Ink droplet observation image processing method and device, equipment and storage medium

Abstract

The invention discloses an ink droplet observation image processing method and device, equipment and a storage medium, and the method comprises the steps: determining the position information of a fixed flaw in a background according to a plurality of observation background images; based on the position information of the fixed flaws, eliminating the fixed flaws in the background mean value image to obtain a background mean value image without the fixed flaws; determining brightness distribution information of the background mean value image without the fixed flaws; calculating a brightness reference value of a flying ink droplet observation background according to the brightness value of a part of pixel areas in the brightness distribution information; calculating to obtain a brightness correction coefficient corresponding to each pixel point in the ink droplet observation image according to the brightness reference value; optimizing the ink droplet observation image based on the position information and the brightness correction coefficient of the fixed flaw; and performing contrast enhancement on the optimized ink droplet observation image to obtain a flight ink droplet observation result image. According to the scheme, the ink droplet observation image without fixed flaws and with good brightness uniformity is obtained, and the subsequent contrast enhancement operation can be smoothly carried out.

Description

Technical Field

[0001] This application relates to the field of inkjet printing flying ink droplet observation technology, specifically to a method, apparatus, device, and storage medium for processing ink droplet observation images. Background Technology

[0002] In inkjet printing, microfluidic control, or other technologies involving the generation and observation of tiny droplets, accurately acquiring parameters such as the morphology, size, and velocity of ink droplets is crucial. This typically requires the use of imaging equipment such as high-speed cameras to capture and analyze ink droplets in flight. Because ink droplets are traveling at high speeds, the grayscale of their edges is often quite similar to the background. Therefore, existing technologies often require contrast enhancement of the captured droplet images to more clearly distinguish the differences between the droplets and the background.

[0003] However, when acquiring and analyzing images of flying ink droplets, two main issues arise. First, ink droplet images are susceptible to fixed defects in the optical system. Specifically, stains on the surface of the optical lens, dust adhering to the sensor's sensing area, and residues inside the lens barrel can create permanent background defect areas in the acquired images. Second, during ink droplet observation, uneven illumination from the light source and differences in the response of optical components (such as lenses and filters) often lead to locally overly bright or dark areas in the acquired images. For example, excessive brightness in the central area of ​​the light source or weakening in the edge areas. Both fixed defects in the ink droplet image and uneven brightness in different areas will affect the subsequent contrast enhancement process, reducing the contrast between the background and the ink droplet in the subsequent ink droplet image. Ultimately, even after image processing, it is still difficult to calculate reliable ink droplet flight parameters from the ink droplet image. Summary of the Invention

[0004] This application provides a method, apparatus, device, and storage medium for processing ink droplet observation images, which can solve the problems of background fixation defects and uneven brightness distribution in existing flying ink droplet images.

[0005] In a first aspect, embodiments of this application provide a method for processing ink droplet observation images, employing the following technical solution:

[0006] A method for processing ink droplet observation images, comprising the following steps:

[0007] Acquire multiple background and ink droplet observation images captured under set shooting conditions;

[0008] Based on multiple observed background images, a mean background image is obtained;

[0009] Based on the background mean image and the set fixed defect detection algorithm, the location information of fixed defects in the background is determined;

[0010] Based on the location information of the fixed defects, the fixed defects in the background mean image are eliminated to obtain a background mean image without fixed defects;

[0011] Determine the brightness distribution information of the background mean image without fixed defects;

[0012] Calculate the brightness reference value of the background of the flying ink droplet observation based on the brightness values ​​of some pixel regions in the brightness distribution information;

[0013] Based on the brightness reference value, the brightness correction coefficient corresponding to each pixel in the ink droplet observation image is calculated;

[0014] Based on the location information of the fixed defects, the fixed defects in the ink droplet observation image are removed to obtain a first-level ink droplet observation image without fixed defects;

[0015] Based on the brightness correction coefficient, the first-level ink droplet observation image without fixed defects is optimized to obtain the second-level ink droplet observation image;

[0016] The contrast of the secondary ink droplet observation image is enhanced to obtain the flying ink droplet observation result image.

