Digital printed image registration method fusing feature registration and deformation optimization
By integrating feature registration with deformation optimization, adaptively introducing reduction factors and nonlinear brightness compensation mechanisms, the accuracy and efficiency issues caused by deformation during the printing process are resolved, and high-precision digital printing image registration is achieved.
Patent Information
- Application Number
- CN202411714198.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-27
- Publication Date
- 2025-10-10
- Estimated Expiration
- 2044-11-27
AI Technical Summary
Existing digital printing image registration methods struggle to simultaneously ensure high precision and efficiency when faced with elastic deformation caused by factors such as equipment vibration and paper instability during the printing process. This is especially true when detecting subtle print streak defects, where brightness differences affect registration accuracy.
A method of fusion feature registration and deformation optimization is adopted. Global registration is performed by adaptively introducing a reduction factor. Local deformation optimization is performed by combining the deformation estimation algorithm and nonlinear brightness compensation mechanism to improve the registration accuracy and efficiency.
It effectively corrects the rigid and elastic deformation of printed images, improves the registration accuracy of digital printed images, overcomes the constraints of brightness differences on deformation estimation algorithms, and improves registration efficiency.
Smart Images
Figure CN119559227B_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of image registration, and relates to a high-precision registration method for digital printed images, and in particular to a digital printed image registration method that integrates feature registration and deformation optimization. Background Art
[0002] Image registration is the process of matching two or more images acquired at different times, using different sensors, or under different conditions. It has been widely used in fields such as remote sensing data analysis, computer vision, and image processing, and is a hot research area for correcting image transformations. Depending on the type of image deformation, image registration methods can be divided into rigid and non-rigid registration. Image registration techniques are primarily categorized as spatial feature-based and grayscale-based. Spatial feature-based image registration methods are widely used. However, when the number of feature points extracted from an image is large, while this improves registration accuracy, it also significantly reduces registration speed. Conversely, reducing the number of feature points to increase speed inevitably sacrifices registration accuracy. Image registration methods based on grayscale information require analyzing the grayscale information of the entire image, resulting in high computational complexity and low efficiency. In recent years, feature extraction-based registration algorithms have become a research hotspot and a commonly used method in the field. The displacement field solved in registration is similar to the velocity field solved in optical flow. In recent years, optical flow computing technology has developed rapidly, and image registration methods based on optical flow models have been able to obtain high-quality registration results. The optical flow field is a registration method based on grayscale information, which analyzes the grayscale of the image.
[0003] In the field of digital printing, accurate registration of printed images with reference images is crucial for surface defect detection in printed products. Currently, the mainstream registration methods for printed images are feature-extraction-based registration methods such as SIFT and SURF. However, during the printing process, factors such as equipment vibration and paper instability cause the captured printed image to exhibit slight elastic and other rigid deformations, which feature-extraction registration algorithms like SIFT struggle to account for. While the optical flow-based Active Demons deformation estimation algorithm can account for elastic deformation, it is sensitive to brightness. When detecting specific defects, such as subtle streaks caused by printhead issues resulting from interruptions in the printing process, prepress images are often used as reference images. The captured printed image undergoes complex mapping between the printing equipment and the image acquisition device, so its brightness inevitably differs from the prepress image. Therefore, using deformation estimation algorithms to register the printed image with the prepress image inevitably compromises registration accuracy.
[0004] In order to improve the registration accuracy of digital printed images as much as possible and correct the elastic deformation of printed images caused by equipment vibration, paper instability, etc., the present invention proposes a digital printed image registration method that integrates feature registration and deformation optimization. Summary of the Invention
[0005] In response to the problems existing in the prior art, the present invention provides a digital printing image registration method that integrates feature registration and deformation optimization.
[0006] The technical solutions of the present invention are as follows:
[0007] A digital printing image registration method that integrates feature registration and deformation optimization includes: first, using a feature extraction algorithm to achieve global registration between the prepress original image and the printed image; then, combining it with a deformation estimation algorithm to perform local deformation optimization to improve registration accuracy and address elastic deformation; and, in the global registration stage, adaptively introducing a reduction factor to improve registration efficiency; and, in the local deformation optimization stage, introducing a nonlinear brightness compensation mechanism to overcome the constraints imposed by brightness differences on the deformation estimation algorithm.
