CN112508922B - Mura detection method, device, terminal equipment and storage medium - Google Patents
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Abstract
The invention discloses a Mura detection method, which comprises the following steps: acquiring a target image of a screen to be detected; performing data accumulation on the data in the target image according to rows or columns to obtain one-dimensional accumulated data; processing the one-dimensional accumulated data by using a time series decomposition algorithm to obtain a trend component and a period component of the one-dimensional accumulated data; obtaining a remainder component having an abnormal value of the one-dimensional accumulated data based on the trend component and the period component; and obtaining a Mura detection result of the screen to be detected according to the residual component with the abnormal value. The invention also discloses a Mura detection device, terminal equipment and a storage medium. Compared with the current Mura observation result, the accuracy of the abnormal value is higher, so the Mura detection method has higher accuracy of the Mura detection result.
Description
Technical Field
The present invention relates to the field of display screen processing, and in particular, to a Mura detection method, apparatus, terminal device, and storage medium.
Background
With the continuous development of information display technology, the OLED (organic light-Emitting Diode, chinese-character) is gradually replacing the conventional LCD by virtue of its advantages of self-luminescence, flexibility, wide viewing angle, fast response speed, simple process, etc., and is rapidly and deeply applied to various fields of modern society. Mura (uneven brightness of a display screen) is a common defect, and is more serious in OLED display than LCD, and the Mura defects are very various, and have large-area color cast and dot, spot, strip, ring, cloud and other forms, wherein each form corresponds to different colors and gray-scale pictures. The main reason is that the OLED process is complex, and the evaporation process is difficult to achieve very good flatness, so that the difference of the luminance of each sub-pixel under the same external condition is large, and each sub-pixel has to be compensated to achieve the panel display standard. Before compensating Mura, the Mura needs to be detected, classified and graded, so that the feedback of the process and the control of the product yield are realized.
In the related art, a Mura detection result of a display screen to be detected is obtained through shooting a target image of the display state of the display screen to be detected and carrying out Mura observation on the target image.
However, when the existing Mura detection method is used for detecting the display screen to be detected, the Mura observation result accuracy of the target image is low, and the Mura detection accuracy is low.
Disclosure of Invention
The invention mainly aims to provide a Mura detection method, a Mura detection device, terminal equipment and a storage medium, and aims to solve the technical problem that when the existing Mura detection method is used for detecting a display screen to be detected in the prior art, the Mura observation result accuracy of a target image is low, so that the Mura detection accuracy is low.
In order to achieve the above object, the present invention provides a Mura detection method, comprising the following steps:
acquiring a target image of a screen to be detected;
performing data accumulation on the data in the target image according to rows or columns to obtain one-dimensional accumulated data;
processing the one-dimensional accumulated data by using a time series decomposition algorithm to obtain a trend component and a period component of the one-dimensional accumulated data;
obtaining a remainder component having an abnormal value of the one-dimensional accumulated data based on the trend component and the period component;
and obtaining a Mura detection result of the screen to be detected according to the residual component with the abnormal value.
Optionally, before the step of performing data accumulation on the data in the target image according to rows or columns to obtain one-dimensional accumulated data, the method further includes:
performing rotation correction on the target image to obtain a corrected target graph;
the step of performing data accumulation on the data in the target image according to rows or columns to obtain one-dimensional accumulated data comprises:
and performing data accumulation on the corrected data in the target image according to rows or columns to obtain one-dimensional accumulated data.
Optionally, before the step of performing data accumulation on the data in the corrected target image according to rows or columns to obtain one-dimensional accumulated data, the method further includes:
acquiring a region of interest of the corrected target image;
obtaining a display area image according to the region of interest;
the step of performing data accumulation on the data in the corrected target image according to rows or columns to obtain one-dimensional accumulated data comprises:
and performing data accumulation on the data in the display area image according to rows or columns to obtain one-dimensional accumulated data.
Optionally, before the step of performing data accumulation on the data in the display area image according to rows or columns to obtain one-dimensional accumulated data, the method further includes:
carrying out mean value filtering processing on the display area image to obtain a filtered display area image;
carrying out contrast enhancement processing on the filtered display area image by utilizing a histogram equalization method to obtain a preprocessed image;
the step of performing data accumulation on the data in the display area image according to rows or columns to obtain one-dimensional accumulated data comprises the following steps:
and performing data accumulation on the data in the preprocessed image according to rows or columns to obtain one-dimensional accumulated data.
Optionally, before the step of performing data accumulation on the data in the preprocessed image according to rows or columns to obtain one-dimensional accumulated data, the method further includes:
carrying out multiple mean filtering treatments on the preprocessed image to obtain a filtered preprocessed image;
obtaining a detection image according to a preset size and the filtered preprocessing image;
the step of performing data accumulation on the data in the preprocessed image according to rows or columns to obtain one-dimensional accumulated data comprises:
and performing data accumulation on the data in the detection image according to rows or columns to obtain one-dimensional accumulated data.