[0017] In conjunction with the first aspect, in one embodiment, the contrast enhancement of the secondary ink droplet observation image to obtain the flying ink droplet observation result image includes the following steps:

[0018] According to the preset denoising algorithm, pixel noise in the secondary ink droplet observation image is removed to obtain the tertiary ink droplet observation image;

[0019] Determine the initial grayscale variation range in the three-level ink droplet observation image;

[0020] The three-level ink droplet observation image is normalized by normalizing the initial grayscale variation range to a larger grayscale range, thus obtaining the flying ink droplet observation result image.

[0021] In conjunction with the first aspect, in one embodiment, the denoising algorithm for removing pixel noise from the secondary ink droplet observation image according to a preset denoising model to obtain the tertiary ink droplet observation image includes the following steps:

[0022] The pixels in the secondary ink droplet observation image are sorted according to their grayscale value to obtain the pixel grayscale sequence of the secondary ink droplet observation image;

[0023] By removing pixels from the pixel grayscale sequence of the secondary ink droplet observation image at a predetermined ratio / number, a tertiary ink droplet observation image with outliers removed is obtained.

[0024] In conjunction with the first aspect, in one embodiment, the removal of pixels from the pixel grayscale sequence of the secondary ink droplet observation image by a predetermined ratio / number of pixels before and after the initial value results in a tertiary ink droplet observation image with outliers removed.

[0025] The set ratio is one ten-thousandth of the pixel grayscale sequence.

[0026] In conjunction with the first aspect, in one embodiment, the normalization of the three-level ink droplet observation image involves normalizing the initial grayscale variation range to a larger grayscale range, resulting in the flying ink droplet observation image, using the following formula:

[0027]

[0028] In the formula, I norm (x, y) and I(x, y) are the gray values ​​of the same pixel in the flying ink droplet observation image and the three-level ink droplet observation image, respectively; max(I) and min(I) are the maximum and minimum gray values ​​in the three-level ink droplet observation image, respectively; i max The maximum grayscale value within the target grayscale range.

[0029] In conjunction with the first aspect, in one embodiment, the step of calculating the brightness correction coefficient corresponding to each pixel in the ink droplet observation image based on the brightness reference value,

[0030] The brightness correction coefficient for each pixel in the ink droplet observation image is determined based on the ratio between the brightness reference value and the brightness of any pixel in the mean background image without fixed defects.

[0031] In conjunction with the first aspect, in one embodiment, the step of optimizing the first-level ink droplet observation image without fixed defects according to the brightness correction coefficient yields the second-level ink droplet observation image.

[0032] Based on the brightness correction coefficient corresponding to any pixel in the ink droplet observation image, the brightness of the corresponding pixel in the ink droplet observation image without fixed defects is weighted and corrected to obtain the brightness of the corresponding pixel in the secondary ink droplet observation image.

[0033] Secondly, embodiments of this application provide a processing apparatus for ink droplet observation images, employing the following technical solution:

[0034] A processing apparatus for ink droplet observation images, comprising:

[0035] The acquisition module is configured to acquire multiple background images and ink droplet observation images captured under set shooting conditions;

[0036] The first calculation module is configured to obtain a background mean image based on multiple observed background images; determine the location information of fixed defects in the background based on the background mean image and a set fixed defect detection algorithm; and eliminate the fixed defects in the background mean image based on the location information of the fixed defects to obtain a background mean image without fixed defects.

[0037] The second calculation module is configured to determine the brightness distribution information of the background mean image without fixed defects; calculate the brightness reference value of the flying ink droplet observation background based on the brightness values ​​of some pixel regions in the brightness distribution information; and calculate the brightness correction coefficient corresponding to each pixel in the ink droplet observation image based on the brightness reference value.

[0038] An image optimization module is configured to remove fixed defects from the ink droplet observation image based on the location information of the fixed defects, to obtain a first-level ink droplet observation image without fixed defects; optimize the first-level ink droplet observation image without fixed defects according to the brightness correction coefficient, to obtain a second-level ink droplet observation image; and perform contrast enhancement on the second-level ink droplet observation image to obtain a flying ink droplet observation result image.