[0008] The specific steps include:
[0009] The first step is to adaptively introduce a reduction factor of 1 / n and obtain the corresponding reduced affine matrix according to the reduction factor. and the corresponding magnified affine matrix
[0010] In the second step, after resizing the prepress original image O and the printed image P by reducing the affine matrix S, the printed image P and the prepress original image O are globally registered using a feature extraction algorithm.
[0011] In the third step, the global registration result described in the second step is locally deformed and optimized using a deformation estimation algorithm, while a nonlinear brightness compensation mechanism is introduced.
[0012] Furthermore, the specific steps of adaptively introducing the reduction factor in the first step are as follows:
[0013] 1.1) Select the smaller of the length and width of the original image before printing, and divide 240 by this value. The result is the initial reduction factor.
[0014] 1.2) introducing the reduction factor, resizing the printed image and the prepress original image, and performing global registration of the resized printed image and the prepress original image;
[0015] 1.3) Calculate the correlation coefficient CC value between the global registration result and the prepress original image, and set a threshold (usually 0.9). If the CC value exceeds the set threshold, output the global registration result; if it does not exceed the set threshold, double the reduction factor, and then repeat steps 1.2) and 1.3) until the CC value exceeds the set threshold, and output the global registration result that meets the requirements.
[0016] Specifically, the correlation coefficient The value of CC ranges from -1 to 1, where 1 represents a perfect match, 0 represents no correlation, and -1 represents a completely negative correlation; where N is the total number of pixels, O i is the pixel value of the i-th pixel in the original image before printing, is the average value of the intensity of all pixels in the original image before printing, G i is the intensity value of the corresponding pixel i in the registration image, is the average intensity value of all pixels in the registered image.
[0017] Furthermore, the specific steps of the second step are as follows:
[0018] 2.1) Resize the pre-press original image O and the printed image P using the reduced affine matrix S, and at the same time, update the printed image spatial position matrix to A ′ =SA, A is the spatial position matrix of the printed image before resizing;
[0019] 2.2) Extracting feature points from the resized prepress image and the printed image using a feature extraction algorithm, locating the feature points, determining their orientation, describing them, and performing feature matching;
[0020] 2.3) Obtain the affine transformation coefficients of the resized pre-press original image and the printed image through random sample consensus (RANSAC) to obtain the applied transformation matrix H;
[0021] 2.4) Through matrix calculation, the printed image spatial position matrix T = UHA′ after global registration is obtained. According to A in 2.1) ′ , we can get T = UHSA, so we can get the final application transformation matrix Finally, the printed image P is affine transformed by M to obtain the global registration result G;
[0022] Furthermore, the deformation estimation algorithm in the third step is the Active Demons algorithm, which specifically refers to:
[0023] The global registration result G in the second step is regarded as a frame of the prepress original image O after slight movement and deformation. The brightness value of the point (x, y) on the image O at time t is O(x, y, t). According to the motion process: O(x, y, t) = O(x + Δx, y + Δy, t + Δt), the Taylor expansion on the right is:
[0024] G is considered as a frame of image after O is slightly moved and deformed. dx, dy, and dt are very small, and the ε after Taylor expansion can be ignored: remember, but,
[0025] Specifically, the deformation estimation algorithm can be used to estimate the gradient information of the pre-press original image O. The difference information O between the prepress original image O and the global registration result G t To calculate the offset between the original image before printing and the printed image The O t The difference between O and G: O t =diff=GO, in the present invention, a nonlinear brightness compensation mechanism is introduced to eliminate the error in deformation estimation caused by the nonlinear brightness difference between the pre-press original image O and the global registration result G.
[0026] Furthermore, after the nonlinear brightness compensation mechanism is introduced, the third step is specifically as follows:
[0027] 3.1) The pixels in the prepress original image O are grouped into larger regions with similar properties through the superpixel segmentation SLIC algorithm. Brightness compensation is performed on the regions with similar properties. Specifically, brightness compensation is performed on the prepress original image based on the difference information diff between the prepress original image O and the global registration result G: O_c(x,y)=O(x,y)+δ*diff avg (x,y), where the diff avg (x, y) is the average pixel value of the superpixel region where the point (x, y) is located on diff, δ*diff avg (x,y) is the brightness compensation value of point (x,y), δ is the compensation coefficient, and the value range is 0.8~1.2;
[0028] 3.2) Obtain the inter-frame difference O_c between the original pre-press image O_c after brightness compensation and the global registration result G t =diff c =G-O_c=diff-δ*diff avg ; Gradient information of the prepress original image after brightness compensation
[0029] 3.3) According to the shape deformation estimation algorithm, O_c(x, y, t) = O_c(x+Δx, y+Δy, t+Δt) is expanded by Taylor, Thus,
[0030] 3.4) The gradient of the global registration result is added to the calculation of the offset, and the offset (U x , U y ) of each pixel point is obtained.
