A color optimization printing method suitable for double-side paper

Abstract

This invention relates to the field of color printing technology, and discloses a color optimization printing method suitable for offset paper. The method includes: converting the ink droplet diffusion process into a linear operator based on a diffusion kernel to form a continuous image after diffusion; defining an objective function; and alternately solving the continuous image and the objective function to output a first matrix. This invention establishes partial differential equation constraints based on measured diffusion kernels and anisotropic parameters, ensuring that the ink volume predicted by the neural network strictly follows the actual diffusion law. Furthermore, by increasing color channels, more layers of ink dots can be superimposed, achieving smoother color transitions. Simultaneously, it expands the color gamut, allowing precise control of ink dot distribution in each region, ensuring clear strokes and rich colors in tiny characters, vividly reproducing image textures, shadow gradations, and other details, achieving smoother skin tone transitions and metallic texture representation. This method is suitable for industrial-grade high-speed printing and one-pass paper printing scenarios.

Description

Technical Field

[0001] This invention relates to the field of color printing technology, and more specifically, to a color optimization printing method suitable for offset paper. Background Technology

[0002] Offset paper, also known as double-sided offset paper, is a typical representative of printing paper and belongs to the category of cultural printing paper. Its name originates from the process of coating both sides of the paper with adhesive during the papermaking process to improve surface properties. It features low stretchability, uniform ink absorption, good smoothness, dense and opaque texture, and strong water resistance. Its main uses include printing books, textbooks, magazines, brochures, maps, calendars, envelopes, notebooks, advertising posters, and corporate brochures. It is especially suitable for high-quality monochrome or color printing on offset printing presses. Compared to coated paper, offset paper has slightly inferior printing quality but is cheaper and lighter, making it more suitable for large-scale printing of textbooks and book texts where cost-effectiveness is crucial. It also boasts good dimensional stability, uniform whiteness, high tensile strength, strong folding endurance, and high opacity.

[0003] Existing printing methods for offset paper have several drawbacks: traditional printing methods do not consider the anisotropic elliptical diffusion characteristics of ink droplets caused by the paper fiber orientation, leading to color shift and edge burrs; halftone algorithms assume ideal diffusion conditions, ignoring the strong ink absorption characteristics of offset paper, resulting in excessive ink smudging, blurred details, and a lack of actual diffusion nuclei for reverse optimization; moreover, the lack of a closed-loop compensation mechanism makes it impossible to monitor dynamic factors such as paper deformation and paper feed errors, resulting in poor long-term printing consistency and an inability to correct ink volume / drop point offset; in addition, it is difficult to break down ink volume according to ink quantity and paper condition, resulting in insufficient ability to suppress lateral diffusion, ultimately restricting the improvement of printing accuracy and color performance of offset paper. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a color optimization printing method suitable for offset paper.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A color-optimized printing method for offset paper, the method comprising: S101: Collect the physical parameters of the offset paper, obtain the diffusion parameters and diffusion nuclei corresponding to different ink droplet volume levels, and analyze the physical parameters and diffusion parameters to obtain the transverse diffusion tensor, longitudinal diffusion tensor and fiber orientation angle. S102: Convert the physical parameters of the offset paper into partial differential equations, train the image neural network, and use the trained image neural network to obtain the predicted ink amount and its distribution from the printed image; S103: Based on the diffusion kernel, the ink droplet diffusion process is converted into a linear operator to form a continuous image after diffusion. The objective function is defined, and the continuous image after diffusion and the objective function are solved alternately to output the first matrix. The second matrix is ​​preprocessed to obtain the printing matrix. S104: Collect the actual ink volume ejected from each nozzle, the deformation of the offset paper, and the paper feeding error. Calculate the error using the actual ink volume ejected and the predicted ink volume to obtain the ink volume correction value. Based on the ink volume correction value, the physical parameters of the offset paper, the deformation of the offset paper, and the paper feeding error, generate the landing point offset compensation and the drive parameters for each Epson nozzle.