[0039] Thirdly, embodiments of this application provide a processing device for ink droplet observation images, employing the following technical solution:

[0040] A processing apparatus for ink droplet observation images includes a processor, a memory, and a processing program for ink droplet observation images stored in the memory and executable by the processor. When the processing program for ink droplet observation images is executed by the processor, it implements the steps of the ink droplet observation image processing method as described above.

[0041] Fourthly, embodiments of this application provide a storage medium, employing the following technical solution:

[0042] A storage medium storing a processing program for an ink droplet observation image, wherein when the ink droplet observation image processing program is executed by a processor, the steps of the ink droplet observation image processing method described above are implemented.

[0043] The beneficial effects of the technical solutions provided in this application include:

[0044] The ink droplet observation image processing method, apparatus, device, and storage medium provided in this application first utilize multiple background images without ink droplets to pre-determine the location information of fixed defects in the background. Then, after eliminating the interference of fixed defects using the location information of the fixed defects, the brightness distribution information in the background image can be accurately calculated. Based on the brightness distribution information, a brightness correction coefficient that can adjust the brightness of each position in the background image to a uniform level can be calculated. Finally, by processing the ink droplet observation image with ink droplets sequentially using the location information of the fixed defects and the brightness correction coefficient, an ink droplet observation image without fixed defects and with good brightness uniformity can be obtained. This improves image quality and also enables subsequent contrast enhancement operations to be carried out smoothly, ultimately achieving a more accurate grasp of the ink droplet information in the ink droplet observation image. Attached Figure Description

[0045] Figure 1 This is a schematic flowchart of an embodiment of the ink droplet observation image processing method of this application;

[0046] Figure 2 This is a flowchart illustrating step S1000 in the ink droplet observation image processing method of this application;

[0047] Figure 3 This is a schematic diagram of the functional modules in one embodiment of the ink droplet observation image processing device of this application;

[0048] Figure 4 This is a schematic diagram of the hardware structure of the ink droplet observation image processing device involved in the embodiments of this application. Detailed Implementation

[0049] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0050] The present application provides a method, apparatus, device, and storage medium for processing ink droplet observation images. The key feature of this invention is that it first uses multiple background images without ink droplets to pre-determine the location information of fixed defects in the background. Then, after eliminating the interference of fixed defects using their location information, the brightness distribution information in the background image can be accurately calculated. Based on the brightness distribution information, a brightness correction coefficient can be calculated to adjust the brightness of each position in the background image to a uniform level. Finally, by sequentially processing the ink droplet observation image containing ink droplets using the location information of the fixed defects and the brightness correction coefficient, an ink droplet observation image without fixed defects and with good brightness uniformity can be obtained. This improves image quality and also enables subsequent contrast enhancement operations, ultimately achieving a more accurate understanding of the ink droplet information in the observation image.

[0051] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0052] In a first aspect, embodiments of this application provide a method for processing ink droplet observation images.

[0053] In one embodiment, reference is made to Figure 1 , Figure 1 This is a schematic flowchart of the first embodiment of the ink droplet observation image processing method of this application. The ink droplet observation image processing method includes:

[0054] S100: Acquire multiple background images and ink droplet observation images captured under the set shooting conditions;

[0055] Specifically, the shooting conditions for each background image and ink droplet observation image are consistent, both being under the set shooting conditions to ensure that the size and brightness attributes of the background area captured in each image are consistent. The shooting conditions are determined by technicians according to shooting requirements; this application does not impose any restrictions, as long as the shooting conditions ensure that a background image suitable for normal analysis and processing, as well as an ink droplet observation image containing the ink droplet image, can be obtained.

[0056] S200. Obtain the mean background image based on multiple observed background images;

[0057] Specifically, this step involves calculating the average grayscale value of each pixel in the background using multiple observed background images, ultimately obtaining a background average image. It's worth noting that in this embodiment, the multiple observed background images are taken consecutively to minimize differences caused by external factors, thus obtaining a background average image that best reflects the true background. In some embodiments provided in this application, the number of observed background images required is 10 to 20 consecutively taken images.

