the enhanced printed material;

[0072] S120, segment and extract the printed image to obtain multiple printed image features;

[0073] S130, perform matching calculations on each printed image feature and the preset enhanced image matching template to obtain the mean absolute difference and weighted Euclidean distance between the printed image feature and the enhanced image matching template;

[0074] S140, when the mean absolute difference is equal to the preset absolute difference threshold and the weighted Euclidean distance is equal to the preset Euclidean distance threshold, the printed image features corresponding to the mean absolute difference and the weighted Euclidean distance are used as the enhancement target image.

[0075] S150: Determine the enhancement location based on the enhancement target image, and perform enhancement operation on the enhancement location of the enhanced printed material.

[0076] It should be noted that this application can acquire the printed image of the current enhanced printed material in real time, preprocess the printed image to obtain multiple printed image features, and perform matching calculations on each of the preprocessed printed image features and a preset enhanced image matching template to obtain printed image features that match the enhanced image matching template. The enhanced position is determined by the printed image features, and the enhanced operation is performed on the enhanced position of the enhanced printed material, so that the enhanced image and the target image on the printed material are accurately matched, reducing the registration error and improving the registration accuracy.

[0077] According to one embodiment of this application, firstly, an image of the enhanced printed material needs to be acquired using an image acquisition device to obtain a printed material image. The enhanced printed material is a substrate that has undergone a series of printing processes and requires digital enhancement. After obtaining the printed material image, to facilitate subsequent matching operations, the printed material image needs to be segmented and extracted, breaking it down into multiple printed material image features. Each printed material image feature represents the image features of a different region of the printed material image. After obtaining the printed material image features, to reduce registration errors, it is also necessary to perform matching calculations between the printed material image features and a pre-imported enhanced image matching template to obtain the printed material image feature with the highest matching degree to the enhanced image matching template. Specifically, the detailed operation for matching printed image features and enhanced image matching templates is as follows: First, calculate the mean absolute difference between printed image features and enhanced image matching templates, and determine whether the mean absolute difference is equal to the preset absolute difference threshold; if the mean absolute difference is greater than the absolute difference threshold, the acquired printed image needs to be re-segmented and extracted, and the mean absolute difference between printed image features and enhanced image matching templates needs to be recalculated until the mean absolute difference is equal to the absolute difference threshold; if the mean absolute difference is equal to the absolute difference threshold, the weighted Euclidean distance between printed image features and enhanced image matching templates is calculated. After calculating the weighted Euclidean distance, it is necessary to determine whether the weighted Euclidean distance is equal to the preset Euclidean distance threshold. If the weighted Euclidean distance is greater than the Euclidean distance threshold, the acquired printed image needs to be re-segmented and extracted, and the mean absolute difference and weighted Euclidean distance between the printed image features and the matching template of the augmented image need to be recalculated until the mean absolute difference equals the absolute difference threshold and the weighted Euclidean distance equals the Euclidean distance threshold. If the weighted Euclidean distance equals the Euclidean distance threshold, the printed image features corresponding to the current mean absolute difference and weighted Euclidean distance are used as the augmented target image.

[0078] Furthermore, after confirming the target image for enhancement, it is necessary to determine the enhancement position based on the positional information of the target image and perform enhancement operations on the corresponding enhancement position of the printed material. This application accurately determines the enhancement position by scanning the printed material in real time and matching the printed image of the enhanced material with the enhancement image matching template, thereby reducing registration errors and improving registration accuracy.

[0079] Reference Figure 2 In the first aspect, embodiments of this application provide a method for enhancing the efficiency of digital prints, including but not limited to steps S210, S220, and S230.

[0080] S210, Obtain the enhanced original image;

[0081] S220, preprocess the original image to obtain the enhanced image features of the original image;

[0082] S230, Establish an enhancement image matching template based on the enhancement image features.