[0006] Furthermore, obtaining the diffusion parameters and diffusion nuclei corresponding to different ink droplet volume levels includes: Spray single ink droplets of different volume levels onto the surface of offset paper, and record the diffusion diameter and edge roughness of the ink droplets at each time after they come into contact with the offset paper. The diffusion diameter is fitted, and the diffusion parameters for each volume level are solved using the least squares method. The radial ink distribution at each time point is calculated to obtain the diffusion nucleus.

[0007] Furthermore, the conversion of the physical parameters of the double-sided adhesive paper into partial differential equations includes: Based on the transverse and longitudinal diffusion tensors, a concentration distribution function of ink droplets on the surface of offset paper is defined. :

[0008] In the formula: Represents the transverse diffusion tensor and the longitudinal diffusion tensor. Indicates absorption rate; Define residual:

[0009] In the formula: The predicted concentration distribution; Among them, absorption rate This was obtained by fitting the ink droplet volume decay curve.

[0010] Furthermore, the specific steps for converting the ink droplet diffusion process into a linear operator based on the diffusion kernel are as follows: Discrete convolution kernels are constructed by selecting a fixed diffusion time based on the diffusion kernel:

[0011] In the formula: where i and j are integer indices. It is the covariance matrix; The diffusion process is represented by convolution:

[0012] In the formula: This indicates the distance at coordinates after the ink droplet diffuses. The final ink concentration at the point, Represents the binary halftone dot matrix in coordinates The value at that location, For the diffusion nucleus at the offset The weight value at that point, Integer offset is the radius of the convolution kernel.

[0013] Furthermore, the step of alternately solving the combined diffused continuous image and the objective function includes: The n color channels are processed independently, and the difference between the diffused continuous image and the printed image is quantized to obtain the data fidelity term. A regularization term is then introduced for constraint. By combining the data fidelity term and the regularization term, we obtain the objective function, forming a constrained optimization problem. We then add continuous variables and penalty terms to simplify the optimization problem. The simplified optimization problem is decomposed into multiple subproblems. The maximum number of iterations and the convergence tolerance are set. In each iteration, the continuous variables, binary variables and Lagrange multipliers are updated sequentially. The first matrix is ​​obtained when the iteration converges or the maximum number of iterations is reached.

[0014] Furthermore, the specific operation of calculating the error using the actual ink volume and the predicted ink volume is as follows: For the pixel currently being printed, if ejection is required, the error calculation formula is as follows:

[0015] In the formula: To predict ink volume, This refers to the actual amount of ink ejected. Record the historical error of each nozzle, calculate the cumulative error from the historical error, and calculate the correction value based on the current ambient temperature and humidity; The formula for calculating the ink volume correction value is:

[0016] In the formula: , For correction factor, For cumulative error, This indicates the correction value.

[0017] Furthermore, the specific steps for generating the landing point offset compensation and the driving parameters for each nozzle include: The final ink volume is calculated based on the ink volume correction value. The base voltage of the nozzle is then found based on the final ink volume. A temperature compensation coefficient is introduced to compensate the base voltage, resulting in the compensated voltage. The landing point offset compensation is calculated from the direction offset, paper deformation offset, and paper feed error offset; The formula for calculating the directional offset is as follows:

[0018] In the formula: This represents the offset magnitude.

[0019] Furthermore, the calculation formulas for the paper deformation offset and the paper feed error offset are as follows: Paper deformation offset:

[0020] Paper feed error offset:

[0021] The total offset is:

[0022]

[0023] In the formula: The deformation coefficient, This is the paper feed error offset.

[0024] An electronic device includes a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the aforementioned color optimization printing method for offset paper.

[0025] A computer-readable storage medium storing a computer program that, when executed, implements the aforementioned color optimization printing method for offset paper.