[0031]
[0032] Wherein, the is the result calculated after adding brightness compensation, and is the value of (δ*diff avg -diff) at the point (x, y), O_c x and O_c y are the gradients of the pre-press original image at the point (x, y) in the x direction and the y direction after adding brightness compensation, G x and G y are the gradients of the global registration image at the point (x, y) in the x direction and the y direction, and α is a normalization factor for controlling the size of the coordinate offset. The larger α is, the smaller the offset is, and vice versa.
[0033] Further, the global registration result is locally deformed and optimized according to the offset (U x , U y ), so as to obtain a final registration result image.
[0034] The application provides a digital printing image registration method combining feature registration and deformation optimization, which can improve the registration accuracy of digital printing images, so as to cope with various rigid deformations, elastic deformations and brightness differences between the printing images and the pre-press original images that may occur in the printing process and the process of collecting the printing images.
[0035] A computer readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the digital printing image registration method combining feature registration and deformation optimization as claimed in any one of the above.
[0036] An electronic device, the device comprising:
[0037] one or more processors;
[0038] a memory for storing one or more programs;
[0039] When the one or more programs are executed by the one or more processors, the one or more processors implement the digital printing image registration method integrating feature registration and deformation optimization as described in any one of the above items.
[0040] The beneficial effects of the present invention are as follows:
[0041] 1) In the local deformation optimization stage, the prepress original image is segmented into superpixels. Based on the difference information of the corresponding superpixel areas between the printed image and the prepress original image, a nonlinear brightness compensation mechanism is introduced to overcome the brightness constraint on the deformation estimation algorithm.
[0042] 2) Adaptively introduce a reduction factor in the global registration stage to significantly improve registration efficiency;
[0043] 3) The global registration stage corrects the rigid deformation of the printed image;
[0044] 4) The local deformation optimization stage corrects the elastic deformation of the printed image. BRIEF DESCRIPTION OF THE DRAWINGS
[0045] Figure 1 Schematic diagram of the process of the present invention;
[0046] Figure 2 This is the original pre-press image of Lena;
[0047] Figure 3 Print images for lena;
[0048] Figure 4 Schematic diagram of the process of introducing reduction factor for adaptive operation;
[0049] Figure 5 This is a graph showing the relationship between the reduction factor, the number of feature points, and the global registration time;
[0050] Figure 6 This is the feature extraction and matching result of Lena prepress original image and printed image;
[0051] Figure 7 The global registration result image and its local magnified image;
[0052] Figure 8 This is the SCIL superpixel segmentation map of the lena prepress original image;
[0053] Figure 9 This is the effect diagram of the deformation estimation algorithm without introducing brightness compensation;
[0054] Figure 10 This is the effect diagram of the deformation estimation algorithm with the introduction of brightness compensation. DETAILED DESCRIPTION
[0055] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process. However, the protection scope of the present invention is not limited to the following embodiment.
[0056] This embodiment will use the Lena prepress original image with an image resolution of 7795×3840, such as Figure 2 As shown, the image resolution is 8088×4127 lena printing image, such as Figure 3 As shown. Figure 2 and Figure 3 It can be seen that the printed image has slight elastic deformation and rigid deformation such as scaling, rotation, and translation compared to the pre-press original image, and there is also a certain brightness difference. This embodiment uses the classic correlation coefficient (CC), mean square error (MSE), peak signal-to-noise ratio (PSNR), mutual information (MI), and structural similarity (SSIM) to evaluate the registration effect of the algorithm; at the same time, the obtained registration result map can verify the performance of the registration algorithm. Among these five evaluation indicators, the larger the PSNR and MI values, the better the image registration quality; the closer the SSIM and CC values are to 1, the more similar the images are; MSE is used to detect the stability of the algorithm. The smaller the MSE value, the more stable the proposed algorithm.