[0083] According to one embodiment of this application, a preset enhancement image matching template is obtained through the following steps: Before acquiring the printed image of the enhancement printed material, it is also necessary to acquire the enhancement original image, scan and import the enhancement original image, and preprocess the imported enhancement original image after importing it to obtain enhancement image features of the enhancement original image. Enhancement image features refer to one or more features that can describe the enhancement original image, including but not limited to grayscale histograms, edge features, texture features, and color features. After obtaining multiple enhancement image features, an image matching template is established using the enhancement image features of the enhancement original image to facilitate subsequent matching of printed image features. Specifically, this application avoids frequent scanning of the enhancement original image during subsequent matching of printed image features by establishing an image matching template before acquiring the printed image of the enhancement printed material, thus improving matching efficiency.

[0084] Reference Figure 3 In the first aspect, embodiments of this application provide a method for enhancing the efficiency of digital prints, including but not limited to steps S310, S320, S330, and S340.

[0085] S310, Perform image segmentation on the printed image to obtain multiple segmented images;

[0086] S320: Perform grayscale transformation on the segmented image to obtain a grayscale image;

[0087] S330 performs image normalization on the grayscale image to obtain a normalized image;

[0088] S340 performs image filtering and denoising operations on the normalized image to obtain the features of the printed image.

[0089] According to one embodiment of this application, after acquiring the printed image of the enhanced printed material, preprocessing is required to facilitate subsequent matching of the printed image and the enhanced image with a matching template. First, image segmentation is performed on the printed image, dividing it into several non-overlapping regions based on features such as grayscale, color, spatial texture, and geometric shape, resulting in multiple segmented images. These features exhibit consistency or similarity within the same region, but show significant differences between different regions. After obtaining the segmented images, grayscale transformation is performed to improve their quality, allowing them to display more details, resulting in grayscale images. After obtaining the grayscale images, image normalization is performed to adjust the feature values ​​to a similar range, resulting in normalized images. Finally, image filtering and denoising are performed on the normalized images to remove excess noise, yielding printed image features. These printed image features, compared to the initial printed image, better represent the image's characteristics.

[0090] Reference Figure 4 In the first aspect, embodiments of this application provide a method for enhancing the efficiency of digital prints, including but not limited to steps S410 and S420.

[0091] S410, calculate the weighted Euclidean distance based on the pixel value of the first pixel in the enhanced image matching template and the pixel value of the second pixel in the printed image feature; wherein the first pixel and the second pixel are in the same position in the image matrix;

[0092] S420: The mean absolute difference is calculated based on the number of rows of the printed image features, the number of columns of the printed image features, the pixel value of the first pixel, and the pixel value of the second pixel.

[0093] According to one embodiment of this application, in order to improve the matching accuracy of printed image features and enhanced image matching templates, this application uses Euclidean distance and mean absolute difference to form a dual constraint. First, the formula for calculating the weighted Euclidean distance is introduced. The weighted Euclidean distance formula is:

[0094] d=sqrt(sum((T(i,j)-I(i,j))^2));

[0095] Where d represents the weighted Euclidean distance; sqrt represents the function for calculating the positive square root; sum represents the function for summation; T(i,j) represents the pixel value of the first pixel in the enhanced image matching template; I(i,j) represents the pixel value of the second pixel in the printed image feature; and i and j represent the positions of the pixels in the image matrix.

[0096] Furthermore, the formula for calculating the mean absolute difference is introduced here. The formula for the mean absolute difference is:

[0097] MAD=(1 / (m*n))*sum(|T(i,j)-I(i,j)|);

[0098] Where MAD represents the mean absolute difference; m and n represent the number of rows and columns of the printed image features; sum represents the summation function; T(i,j) represents the pixel value of the first pixel in the enhanced image matching template; I(i,j) represents the pixel value of the second pixel in the printed image features; and i and j represent the position of the pixel in the image matrix.