[0026] Compared with the prior art, the beneficial effects of the present invention are as follows: This application discloses a color optimization printing method suitable for offset paper, comprising: converting the ink droplet diffusion process into a linear operator based on a diffusion kernel to form a continuous image after diffusion; defining an objective function; alternately solving the continuous image after diffusion and the objective function; outputting a first matrix; and preprocessing a second matrix to obtain a printing matrix. This invention establishes partial differential equation constraints based on measured diffusion kernels and anisotropic parameters, ensuring that the ink volume predicted by the neural network strictly follows the actual diffusion law, avoiding the overfitting risk of purely data-driven approaches. Furthermore, by increasing color channels, more layers of ink dots can be superimposed, achieving smoother color transitions. Simultaneously, expanding the color gamut allows for precise control of ink dot distribution in each region, ensuring clear strokes and rich colors for even the smallest characters, vividly reproducing image textures, shadow gradations, and other details, achieving smoother skin tone transitions and metallic textures. This is suitable for industrial-grade high-speed printing and one-pass paper printing scenarios. Secondly, the invention employs diffusion kernel deconvolution and alternating direction multiplier methods to directly solve the halftone dot matrix, adaptively optimizing the dot matrix distribution based on paper characteristics, effectively suppressing common issues like smudging and edge blurring on offset paper. Finally, real-time acquisition of printhead feedback, paper deformation, and paper feed errors generates dynamic waveforms and drop point compensation, achieving micro-droplet-level precise control, significantly improving long-term printing consistency and stability, and resulting in a significant improvement in offset paper printing quality. Attached Figure Description

[0027] Figure 1 A flowchart of a color optimization printing method for offset paper provided by the present invention; Figure 2 A schematic diagram of the structure of an electronic device provided by the present invention; Figure 3 This is a schematic diagram of the structure of a computer-readable storage medium provided by the present invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Example 1 Please see Figure 1 As shown, this embodiment discloses a color optimization printing method suitable for offset paper, the method comprising: S101: Collect the physical parameters of the offset paper, obtain the diffusion parameters and diffusion nuclei corresponding to different ink droplet volume levels, and analyze the physical parameters and diffusion parameters to obtain the transverse diffusion tensor, longitudinal diffusion tensor and fiber orientation angle. In this embodiment, the specific operation for collecting the physical parameters of the double-sided adhesive paper is as follows: Use a digital thickness gauge to take 5 points on the double-sided coated paper and measure the thickness value. Use a surface roughness meter to measure the double-sided coated paper 3 times in the longitudinal direction and 3 times in the transverse direction, and take the average value to obtain the surface roughness. The process of obtaining diffusion parameters and diffusion nuclei corresponding to different ink droplet volume levels includes: Fix the offset paper on the table, and spray single ink droplets of different volume levels onto the surface of the offset paper. Record the diffusion diameter and edge roughness of the ink droplets at each time after they come into contact with the offset paper. The volume settings are based on the droplet volume settings provided by the printhead manufacturer. Each setting corresponds to a fixed drive waveform, which is directly called by the printhead controller. In this example, Epson is used.

[0030] A high-speed camera was used to capture the diffusion process of the ink droplet after it came into contact with the offset paper from a vertical direction. Each frame of the image was binarized, and the equivalent diameter of the ink droplet profile was measured. The average value of 30 repetitions was taken as the diffusion diameter of that volume range at time tt.

[0031] The diffusion diameter is fitted, and the diffusion parameters for each volume level are solved using the least squares method. The radial ink distribution at each time point is calculated to obtain the diffusion nucleus.

[0032] The diffusion diameter is fitted using the following formula:

[0033] In the formula: The initial diameter was measured directly from the first frame of the image at the moment of ejection. This is the diffusion rate coefficient, with a value of 30 μm. The time constant is 1 second. For time.

[0034] The specific steps to obtain the diffused nucleus are as follows: Assuming the radial ink distribution is Gaussian, the diffusion kernel is represented as:

[0035] In the formula: Radial distance, The standard square of the Gaussian distribution at time tt; Mark the offset paper along the longitudinal and transverse directions. Using the same ink droplet setting, spray single ink droplet arrays along the longitudinal and transverse directions respectively. Measure the major and minor axes of the diffusion ellipse at fixed time points and calculate the anisotropy ratio:

[0036] The diffusion rate coefficient is divided into two components: transverse and longitudinal.