[0058] S300. Based on the background mean image and the set fixed defect detection algorithm, determine the location information of the fixed defect in the background;

[0059] Specifically, the fixed defect detection algorithm is a pre-established algorithm that can effectively detect the location of fixed defects in the background mean image. This algorithm can vary in different embodiments. For example, in this embodiment, the YOLOv8 algorithm is specifically selected, and its execution includes the following steps:

[0060] S301. Load Model Parameters

[0061] S302. Input image BigImg, i.e., the background mean image;

[0062] S303.BigImg is divided into multiple smaller images of 512 pixels each;

[0063] S304.BigImg is divided into multiple smaller images of 512 pixels each;

[0064] S305. Each small image is fed into the YOLOv8 inference network;

[0065] S306. Output the detection results of fixed defects in a single small image, and stitch the detection results of all small images together to form the final detection result;

[0066] S307. Provide the final detection result image and the detection coordinate information of the ink droplets, that is, obtain the location information of the fixed defects in the background.

[0067] After obtaining the location information of the fixed blemish in the background, the following steps will be taken:

[0068] S400. Based on the location information of the fixed defects, eliminate the fixed defects in the background mean image to obtain a background mean image without fixed defects.

[0069] Specifically, the removal of fixed defects will be based on a pre-set image inpainting algorithm to obtain a defect-free background mean image. This image can then be used to represent the current true brightness state, unaffected by the fixed defects. Different algorithms can be used in different embodiments for image inpainting. For example, in this embodiment, the Navier-Stokes algorithm is used, and the algorithm process includes the following steps:

[0070] S401. Based on the obtained location information of the fixed defect, generate a mask binary image that corresponds to the same region as the location information of the fixed defect;

[0071] S402. Simultaneously feed the mask binary image, the location information of the fixed defects, and the image containing the fixed defects into the Navier-Stokes algorithm.

[0072] S403. Output the image after fixed defect removal.

[0073] After obtaining the mean background image without fixed flaws, the following steps will be taken:

[0074] S500, Determine the brightness distribution information of the background mean image without fixed defects;

[0075] S600. Calculate the brightness reference value of the background of the flying ink droplet observation based on the brightness values ​​of some pixel regions in the brightness distribution information;

[0076] Specifically, since the central region of the image has the best brightness in the captured image during ink droplet observation, in this embodiment, the average brightness value of the 100*100 pixel region in the center of the image without fixed defects will be used as the brightness reference value fCenterMean for the background of flying ink droplet observation. In other embodiments, the brightness quality of different regions in the image can also be selected. At the same time, the size of the region can be confirmed by the technician. When the region is the central region of the image, the size of the region shall not exceed 1 / 10 of the image width.

[0077] S700. Calculate the brightness correction coefficient corresponding to each pixel in the ink droplet observation image based on the brightness reference value.

[0078] Specifically, in this embodiment, step S700 determines the brightness correction coefficient corresponding to each pixel in the ink droplet observation image based on the ratio between the brightness reference value and the brightness of any pixel in the mean background image without fixed defects, referring to the following formula:

[0079]

[0080] In the formula, CorrCoe f(x, y) is the brightness correction coefficient corresponding to the target pixel in the ink droplet observation image, fCenterMean is the brightness reference value, and oBgImgClean(x, y) is the brightness of the pixel in the background mean image without fixed defects that is consistent with the position information of the target pixel.

[0081] S800. Based on the location information of the fixed defects, remove the fixed defects from the ink droplet observation image to obtain a first-level ink droplet observation image without fixed defects.

[0082] Specifically, based on the location information of the fixed defects, the method for removing fixed defects in the ink droplet observation image is the same as the method for removing fixed defects in the aforementioned observation background image, and will not be repeated here. Through step S800, an ink droplet observation image without fixed defects will finally be obtained.