[0099] Correspondingly, the formula for forming a double constraint by using the Euclidean distance method and the mean absolute difference method is as follows:

[0100] d = d min

[0101] MAD = MAD min ;

[0102] Where d represents the weighted Euclidean distance; d min Indicates the preset Euclidean distance threshold; MAD represents the mean absolute difference; MAD min This represents the preset absolute difference threshold.

[0103] Reference Figure 5 In the first aspect, embodiments of this application provide a method for enhancing the efficiency of digital prints, including but not limited to steps S510, S520, S530, and S540.

[0104] S510, perform homography transformation on the original image of the enhancement work based on the enhancement target image to obtain the optimized enhancement image;

[0105] S520 performs image rasterization processing on the optimized and enhanced image to obtain dot matrix data;

[0106] S530, determine the enhancement location based on the enhancement target image;

[0107] S540 performs enhancement operations on the enhancement positions of the enhanced printed material based on dot matrix data.

[0108] According to one embodiment of this application, after confirming the enhancement target image, it is necessary to optimize the enhancement target image using homography transformation to describe the positional mapping relationship of objects in the enhancement target image between the world coordinate system and the pixel coordinate system, thereby obtaining an optimized enhancement image. Then, the optimized enhancement image undergoes image rasterization processing to obtain bitmap data, which is a bitmap composed of individual points called pixels (image elements). These points can be arranged and colored differently to form patterns. Simultaneously, it is also necessary to determine the enhancement position (the enhancement position being the area to be processed and covered during subsequent enhancement operations) based on the enhancement target image. Finally, enhancement operations are performed on the enhancement position of the enhanced printed material based on the bitmap data. This accurate determination of the enhancement position reduces registration errors and improves registration accuracy.

[0109] Reference Figure 6 In the first aspect, embodiments of this application provide a method for enhancing the efficiency of digital prints, including but not limited to steps S610 and S620.

[0110] S610, calculate the second homography transformation parameters by using the random sampling consistency algorithm on the target image and the original image for enhancement;

[0111] S620, the original image for enhancement is subjected to homography transformation according to the second homography transformation parameters to obtain the optimized image.

[0112] According to one embodiment of this application, in order to improve the accuracy of homography transformation, this application further calculates the target image and the original image for enhancement based on the Random Sample Consensus Algorithm (RANSAC) to optimize and obtain a more suitable second homography transformation parameter. Then, based on the optimized second homography transformation parameter, the original image for enhancement undergoes homography transformation to obtain an optimized enhanced image. Specifically, this application improves the accuracy of homography transformation by using the optimized homography transformation parameter.

[0113] Furthermore, the homography transformation formula for performing homography transformation on the enhanced original image based on the homography transformation parameters is as follows:

[0114] x′=(h11*x+h12*y+h13) / (h31*x+h32*y+h33);

[0115] y′=(h21*x+h22*y+h23) / (h31*x+h32*y+h33);

[0116] Where h11, h12, h13, h21, h22, h23, h31, h32 and h33 all represent the second homography transformation parameters; each coordinate point on the enhanced original image is (x, y), and each coordinate point on the optimized enhanced image is (x′, y′).

[0117] Reference Figure 7 In the first aspect, embodiments of this application provide a method for enhancing the efficiency of digital prints, including but not limited to steps S710, S720, S730, S740, S750, and S760.

[0118] S710, compare the position matrix information of the original image and the target image to obtain the feature corner points in the original image and the target image that correspond one-to-one, and obtain the feature corner point sample set;

[0119] S720, Select at least five feature corner points with different locations from the feature corner point sample set to obtain the first feature corner point group:

[0120] S730, the first homography transformation parameters and the initial homography transformation model are calculated based on the first characteristic corner point group;

[0121] S740, according to the initial homography transformation model, multiple second feature corner points are subjected to homography transformation to obtain multiple transformed corner points, wherein the second feature corner points are the remaining feature corner points in the feature corner point sample set excluding the first feature corner point group;

[0122] S750, calculate the Euclidean distance between the transformed corner point and the feature corner point of the enhanced target, and obtain the corner point Euclidean distance. When the corner point Euclidean distance is greater than the preset corner point Euclidean distance threshold, the second feature corner point corresponding to the corner point Euclidean distance is taken as the interior point. Here, the feature corner point of the enhanced target is the feature corner point corresponding to the position matrix information of the second feature corner point in the enhanced target image.