Optionally, the step of obtaining a remainder component having an abnormal value of the one-dimensional accumulated data based on the trend component and the period component includes:
obtaining a remainder component with an abnormal value of the one-dimensional accumulated data by using a formula I, the trend component and the period component;
the first formula is as follows:
Yt＝Tt+St+Rt
wherein, YtFor said one-dimensional accumulated data, TtAs the trend component, StIs the said circumferential component, RtIs the remainder component.
Optionally, the step of obtaining a Mura detection result of the screen to be detected according to the residual component with the abnormal value includes:
carrying out abnormal value positioning on the residual component with the abnormal value by utilizing a preset threshold value to obtain the position information of the abnormal value;
and obtaining a Mura detection result of the screen to be detected according to the position information.
In addition, to achieve the above object, the present invention further provides a Mura detection apparatus, including:
the acquisition module is used for acquiring a target image of a screen to be detected;
the accumulation module is used for carrying out data accumulation on the data in the target image according to rows or columns to obtain one-dimensional accumulated data;
the first obtaining module is used for processing the one-dimensional accumulated data by utilizing a time series decomposition algorithm to obtain a trend component and a period component of the one-dimensional accumulated data;
a second obtaining module for obtaining a remainder component having an abnormal value of the one-dimensional accumulated data based on the trend component and the period component;
and the third obtaining module is used for obtaining a Mura detection result of the screen to be detected according to the abnormal value.
In addition, to achieve the above object, the present invention further provides a terminal device, including: a memory, a processor, and a Mura detection program stored on the memory and executable on the processor, the Mura detection program configured to implement the steps of the Mura detection method as recited in any of the above.
In addition, to achieve the above object, the present invention further provides a storage medium having a Mura detection program stored thereon, wherein the Mura detection program, when executed by a processor, implements the steps of the Mura detection method according to any one of the above aspects.
According to the technical scheme, a Mura detection method is adopted, and a target image of a screen to be detected is obtained; performing data accumulation on the data in the target image according to rows or columns to obtain one-dimensional accumulated data; processing the one-dimensional accumulated data by using a time series decomposition algorithm to obtain a trend component and a period component of the one-dimensional accumulated data; obtaining a remainder component having an abnormal value of the one-dimensional accumulated data based on the trend component and the period component; and obtaining a Mura detection result of the screen to be detected according to the residual component with the abnormal value. The method comprises the steps of performing data accumulation on a display area image corresponding to a target image to obtain corresponding one-dimensional accumulated data, processing the one-dimensional accumulated data by utilizing a time series decomposition algorithm to obtain a trend component and a periodic component, obtaining a remainder component with an abnormal value corresponding to the one-dimensional accumulated data based on the trend component and the periodic component, and obtaining a Mura detection result according to the remainder component with the abnormal value; compared with the current Mura observation result, the accuracy of the residual component with the abnormal value is higher, so the Mura detection method has higher accuracy of the Mura detection result.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a terminal device in a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a Mura detection method according to a first embodiment of the present invention;
FIG. 3 is a schematic diagram of a target image after a rotation correction process according to the present invention;
FIG. 4 is a schematic view of a display region image corresponding to a region of interest according to the present invention;
FIG. 5 is a schematic diagram of a pre-processed image after contrast enhancement according to the present invention;
FIG. 6 is a diagram of the remainder component with outliers of the present invention;
fig. 7 is a block diagram of the Mura detection apparatus according to the first embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
With the continuous development of information display technology, the OLED is gradually replacing the conventional LCD by virtue of its advantages of self-luminescence, flexibility, wide viewing angle, fast response speed, simple manufacturing process and the like, and is rapidly and deeply applied to various fields of modern society, and as the year is 2019, the total investment of the OLED panel in china is over 4000 billion yuan, and only 2019, the market demand of the OLED display screen in china is 2.89 billion pieces. Mura is a common defect, and is more serious in OLED display than LCD, and the Mura defect is very various, and has large-area color cast and dot, spot, strip, ring, cloud, etc. forms, wherein each form corresponds to different color and gray scale picture. The main reason is that the OLED process is complex, and the evaporation process is difficult to achieve very good flatness, so that the difference of the luminance of each sub-pixel under the same external condition is large, and each sub-pixel has to be compensated to achieve the panel display standard. Before compensating Mura, the Mura needs to be detected, classified and graded, so that feedback of the process and management and control of product yield are realized, and the Mura needs to be accurately detected. Because the image of the screen to be detected obtained by shooting through the camera is different from the image seen by human eyes from the screen to be detected, Mura which can be seen by some human eyes can be caused, and the Mura can not be seen or the Mura information is very fuzzy on the shot image, so that the terminal equipment can not distinguish the Mura in the shot image.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a terminal device in a hardware operating environment according to an embodiment of the present invention.
The terminal device may be a User Equipment (UE) such as a Mobile phone, a smart phone, a laptop, a digital broadcast receiver, a Personal Digital Assistant (PDA), a tablet computer (PAD), a handheld device, a vehicle mounted device, a wearable device, a computing device or other processing device connected to a wireless modem, a Mobile Station (MS), etc. The terminal device may be referred to as a user terminal, a portable terminal, a desktop terminal, etc.