[0037] Assume the diffusion tensor is a diagonal matrix in the principal axis coordinate system:

[0038] in: = , = ; Images of the paper surface were captured using a polarizing microscope to obtain a grayscale image of the fiber arrangement. Image processing algorithms were then used to calculate the global principal direction. .

[0039] It should be noted that the image processing algorithm can be either the histogram of directional gradients or the Fourier transform method, both of which are existing technologies and are not limited to here.

[0040] S102: Convert the physical parameters of the offset paper into partial differential equations, train the image neural network, and use the trained image neural network to obtain the predicted ink amount and its distribution from the printed image; The process of converting the physical parameters of the double-sided adhesive paper into partial differential equations includes: Based on the transverse and longitudinal diffusion tensors, a concentration distribution function of ink droplets on the surface of offset paper is defined. :

[0041] In the formula: Represents the transverse diffusion tensor and the longitudinal diffusion tensor. Indicates absorption rate; To enforce the above equations during image neural network training, the residuals are defined as follows:

[0042] In the formula: The predicted concentration distribution; Among them, absorption rate The curve is obtained by fitting the ink droplet volume decay curve, which is obtained according to the different types of ink droplets.

[0043] The image neural network is trained as follows: The loss function is defined as:

[0044] In the formula: Indicates color loss. This is a spatial smoothing regularization term for ink distribution, calculated as the sum of squares of the ink difference between adjacent pixels, to prevent isolated noise. , is the weighting coefficient, with values ​​of 0.1 and 0.01.

[0045] Training strategy: Phase 1 (Supervised Pre-training): The network is trained using a synthetic dataset (with known ink distribution, the final image is generated through physical simulation), using only... The learning rate is 0.001, and the training run is 200 rounds.

[0046] Phase Two (Physical Fine-tuning): Using a real print dataset, add... and The learning rate is 0.0001, and the training takes 50 rounds.

[0047] The optimizer used is Adam, with a batch size of 64.

[0048] As a specific implementation method, the image neural network adopts a multi-branch neural network structure: Image encoding branch: Consists of 6 convolutional layers and 2 fully connected layers. The input is a 32×32 neighborhood image patch centered on the target pixel (including the target Lab value, texture complexity, edge gradient, local contrast, etc.). The output is a 128-dimensional feature vector.

[0049] Physical constraint branch: Receives features from the image encoding branch and outputs an initial concentration field (64×64 grid, covering the influence area of ​​one ink droplet) through four fully connected layers. Then, a differentiable PDE solver (based on the finite difference method) is used to perform a forward simulation with a time step of Δt=0.1 s for a total of 50 steps to obtain the final concentration field.

[0050] Output layer: Integrates the final concentration field to obtain the ink volume prediction for each pixel.

[0051] S103: Based on the diffusion kernel, the ink droplet diffusion process is converted into a linear operator to form a continuous image after diffusion. The objective function is defined, and the continuous image after diffusion and the objective function are solved alternately to output the first matrix. The second matrix is ​​preprocessed to obtain the printing matrix. The specific steps for converting the ink droplet diffusion process into a linear operator based on the diffusion kernel are as follows: Discrete convolution kernels are constructed by selecting a fixed diffusion time based on the diffusion kernel:

[0052] In the formula: where i and j are integer indices, ranging from -R ≤ i, j ≤ R, and R is twice the diffusion diameter. It is the covariance matrix; The diffusion process is represented by convolution:

[0053] In the formula: This indicates the distance at coordinates after the ink droplet diffuses. The final ink concentration at the point, Represents the binary halftone dot matrix in coordinates The value at that location, For the diffusion nucleus at the offset The weight value at that point, Integer offset is the radius of the convolution kernel.