[0083] S900. Based on the brightness correction coefficient, optimize the first-level ink droplet observation image without fixed defects to obtain the second-level ink droplet observation image;

[0084] Specifically, in this embodiment, when step S900 is executed, the brightness of the corresponding pixel in the ink droplet observation image without fixed defects is weighted and corrected according to the brightness correction coefficient corresponding to any pixel in the ink droplet observation image, so as to obtain the brightness of the corresponding pixel in the secondary ink droplet observation image, as shown in the following formula:

[0085] oDWCorrImg(x,y)=oDWImg(x,y)×CorrCoef(x,y)

[0086] In the formula, oDWCorrImg(x,y) is the brightness of the pixel with coordinates (x,y) in the secondary ink droplet observation image; oDWImg(x,y) is the brightness of the pixel with coordinates (x,y) in the ink droplet observation image without fixed defects; and CorrCoe f(x,y) is the brightness correction coefficient of the pixel with coordinates (x,y) in the ink droplet observation image.

[0087] After obtaining a secondary ink droplet observation image with no fixed defects and optimized brightness uniformity, step S1000 can be executed to further enhance the contrast of the secondary ink droplet observation image, including the following steps:

[0088] S1000: Enhance the contrast of the secondary ink droplet observation image to obtain the flying ink droplet observation result image.

[0089] Specifically, refer to Figure 2 In this embodiment, step S1000 includes the following steps:

[0090] S1100. According to the preset denoising algorithm, remove pixel noise in the secondary ink droplet observation image to obtain the tertiary ink droplet observation image;

[0091] Specifically, by removing pixel noise from the secondary ink droplet observation image, the grayscale range of the pixels in the secondary ink droplet observation image can be further ensured to be closer to the grayscale range of the ink droplet and the background. This allows for a more effective increase in the contrast between the ink droplet and the background during subsequent normalization and stretching of the grayscale range. Specifically, step S1100 in this embodiment includes the following steps:

[0092] S1110. Sort the pixels in the secondary ink droplet observation image according to their grayscale value to obtain the pixel grayscale sequence of the secondary ink droplet observation image;

[0093] S1120. Remove pixels from the pixel grayscale sequence of the secondary ink droplet observation image by a predetermined ratio / number, to obtain a tertiary ink droplet observation image with outliers removed.

[0094] The set ratio / quantity is an empirical value set by technicians based on image quality. In this embodiment, the set ratio before and after removing the secondary ink droplets from the image pixel grayscale sequence is specifically selected. The set ratio is one ten-thousandth of the pixel grayscale sequence. In other embodiments, it can be other ratios, such as one thousandth when the image quality is poor and there is a lot of noise.

[0095] S1200: Determine the initial grayscale variation range in the three-level ink droplet observation image;

[0096] S1300. Normalize the three-level ink droplet observation image, normalize the initial grayscale variation range to a larger grayscale range, and obtain the flying ink droplet observation result image.

[0097] Specifically, in this embodiment, step S1300 adopts the following formula:

[0098]

[0099] In the formula, I norm (x, y) and I(x, y) are the gray values ​​of the same pixel in the flying ink droplet observation image and the three-level ink droplet observation image, respectively; max(I) and min(I) are the maximum and minimum gray values ​​in the three-level ink droplet observation image, respectively; i max To determine the maximum grayscale value within the target grayscale range, in this embodiment, the grayscale variation range of the three-level ink droplet observation image is normalized from the initial grayscale variation range to 0–255, i.e., i max The value is 255.

[0100] Ultimately, after obtaining a three-level ink droplet observation image with no fixed defects and good brightness uniformity, it is possible to further remove outliers in the grayscale data involved, thereby removing noise points with grayscale anomalies in the image. Then, by performing normalized stretching of the grayscale range, the influence of noise on pixel grayscale can be reduced. This is reflected in the significantly enhanced contrast between the ink droplets and the background image in the stretched flying ink droplet observation result image, which facilitates the subsequent calculation and determination of ink droplet parameters using this result image.

[0101] Secondly, embodiments of this application also provide a processing apparatus for ink droplet observation images.