[0123] S760, calculate the number of interior points. When the number of interior points is greater than or equal to the preset interior point threshold, use the first homography transformation parameter as the second homography transformation parameter.

[0124] According to one embodiment of this application, the process of optimizing the homography transformation parameters to obtain a more suitable second homography transformation parameter is described here: First, the enhancement target image is used as an optimization sample, and the position matrix information of the enhancement original image and the enhancement target image are compared. Some feature corner points in the enhancement original image and the enhancement target image have the same position matrix. At this point, it is necessary to obtain these feature corner points in the enhancement original image that correspond one-to-one with those in the enhancement target image, thus obtaining a feature corner point sample set. After obtaining the feature corner point sample set, at least five feature corner points are selected from the feature corner point sample set to obtain the first feature corner point group. It is necessary to determine that these selected feature corner points are located in different regions to ensure the diversity of the selected feature corner points. After obtaining the first set of feature corner points, the first homography transformation parameters and the initial homography transformation model are calculated based on the selected feature corner points. The initial homography transformation model is then used to perform homography transformation on the remaining feature corner points in the feature corner point sample set, excluding the first set of feature corner points, to obtain transformed corner points. The Euclidean distance between the transformed corner points and the enhanced target feature corner points is calculated to obtain the corner point Euclidean distance. If the corner point Euclidean distance is greater than or equal to a preset corner point Euclidean distance threshold, the second feature corner point corresponding to that corner point Euclidean distance is taken as an interior point. The Euclidean distances between other transformed corner points and enhanced target feature corner points are then calculated. The calculated corner point Euclidean distances are judged, and the above steps are repeated until the Euclidean distances between all transformed corner points and enhanced target feature corner points corresponding to all second feature corner points are calculated. Finally, the number of all interior points is calculated. Furthermore, when the number of inliers is greater than or equal to the preset inlier threshold, the current first homography transformation parameter is used as the second homography transformation parameter. If the number of inliers is less than the preset inlier threshold, it is determined that the current first homography transformation parameter does not meet the preset requirements. It is necessary to re-compare the position matrix information of the original image and the target image to obtain a feature corner point sample set, and recalculate the number of inliers based on the feature corner point sample set until the number of inliers is greater than or equal to the inlier threshold.

[0125] Reference Figure 8 , Figure 8The first part is a block diagram of a digital printing enhancement device according to one embodiment of this application. In a second aspect, this application provides a digital printing enhancement device, comprising: an image acquisition unit for acquiring a printed image of the enhanced printing; an image processing unit for segmenting and extracting the printed image to obtain multiple printed image features; a calculation matching unit for performing matching calculations on each printed image feature and a preset enhancement image matching template to obtain the mean absolute difference and weighted Euclidean distance between the printed image features and the enhancement image matching template; when the mean absolute difference equals a preset absolute difference threshold and the weighted Euclidean distance equals a preset Euclidean distance threshold, the printed image feature corresponding to the mean absolute difference and weighted Euclidean distance is used as the enhancement target image; and a digital enhancement unit for determining the enhancement position based on the enhancement target image and performing enhancement operations on the enhancement position of the enhanced printing.

[0126] Thirdly, referring to Figure 9 This application provides an efficiency-enhancing system for digital prints, comprising:

[0127] At least one memory 200;

[0128] At least one processor 100;

[0129] At least one program;

[0130] The program is stored in memory 200, and processor 100 executes at least one program to achieve:

[0131] The method for enhancing digital prints as described in any embodiment of the first aspect of this application.