[0057] like Figure 1 As shown, in this embodiment, the digital printing image registration method integrating feature registration and deformation optimization specifically includes the following steps:
[0058] First, the reduction factor is adaptively introduced to achieve global registration of the lena prepress original image O and the lena printed image P. The specific steps are as follows:
[0059] 1) Select the smaller value of the length or width of the prepress original image, 3840, and divide 240 by this value. The result is the initial reduction factor: 1 / 16;
[0060] 2) Obtain the corresponding reduced affine matrix according to the reduction factor and the corresponding magnified affine matrix Resize the printed image and the pre-press original image according to the reduced affine matrix S. At the same time, let the spatial position matrix of the printed image P be A, and update it to A after resizing. ′ =SA;
[0061] 3) Global registration of the resized Lena printed image P and the Lena pre-press original image O: In this embodiment, a feature extraction algorithm, the SIFT algorithm (other feature extraction algorithms, such as the SUFR algorithm, may also be used) is used to extract feature points of the resized Lena pre-press original image O and the Lena printed image P, locate the feature points, determine their directions, describe the feature points, and perform feature matching;
[0062] 4) Based on the feature matching results, the affine transformation coefficients of the resized Lena pre-press original image O and the Lena printed image P are obtained through random sample consensus (RANSAC), thereby obtaining the application transformation matrix H;
[0063] 5) Through matrix calculation, the spatial position matrix of the lena printed image after global registration is obtained, T = UHA'. According to A' described in 2), T = UHSA can be obtained. Therefore, the final application transformation matrix can be obtained Finally, the affine transformation of the lena printed image P is performed through M to obtain the global registration result G;
[0064] 6) Calculate the correlation coefficient between the global registration result G and the lena prepress original image O The value of CC ranges from -1 to 1, where 1 represents a perfect match, 0 represents no correlation, and -1 represents a completely negative correlation; where N is the total number of pixels, O i is the pixel value of the i-th pixel in the original image before printing, is the average value of the intensity of all pixels in the original image before printing, G i is the intensity value of the corresponding pixel i in the registration image, is the average intensity value of all pixels in the registered image;
[0065] 7) Set the threshold to 0.9. If the CC value exceeds the set threshold, output the global registration result. If it does not exceed the set threshold, double the reduction factor and repeat steps 2) to 7) until a global registration result G that meets the requirements is output.
[0066] The flow chart of adaptively introducing reduction factor is as follows Figure 4 As shown in the figure, the relationship between different reduction factors and the number of feature points extracted and the global registration time is as follows: Figure 5 As shown in the figure, the smaller the reduction factor, the fewer the number of feature points detected, and the global registration time is significantly reduced. In this embodiment, after the initial reduction factor of 1 / 16 is adaptively introduced, the feature extraction and matching results of the lena prepress original image O and the lena printed image P are as follows: Figure 6As shown in the figure, the correlation coefficient CC value between the global registration result G and the lena prepress original image O is 0.97, which is greater than the set threshold value 0.9. Therefore, the global registration result G is output. The global registration result G and the local enlarged image are shown in the figure. Figure 7 As shown in Figure 3, rigid deformations such as scaling, translation, and rotation have been successfully resolved. However, as can be observed from the local magnified image, elastic deformation has not been corrected.
[0067] Next, the deformation estimation algorithm is used to perform local deformation optimization on the output global registration result G, and a nonlinear brightness compensation mechanism is introduced. The specific steps are as follows:
[0068] The global registration result G is regarded as a frame of the prepress original image O after slight movement and deformation. Assuming that the brightness of G is consistent with that of O, the brightness value of the point (x, y) on the prepress original image O of lena at time t is O(x, y, t).
[0069] According to the motion process: O(x,y,t)=O(x+Δx,y+Δy,t+Δt);
[0070] Taylor expansion on the right:
[0071] Since G is regarded as a frame of O after slight movement and deformation, dx, dy, and dt are very small, and the ε after Taylor expansion can be ignored:
[0072] remember, but,
[0073] Therefore, the deformation estimation algorithm can use the gradient information of the prepress original image O The difference information O between the prepress original image O and the global registration result G t To calculate the offset between the original image before printing and the printed image The O t The difference between O and G: O t =diff=GO.