[0054] In the discrete form, the linear operator is a coefficient matrix, with each row corresponding to the spread convolution of one pixel. In actual calculations, the fast Fourier transform is used to accelerate the convolution.

[0055] The method of alternately solving the combined diffused continuous image and the objective function includes: The n color channels are processed independently to quantize the difference between the diffused continuous image and the printed image, resulting in a data fidelity term. A regularization term is introduced for constraint. In this embodiment, n is an integer, such as 5, 6, 7, or 8, and is not limited to the traditional four colors (cyan, magenta, black, and yellow). Traditional four-color printing is insufficient in expressing vivid colors and relies on the superposition of ink dots to form colors. Color gradation jumps are prone to occur in the gradient area from light to dark colors (such as the discontinuity in the light halftone area), and the superposition of halftone dots can also cause color bleeding or blurred boundaries.

[0056] Multicolor printing, by increasing the number of color channels (such as 5-8 colors), allows for the layering of ink dots in more layers, achieving smoother color transitions and expanding the color gamut. It can precisely control the distribution of ink dots in each area, ensuring that the strokes of tiny text are clear and the colors are rich, vividly reproducing details such as image textures and shadow gradients, achieving smoother skin tone transitions and metallic textures, and meeting the stringent requirements for color accuracy in high-end commercial printing, art brochures, and other applications. By combining the data fidelity term and the regularization term, we obtain the objective function, forming a constrained optimization problem. We then add continuous variables and penalty terms to simplify the optimization problem. The simplified optimization problem is decomposed into multiple subproblems, namely, iteratively updating continuous variables, binary variables and Lagrange multipliers to obtain an approximately optimal halftone dot matrix. The maximum number of iterations and the convergence tolerance are set, and the continuous variables, binary variables and Lagrange multipliers are updated sequentially in each iteration. The first matrix is ​​obtained when the iteration converges or the maximum number of iterations is reached.

[0057] As a specific implementation method, the optimization problem is:

[0058] In the formula: Represents the continuous image after diffusion. To print the image, For regularization terms, This is the regularization coefficient.

[0059]

[0060] In the formula: Indicates the position of matrix B The element value at that position, since B is a binary halftone dot matrix.

[0061] Simplified optimization problem:

[0062] In the formula: >0 is the penalty parameter.

[0063] The specific steps for updating continuous variables are as follows: Fixed binary variable and Lagrange multipliers Solve the following:

[0064] This problem is a convex optimization problem, which can be solved by gradient descent, a prior art method, and this application does not limit it here.

[0065] The specific steps for updating a binary variable are as follows: Fixed continuous variables and Lagrange multipliers

[0066] Among them, the projection operator If x < 0.5, then x = 1; if x ≥ 0.5, then x ≥ 0.5.

[0067] Update the Lagrange multipliers:

[0068] like If the number of iterations has not reached the upper limit, continue.

[0069] The preprocessing of the second matrix specifically involves eliminating isolated ink dots, which is a prior art and will not be elaborated upon in this application.

[0070] S104: Collect the actual ink volume ejected from each nozzle, the deformation of the offset paper, and the paper feeding error. Calculate the error using the actual ink volume ejected and the predicted ink volume to obtain the ink volume correction value. Based on the ink volume correction value, the physical parameters of the offset paper, the deformation of the offset paper, and the paper feeding error, generate the landing point offset compensation and the drive parameters for each Epson nozzle.

[0071] The specific operation of calculating the error using the actual ink volume and the predicted ink volume is as follows: For the pixel currently being printed, if ejection is required, the error calculation formula is as follows:

[0072] In the formula: To predict ink volume, This refers to the actual amount of ink ejected. Record the historical error of each nozzle, calculate the cumulative error from the historical error, that is, add the historical error to the current error, and calculate the correction value based on the current ambient temperature and humidity; The formula for calculating the ink volume correction value is:

[0073] In the formula: 、 For correction factor, For cumulative error, This indicates the correction value.