[0132] The processor 100 and the memory 200 can be connected via a bus or other means.

[0133] Memory 200, as a non-transitory readable storage medium, can be used to store non-transitory software instructions and non-transitory instructions. Furthermore, memory 200 may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. It is understood that memory 200 may optionally include remotely located memories 200 relative to processor 100, which can be connected to processor 100 via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0134] The processor 100 executes non-transitory software instructions, commands, and signals stored in the memory 200 to perform various functional applications and data processing, thereby realizing a digital printing efficiency enhancement method according to the first aspect embodiment described above.

[0135] The non-transitory software instructions required to implement the digital print enhancement system of the above embodiments are stored in the memory 200. When executed by the processor 100, the digital print enhancement method of the first aspect of this application is executed, for example, the method described above. Figure 1 Method steps S110 to S150 in the text Figure 2 Method steps S210 to S230 in the text Figure 3 Method steps S310 to S340 in the text Figure 4 Method steps S410 to S420 in the text Figure 5 Method steps S510 to S540 in the text Figure 6 Method steps S610 to S620 in the text Figure 7 Method steps S710 to S760.

[0136] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable signals for performing:

[0137] An enhancement method for digital prints, as described in any embodiment of the first aspect of the application.

[0138] For example, execute the above description. Figure 1 Method steps S110 to S150 in the text Figure 2 Method steps S210 to S230 in the text Figure 3 Method steps S310 to S340 in the text Figure 4 Method steps S410 to S420 in the text Figure 5 Method steps S510 to S540 in the text Figure 6 Method steps S610 to S620 in the text Figure 7 Method steps S710 to S760.

[0139] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0140] Based on the above description of the embodiments, those skilled in the art will understand that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable signals, data structures, instruction modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible by a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable signals, data structures, instruction modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0141] The embodiments of this application have been described in detail above with reference to the accompanying drawings. However, this application is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of this application.

Claims

1. A method of boosting a digital print, characterized by, The method comprises the following steps: obtaining an image of a printing product; segmenting and extracting the image of the printing product to obtain a plurality of printing product image features; respectively matching each printing product image feature and a preset matching template of an efficiency image to obtain an average absolute difference and a weighted Euclidean distance between the printing product image feature and the matching template of the efficiency image, wherein the matching template of the efficiency image is obtained by the following steps: obtaining an efficiency original manuscript image; preprocessing the efficiency original manuscript image to obtain efficiency image features of the efficiency original manuscript image; and establishing the matching template of the efficiency image according to the efficiency image features; when the average absolute difference is equal to a preset absolute difference threshold value and the weighted Euclidean distance is equal to a preset Euclidean distance threshold value, taking a printing product image feature corresponding to the average absolute difference and the weighted Euclidean distance as an efficiency target image; determining an efficiency position according to the efficiency target image and performing an efficiency operation on the efficiency position of the efficiency printing product, wherein the operation of determining the efficiency position according to the efficiency target image and performing the efficiency operation on the efficiency position of the efficiency printing product comprises the following steps: performing a homography change on the efficiency original manuscript image according to the efficiency target image to obtain an optimized efficiency image; performing image rasterization processing on the optimized efficiency image to obtain dot array data; determining the efficiency position according to the efficiency target image; and performing the efficiency operation on the efficiency position of the efficiency printing product according to the dot array data.

2. The method of boosting a digital print according to claim 1, characterized in that, The step of segmenting and extracting the image of the printing product to obtain a plurality of printing product image features comprises the following steps: performing image segmentation on the image of the printing product to obtain a plurality of segmented images; performing a gray scale change on the segmented images to obtain gray scale images; performing image normalization on the gray scale images to obtain normalized images; performing image filtering and denoising on the normalized images to obtain printing product image features.