[0074] The above derivation process is based on the assumption that G is a frame of O after slight movement and deformation, and the brightness is consistent. G is the global registration result, which is slightly deformed relative to O. However, there is a nonlinear brightness difference between O and G, such as Figure 2 、 Figure 3 As shown, this will cause errors in the deformation estimation algorithm. Therefore, a nonlinear brightness compensation mechanism is introduced into the deformation estimation algorithm to solve this problem. The specific steps are as follows:
[0075] 1) Through the SLIC algorithm: initialization of cluster centers, search area restriction, pixel similarity measurement, iterative clustering, post-processing, the pixels in the prepress original image are combined into larger areas with similar properties, and the SCIL superpixel segmentation of the prepress original image O is as follows: Figure 8 As shown, brightness compensation is performed on regions with similar properties. Specifically, brightness compensation is performed on the prepress original image according to the difference information diff between the prepress original image O and the global registration result G:
[0076] O_c(x,y)=O(x,y)+δ*diff avg (x,y)
[0077] The diff avg (x, y) is the average pixel value of the superpixel region where the point (x, y) is located on diff, δ*diff avg (x, y) is the brightness compensation value of point (x, y), δ is the compensation coefficient, and the value range is 0.8 to 1.2. In this embodiment, δ is 1.1;
[0078] 2) Obtain the inter-frame difference between the original pre-press image O_c after brightness compensation and the global registration result G: O_c t =diff c =G-O_c=diff-δ*diff avg ; Gradient information of the prepress original image after brightness compensation:
[0079] 3) According to the deformation estimation algorithm principle, O_c(x,y,t)=O_c(x+Δx,y+Δy,t+Δt), after Taylor expansion, Thus,
[0080] 4) Based on the deformation estimation algorithm, the normalization factor α is added to control the size of the coordinate offset. The larger α is, the smaller the offset is, and vice versa. At the same time, the gradient of the global registration result is added to the offset calculation to obtain the offset of each pixel (U x , U y ), wherein It is the result of calculation after adding brightness compensation, which is (δ*diff avg –diff) at the point (x, y), O_c x and O_c y They are the gradients in the x and y directions at the point (x, y) on the prepress original image after brightness compensation, G x and G yare the gradients in the x and y directions at the point (x, y) on the global registration map. Finally, according to the calculated offset (U x , U y ) Perform local deformation optimization and adjustment on the printed image after global registration.
[0081] like Figure 9 、 Figure 10 As shown in the figure, there are comparison diagrams of the local optimization effect of the global registration results of the deformation estimation algorithm without introducing brightness compensation and the deformation estimation algorithm with introducing brightness compensation. When there is a brightness difference between the prepress original image O and the printed image P, the deformation estimation algorithm without introducing brightness compensation cannot effectively correct the elastic deformation that still exists in the global registration result. However, by introducing nonlinear brightness compensation, the limitation of brightness difference on the deformation estimation algorithm is successfully overcome, the local deformation optimization of the global registration result is achieved, and the elastic deformation is corrected.
[0082] Table 1 shows a comparative analysis of the registration accuracy of the method of the present invention with a single feature extraction algorithm (SIFT algorithm), a deformation estimation algorithm (Active Demons algorithm), and a feature extraction algorithm (SIFT algorithm) + a deformation estimation algorithm without brightness compensation (Active Demons algorithm) for the same instance. It can be seen that the registration accuracy of the algorithm proposed in the present invention is the best in all five evaluation indicators.
[0083] Table 1: Comparative analysis of registration accuracy
[0084]
[0085] In this embodiment, according to the algorithm of the present invention, the final registration result and the local magnified image are obtained, as shown in FIG. Figure 10 As shown in the figure, a high registration accuracy is achieved, and both rigid and elastic deformations are well corrected.
[0086] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0087] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0088] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.
[0089] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.
[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.