[0074] The specific steps for generating the landing point offset compensation and the driving parameters for each nozzle include: The final ink volume is calculated based on the ink volume correction value. The base voltage of the nozzle is determined based on the final ink volume. A temperature compensation coefficient is then introduced to compensate for the base voltage, resulting in the compensated voltage. The calculation formula is as follows:

[0075] In the formula: The reference temperature during calibration, This is the temperature compensation coefficient, with a value of -0.1. Indicates the base voltage. This is the real-time temperature.

[0076] If the final ink volume is >5pL, then multi-pulse mode is enabled; otherwise, single-pulse mode is used.

[0077] Single pulse mode: Outputs a rectangular wave: Voltage = Pulse width = 2μs (fixed value, determined by nozzle characteristics); Multi-pulse mode: Calculate the number of pulses:

[0078] In the formula: To achieve the minimum stable ink droplet volume, The value is set to 5 to limit the number of pulses and prevent the waveform from becoming too long.

[0079] The ink volume of each pulse is allocated, and the allocation ratio can be adjusted according to different situations. This application does not limit this.

[0080] The landing point offset compensation is calculated from the direction offset, paper deformation offset, and paper feed error offset; The formula for calculating the directional offset is as follows:

[0081] In the formula: This represents the offset magnitude.

[0082] The calculation formulas for the paper deformation offset and paper feed error offset are as follows: Paper deformation offset:

[0083] Paper feed error offset:

[0084] The total offset is:

[0085]

[0086] In the formula: The deformation coefficient has a value of 0.05. This is the paper feed error offset.

[0087] It should be further noted that this invention can be applied to industrial-grade production inkjet printers to meet the requirement of one-pass printing of paper, and can be used for printing instruction manuals (products, such as pharmaceuticals and electronic products), teaching aids / textbooks, and other materials.

[0088] Example 2 Please see Figure 2 As shown, this embodiment discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the color optimization printing method for offset paper provided by the above methods.

[0089] Since the electronic device described in this embodiment is the electronic device used to implement the color optimization printing method for offset paper in the embodiments of this application, those skilled in the art can understand the specific implementation and various variations of the electronic device in this embodiment based on the color optimization printing method for offset paper described in the embodiments of this application. Therefore, how the electronic device implements the method in the embodiments of this application will not be described in detail here. Any electronic device used by those skilled in the art to implement the color optimization printing method for offset paper in the embodiments of this application falls within the scope of protection of this application.

[0090] Example 3 Please see Figure 3 As shown, this embodiment discloses a computer-readable storage medium, including a memory, a processor, and a computer program stored on the memory and running on the processor. When the processor executes the computer program, it implements the color optimization printing method for offset paper provided by the above methods.

[0091] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters, weights, and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0092] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired or wireless network. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0093] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0094] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0095] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only one method, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0096] The units described as separate components may or may not be physically separate. 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.

[0097] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0098] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0099] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A color optimization printing method suitable for offset paper, characterized in that, The method includes: S101: Collect the physical parameters of the offset paper, obtain the diffusion parameters and diffusion nuclei corresponding to different ink droplet volume levels, and analyze the physical parameters and diffusion parameters to obtain the transverse diffusion tensor, longitudinal diffusion tensor and fiber orientation angle. S102: Convert the physical parameters of the offset paper into partial differential equations, train the image neural network, and use the trained image neural network to obtain the predicted ink amount and its distribution from the printed image; S103: Based on the diffusion kernel, the ink droplet diffusion process is converted into a linear operator to form a continuous image after diffusion. The objective function is defined, and the continuous image after diffusion and the objective function are solved alternately to output the first matrix. The second matrix is ​​preprocessed to obtain the printing matrix. S104: Collect the actual ink volume ejected from each nozzle, the deformation of the offset paper, and the paper feeding error. Calculate the error using the actual ink volume ejected and the predicted ink volume to obtain the ink volume correction value. Based on the ink volume correction value, the physical parameters of the offset paper, the deformation of the offset paper, and the paper feeding error, generate the landing point offset compensation and the drive parameters for each Epson nozzle.