[0102] In one embodiment, reference is made to Figure 3 , Figure 3 This is a functional module diagram of an embodiment of the ink droplet observation image processing apparatus of this application. Figure 3 As shown, the processing device for ink droplet observation images includes:

[0103] The acquisition module is configured to: acquire the first relative positional relationship between the marker points on the side wall of the nozzle module and the nozzle hole units on the mounting surface of the nozzle module;

[0104] The initial positioning module is configured to: acquire the image captured by the nozzle module in the observation camera at a preset position, and adjust the position of the nozzle module according to whether the marker point in the image meets the focusing requirements, until the marker point is within the focusing range of the observation camera and a first image with the marker point is obtained; wherein, the observation camera at the preset position is located on one side of the nozzle module on the arrangement plane.

[0105] The focusing module is configured to determine the deviation angle of the nozzle module relative to a set posture based on the shape and position of the marker point in the first image; and to adjust the position of the nozzle module so that the nozzle unit is within the focusing range based on the first relative positional relationship and the deviation angle.

[0106] The functions of each module in the above-mentioned ink droplet observation image processing device correspond to the steps in the above-mentioned ink droplet observation image processing method embodiment, and their functions and implementation processes will not be described in detail here.

[0107] Thirdly, embodiments of this application provide a processing device for ink droplet observation images. The processing device for ink droplet observation images can be a personal computer (PC), a laptop computer, a server, or other devices with data processing capabilities.

[0108] Reference Figure 4 , Figure 4This is a schematic diagram of the hardware structure of the ink droplet observation image processing device involved in the embodiments of this application. In the embodiments of this application, the ink droplet observation image processing device may include a processor, a memory, a communication interface, and a communication bus.

[0109] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0110] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting internal devices within the ink droplet observation image processing equipment, as well as interfaces used for interconnecting the ink droplet observation image processing equipment with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0111] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0112] The processor can be a general-purpose processor, which can call the processing program for ink droplet observation images stored in memory and execute the ink droplet observation image processing method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the ink droplet observation image processing program is called can be referred to in the various embodiments of the ink droplet observation image processing method of this application, and will not be repeated here.

[0113] Those skilled in the art will understand that Figure 4 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0114] Fourthly, embodiments of this application also provide a storage medium.

[0115] The storage medium of this application stores a processing program for ink droplet observation images, wherein when the ink droplet observation image processing program is executed by a processor, the steps of the ink droplet observation image processing method described above are implemented.

[0116] The method implemented when the ink droplet observation image processing program is executed can be referred to in various embodiments of the ink droplet observation image processing method of this application, and will not be repeated here.

[0117] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0118] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0119] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0120] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0121] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0122] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0123] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for processing ink droplet observation images, characterized in that, It includes the following steps: Acquire multiple background and ink droplet observation images captured under set shooting conditions; Based on multiple observed background images, a mean background image is obtained; Based on the background mean image and the set fixed defect detection algorithm, the location information of fixed defects in the background is determined; Based on the location information of the fixed defects, the fixed defects in the background mean image are eliminated to obtain a background mean image without fixed defects; Determine the brightness distribution information of the background mean image without fixed defects; Calculate the brightness reference value of the background of the flying ink droplet observation based on the brightness values ​​of some pixel regions in the brightness distribution information; Based on the brightness reference value, the brightness correction coefficient corresponding to each pixel in the ink droplet observation image is calculated; Based on the location information of the fixed defects, the fixed defects in the ink droplet observation image are removed to obtain a first-level ink droplet observation image without fixed defects; Based on the brightness correction coefficient, the first-level ink droplet observation image without fixed defects is optimized to obtain the second-level ink droplet observation image; The contrast of the secondary ink droplet observation image is enhanced to obtain the flying ink droplet observation result image.

2. The method for processing ink droplet observation images as described in claim 1, characterized in that, The process of enhancing the contrast of the secondary ink droplet observation image to obtain the flying ink droplet observation result image includes the following steps: According to the preset denoising algorithm, pixel noise in the secondary ink droplet observation image is removed to obtain the tertiary ink droplet observation image; Determine the initial grayscale variation range in the three-level ink droplet observation image; The three-level ink droplet observation image is normalized by normalizing the initial grayscale variation range to a larger grayscale range, thus obtaining the flying ink droplet observation result image.