3. The method of boosting a digital print according to claim 1, wherein, The step of respectively matching each printing product image feature and a preset matching template of an efficiency image to obtain an average absolute difference and a weighted Euclidean distance between the printing product image feature and the matching template of the efficiency image comprises the following steps: calculating a weighted Euclidean distance according to a pixel value of a first pixel in the matching template of the efficiency image and a pixel value of a second pixel in the printing product image feature, wherein the first pixel and the second pixel have the same position in an image matrix; calculating an average absolute difference according to a number of rows of the printing product image feature, a number of columns of the printing product image feature, the pixel value of the first pixel and the pixel value of the second pixel.

4. The method of potentiating a digital print according to claim 1, wherein, The step of performing a homography change on the efficiency original manuscript image according to the efficiency target image to obtain an optimized efficiency image comprises the following steps: calculating a second homography transformation parameter according to a random sample consensus algorithm and the efficiency target image and the efficiency original manuscript image; performing a homography change on the efficiency original manuscript image according to the second homography transformation parameter to obtain the optimized efficiency image.

5. The method of boosting a digital print according to claim 4, characterized in that, The step of calculating a second homography transformation parameter according to a random sample consensus algorithm and the efficiency target image and the efficiency original manuscript image comprises the following steps: Corresponding feature corner points in the enhanced original manuscript image and the enhanced target image are obtained by comparing the position matrix information of the enhanced original manuscript image and the enhanced target image, and a feature corner point sample set is obtained; At least five feature corner points with different positions are selected from the feature corner point sample set to obtain a first feature corner point group; First homographic transformation parameters and an initial homographic transformation model are calculated based on the first feature corner point group; A plurality of transformed corner points are obtained by performing homographic transformation on a plurality of second feature corner points based on the initial homographic transformation model, wherein the second feature corner points are the remaining feature corner points in the feature corner point sample set except the first feature corner point group; An angle point Euclidean distance is calculated between the transformed corner points and the enhanced target feature corner points, wherein the enhanced target feature corner points are the feature corner points in the enhanced target image corresponding to the position matrix information of the second feature corner points, and when the angle point Euclidean distance is greater than a preset angle point Euclidean distance threshold, the second feature corner point corresponding to the angle point Euclidean distance is taken as an inlier; When the number of inliers is greater than or equal to a preset inlier threshold, the first homographic transformation parameters are taken as second homographic transformation parameters.

6. A digital print enhancing device characterized by, It comprises: An image acquisition unit is configured to acquire a printed image of an enhanced print; An image processing unit is configured to segment and extract the printed image to obtain a plurality of printed image features; A calculation and matching unit is configured to respectively perform matching calculation on each printed image feature and a preset enhanced image matching template to obtain the average absolute difference and weighted Euclidean distance between the printed image features and the enhanced image matching template; when the average absolute difference is equal to a preset absolute difference threshold and the weighted Euclidean distance is equal to a preset Euclidean distance threshold, the printed image feature corresponding to the average absolute difference and the weighted Euclidean distance is taken as an enhanced target image, wherein the preset enhanced image matching template is obtained by the following steps: obtaining an enhanced original manuscript image; preprocessing the enhanced original manuscript image to obtain enhanced image features of the enhanced original manuscript image; and establishing an enhanced image matching template based on the enhanced image features; A digital enhancement unit is configured to determine an enhanced position based on the enhanced target image and perform enhancement operation on the enhanced position of the enhanced print, which comprises: performing homographic transformation on the enhanced original manuscript image based on the enhanced target image to obtain an optimized enhanced image; performing image rasterization processing on the optimized enhanced image to obtain dot array data; determining the enhanced position based on the enhanced target image; and performing enhancement operation on the enhanced position of the enhanced print based on the dot array data.

7. A system for enhancing a digital print, characterized by, It comprises: At least one memory; At least one processor; At least one program; The program is stored in the memory, and the processor executes at least one of the programs to implement: The synergistic method of digital printing according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer executable signals for performing: The synergistic method of digital printing according to any one of claims 1 to 5.

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