2. The color optimization printing method for offset paper according to claim 1, characterized in that, The process of obtaining diffusion parameters and diffusion nuclei corresponding to different ink droplet volume levels includes: Spray single ink droplets of different volume levels onto the surface of offset paper, and record the diffusion diameter and edge roughness of the ink droplets at each time after they come into contact with the offset paper. The diffusion diameter is fitted, and the diffusion parameters for each volume level are solved using the least squares method. The radial ink distribution at each time point is calculated to obtain the diffusion nucleus.

3. The color optimization printing method for offset paper according to claim 2, characterized in that, The process of converting the physical parameters of the double-sided adhesive paper into partial differential equations includes: Based on the transverse and longitudinal diffusion tensors, a concentration distribution function of ink droplets on the surface of offset paper is defined. : ； In the formula: Represents the transverse diffusion tensor and the longitudinal diffusion tensor. Indicates absorption rate; Define residual: ； In the formula: The predicted concentration distribution; Among them, absorption rate This was obtained by fitting the ink droplet volume decay curve.

4. The color optimization printing method for offset paper according to claim 3, characterized in that, The specific steps for converting the ink droplet diffusion process into a linear operator based on the diffusion kernel are as follows: Discrete convolution kernels are constructed by selecting a fixed diffusion time based on the diffusion kernel: ； In the formula: where i and j are integer indices. It is the covariance matrix; The diffusion process is represented by convolution: In the formula: This indicates the distance at coordinates after the ink droplet diffuses. The final ink concentration at the point, Represents the binary halftone dot matrix in coordinates The value at that location, For the diffusion nucleus at the offset The weight value at that point, Integer offset is the radius of the convolution kernel.

5. The color optimization printing method for offset paper according to claim 1, characterized in that, The method of alternately solving the combined diffused continuous image and the objective function includes: The n color channels are processed independently, and the difference between the diffused continuous image and the printed image is quantized to obtain the data fidelity term. A regularization term is then introduced for constraint. By combining the data fidelity term and the regularization term, we obtain the objective function, forming a constrained optimization problem. We then add continuous variables and penalty terms to simplify the optimization problem. The simplified optimization problem is decomposed into multiple subproblems. The maximum number of iterations and the convergence tolerance are set. In each iteration, the continuous variables, binary variables and Lagrange multipliers are updated sequentially. The first matrix is ​​obtained when the iteration converges or the maximum number of iterations is reached.

6. The color optimization printing method for offset paper according to claim 1, characterized in that, The specific operation of calculating the error using the actual ink volume and the predicted ink volume is as follows: For the pixel currently being printed, if ejection is required, the error calculation formula is as follows: ； In the formula: To predict ink volume, This refers to the actual amount of ink ejected. Record the historical error of each nozzle, calculate the cumulative error from the historical error, and calculate the correction value based on the current ambient temperature and humidity; The formula for calculating the ink volume correction value is: ； In the formula: , For correction factor, For cumulative error, This indicates the correction value.

7. The color optimization printing method for offset paper according to claim 1, characterized in that, The specific steps for generating the landing point offset compensation and the driving parameters for each nozzle include: The final ink volume is calculated based on the ink volume correction value. The base voltage of the nozzle is then found based on the final ink volume. A temperature compensation coefficient is introduced to compensate the base voltage, resulting in the compensated voltage. The landing point offset compensation is calculated from the direction offset, paper deformation offset, and paper feed error offset; The formula for calculating the directional offset is as follows: ； In the formula: This represents the offset magnitude.

8. The color optimization printing method for offset paper according to claim 7, characterized in that, The calculation formulas for the paper deformation offset and paper feed error offset are as follows: Paper deformation offset: ； Paper feed error offset: ； The total offset is: ； ； In the formula: The deformation coefficient, This is the paper feed error offset.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the color optimization printing method for offset paper as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed, implements the color optimization printing method for offset paper as described in any one of claims 1 to 8.

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