3. The method for processing ink droplet observation images as described in claim 2, characterized in that, The step of removing pixel noise from the secondary ink droplet observation image according to a preset denoising model to obtain the tertiary ink droplet observation image includes the following steps: The pixels in the secondary ink droplet observation image are sorted according to their grayscale value to obtain the pixel grayscale sequence of the secondary ink droplet observation image; By removing pixels from the pixel grayscale sequence of the secondary ink droplet observation image at a predetermined ratio / number, a tertiary ink droplet observation image with outliers removed is obtained.

4. The method for processing ink droplet observation images as described in claim 3, characterized in that, The process of removing pixels from the grayscale sequence of the secondary ink droplet observation image by a predetermined ratio / number of pixels before and after the initial value results in a tertiary ink droplet observation image with outliers removed. The set ratio is one ten-thousandth of the pixel grayscale sequence.

5. The method for processing ink droplet observation images as described in claim 2, characterized in that, The normalization of the three-level ink droplet observation image involves normalizing the initial grayscale variation range to a larger grayscale range, resulting in the flying ink droplet observation image using the following formula: In the formula, I norm (x, y) and I(x, y) are the gray values ​​of the same pixel in the flying ink droplet observation image and the three-level ink droplet observation image, respectively; max(I) and min(I) are the maximum and minimum gray values ​​in the three-level ink droplet observation image, respectively; i max The maximum grayscale value within the target grayscale range.

6. The method for processing ink droplet observation images as described in claim 1, characterized in that, In the process of calculating the brightness correction coefficients corresponding to each pixel in the ink droplet observation image based on the brightness reference value, The brightness correction coefficient for each pixel in the ink droplet observation image is determined based on the ratio between the brightness reference value and the brightness of any pixel in the mean background image without fixed defects.

7. The method for processing ink droplet observation images as described in claim 6, characterized in that, The first-level ink droplet observation image without fixed defects is optimized based on the brightness correction coefficient to obtain the second-level ink droplet observation image. Based on the brightness correction coefficient corresponding to any pixel in the ink droplet observation image, the brightness of the corresponding pixel in the ink droplet observation image without fixed defects is weighted and corrected to obtain the brightness of the corresponding pixel in the secondary ink droplet observation image.

8. A processing apparatus for ink droplet observation images, characterized in that, It includes: The acquisition module is configured to acquire multiple background images and ink droplet observation images captured under set shooting conditions; The first calculation module is configured to obtain a background mean image based on multiple observed background images; determine the location information of fixed defects in the background based on the background mean image and a set fixed defect detection algorithm; and eliminate the fixed defects in the background mean image based on the location information of the fixed defects to obtain a background mean image without fixed defects. The second calculation module is configured to determine the brightness distribution information of the background mean image without fixed defects; and to calculate the brightness reference value of the flying ink droplet observation background based on the brightness values ​​of some pixel regions in the brightness distribution information. Based on the brightness reference value, the brightness correction coefficient corresponding to each pixel in the ink droplet observation image is calculated; An image optimization module is configured to remove fixed defects from the ink droplet observation image based on the location information of the fixed defects, so as to obtain a first-level ink droplet observation image without fixed defects. Based on the brightness correction coefficient, the first-level ink droplet observation image without fixed defects is optimized to obtain the second-level ink droplet observation image; the contrast of the second-level ink droplet observation image is enhanced to obtain the flying ink droplet observation result image.

9. A processing device for ink droplet observation images, characterized in that, The ink droplet observation image processing device includes a processor, a memory, and an ink droplet observation image processing program stored in the memory and executable by the processor, wherein when the ink droplet observation image processing program is executed by the processor, it implements the steps of the ink droplet observation image processing method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores a processing program for ink droplet observation images, wherein when the processing program for ink droplet observation images is executed by a processor, it implements the steps of the ink droplet observation image processing method as described in any one of claims 1 to 7.

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