Claims
1. A digital printing image registration method integrating feature registration and deformation optimization, characterized in that: include: Firstly, a feature extraction algorithm is used to achieve global registration between the pre-press original image and the printed image, and then a deformation estimation algorithm is used to perform local deformation optimization. Adaptively introduce a reduction factor in the global registration stage; In the local deformation optimization stage, the pre-press original image is segmented into superpixels, and a nonlinear brightness compensation mechanism is introduced based on the difference information of the corresponding superpixel areas between the printed image and the pre-press original image. The adaptive introduction of the reduction factor specifically includes the following steps: 1.1) Select the smaller of the length and width of the original image before printing, and divide it by 240. The result is the initial reduction factor; 1.2) According to the reduction factor Get the corresponding reduced affine matrix and the corresponding magnified affine matrix , according to the reduced affine matrix S, the printed image to be registered and the pre-press original image are resized and then globally registered; 1.3) Calculating the correlation coefficient (CC) between the global registration result and the prepress original image. If the CC exceeds a set threshold, output the global registration result. If it does not, double the reduction factor and repeat steps 1.2) and 1.3) until the CC exceeds the set threshold, and output a satisfactory global registration result. The nonlinear brightness compensation mechanism specifically includes: 3.1) The pixels in the pre-press original image are grouped into larger areas with similar properties through the super-pixel segmentation algorithm, and the brightness of the areas with similar properties are compensated. Specifically, according to the pre-press original image Compared with the global registration result Difference information , perform brightness compensation on the original image before printing, , wherein for Dot The average value of pixels in the superpixel area, As a point Brightness compensation value, is the compensation coefficient, ranging from 0.8 to 1.2; 3.2) Obtain the original pre-press image after brightness compensation Compared with the global registration result Inter-frame difference ; Prepress original image gradient information after brightness compensation ; 3.3) According to the deformation estimation algorithm, , after Taylor expansion, , thus obtaining, ; It is the offset of the dots between the prepress original image and the printed image; 3.4) Add the gradient of the global registration result to the offset calculation to obtain the offset of each pixel , ， ， in is the result of calculation after adding brightness compensation, On point The value at and They are the points on the prepress original image after adding brightness compensation Department Direction and The gradient of the direction, and They are the points on the global registration map Department Direction and The gradient of the direction, It is a normalization factor used to control the offset size.
2. The digital printing image registration method combining feature registration and deformation optimization according to claim 1, characterized in that: The correlation coefficient CC value is specifically: ， The value of CC ranges from -1 to 1, where 1 represents a perfect match, 0 represents no correlation, and -1 represents a perfect negative correlation; where N is the total number of pixels, is the pixel value of the i-th pixel in the prepress original image, is the average value of the intensity values of all pixels in the original image before printing, is the intensity value corresponding to the i-th pixel in the global registration result, It is the average value of the intensity values of all pixels in the global registration result.
3. The digital printing image registration method combining feature registration and deformation optimization according to claim 1, characterized in that: The above 1.2) specifically includes: 2.1) Using the reduced affine matrix , resize the pre-press original image O and the printed image P, and update the printed image space position matrix, record , is the spatial position matrix of the printed image before resizing; 2.2) Using a feature extraction algorithm, we extract the feature points of the resized prepress image and the printed image, locate the feature points, determine their orientation, describe them, and perform feature matching. 2.3) Obtain the affine transformation coefficients of the resized pre-press original image and the printed image through random sample consensus (RANSAC) to obtain the applied transformation matrix ; 2.4) Obtain the spatial position matrix of the printed image after global registration through matrix calculation , that is: , thus obtaining the final application transformation matrix Finally, the printed image P is affine transformed by M to obtain the global registration result G.
4. The digital printing image registration method combining feature registration and deformation optimization according to claim 1, characterized in that: The deformation estimation algorithm adopts Active Demons algorithm, and the global registration result Treat as prepress original A frame of image after slight movement and deformation, Prepress original image On point The brightness value is , then ; That is, through the gradient information of the prepress original image O , the difference information between the prepress original image O and the global registration result G Ability to calculate the dot offset between the prepress original and the printed image , for and Difference information: , a nonlinear brightness compensation mechanism is introduced to eliminate the error in deformation estimation caused by the nonlinear brightness difference between the prepress original image O and the global registration result G.
5. The digital printing image registration method integrating feature registration and deformation optimization according to claim 1, characterized in that: According to the offset Global registration results Perform local deformation optimization and adjustment to obtain the final registration result map.
6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the digital printing image registration method of fusing feature registration and deformation optimization as described in any one of claims 1 to 5 is implemented.
7. An electronic device, characterized in that: The device comprises: one or more processors; a memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the digital printing image registration method of fusing feature registration and deformation optimization as described in any one of claims 1 to 5.