In general, a terminal device includes: at least one processor 301, a memory 302, and a Mura detection program stored on the memory and executable on the processor, the Mura detection program configured to implement the steps of the Mura detection method as previously described.
The processor 301 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and so on. The processor 301 may be implemented in at least one hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 301 may also include a main processor and a coprocessor, where the main processor is a processor for processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 301 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the display screen. Processor 301 may also include an AI (Artificial Intelligence) processor for processing relevant Mura detection method operations such that the Mura detection method model may be trained and learned autonomously, improving efficiency and accuracy.
In some embodiments, the terminal may further include: a communication interface 303 and at least one peripheral device. The processor 301, the memory 302 and the communication interface 303 may be connected by a bus or signal lines. Various peripheral devices may be connected to communication interface 303 via a bus, signal line, or circuit board. Specifically, the peripheral device includes: at least one of radio frequency circuitry 304, a display screen 305, and a power source 306.
The communication interface 303 may be used to connect at least one peripheral device related to I/O (Input/Output) to the processor 301 and the memory 302. In some embodiments, processor 301, memory 302, and communication interface 303 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 301, the memory 302 and the communication interface 303 may be implemented on a single chip or circuit board, which is not limited in this embodiment.
The Radio Frequency circuit 304 is used for receiving and transmitting RF (Radio Frequency) signals, also called electromagnetic signals. The radio frequency circuitry 304 communicates with communication networks and other communication devices via electromagnetic signals. The rf circuit 304 converts an electrical signal into an electromagnetic signal to transmit, or converts a received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 304 comprises: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a subscriber identity module card, and so forth. The radio frequency circuitry 304 may communicate with other terminals via at least one wireless communication protocol. The wireless communication protocols include, but are not limited to: metropolitan area networks, various generation mobile communication networks (2G, 3G, 4G, and 5G), Wireless local area networks, and/or WiFi (Wireless Fidelity) networks. In some embodiments, the rf circuit 304 may further include NFC (Near Field Communication) related circuits, which are not limited in this application.
The display screen 305 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof. When the display screen 305 is a touch display screen, the display screen 305 also has the ability to capture touch signals on or over the surface of the display screen 305. The touch signal may be input to the processor 301 as a control signal for processing. At this point, the display screen 305 may also be used to provide virtual buttons and/or a virtual keyboard, also referred to as soft buttons and/or a soft keyboard. In some embodiments, the display screen 305 may be one, the front panel of the electronic device; in other embodiments, the display screens 305 may be at least two, respectively disposed on different surfaces of the electronic device or in a folded design; in still other embodiments, the display screen 305 may be a flexible display screen disposed on a curved surface or a folded surface of the electronic device. Even further, the display screen 305 may be arranged in a non-rectangular irregular figure, i.e. a shaped screen. The Display screen 305 may be made of LCD (liquid crystal Display), OLED (Organic Light-Emitting Diode), and the like.
The power supply 306 is used to power various components in the electronic device. The power source 306 may be alternating current, direct current, disposable or rechargeable. When the power source 306 includes a rechargeable battery, the rechargeable battery may support wired or wireless charging. The rechargeable battery may also be used to support fast charge technology.
Those skilled in the art will appreciate that the configuration shown in fig. 1 does not constitute a limitation of the terminal device and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
In addition, an embodiment of the present invention further provides a storage medium, where a Mura detection program is stored, and when executed by a processor, the Mura detection program implements the steps of the Mura detection method described above. Therefore, a detailed description thereof will be omitted. In addition, the beneficial effects of the same method are not described in detail. For technical details not disclosed in embodiments of the computer-readable storage medium referred to in the present application, reference is made to the description of embodiments of the method of the present application. Determining by way of example, the program instructions may be deployed to be executed on one terminal device, or on multiple terminal devices located at one site, or distributed across multiple sites and interconnected by a communication network.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
Based on the hardware structure, the embodiment of the Mura detection method is provided.
Referring to fig. 1, fig. 1 is a schematic flow chart of a Mura detection method according to a first embodiment of the present invention; the method comprises the following steps:
step S11: and acquiring a target image of the screen to be detected.
It should be noted that the execution subject of the present invention may be the terminal device described above. The screen to be detected is the OLED screen to be detected, and the screen to be detected may include Mura; the acquired screen image to be detected can be a target image of the screen to be detected shot by a camera module of the terminal equipment, or can also be a target image of the screen to be detected shot by an external camera, which is acquired by the terminal equipment, and the invention is not limited; the target image includes a display state of a screen to be detected, and the video camera may be a Mura camera.
It can be understood that when the target image is shot, the screen to be detected is in a light-emitting state, and the display state included in the target image is the display state when the screen to be detected emits light.
Step S12: and performing data accumulation on the data in the target image according to rows or columns to obtain one-dimensional accumulated data.
It should be noted that, performing data accumulation on the data in the target image according to rows or columns, that is, merging the row data of all the data in the target image into one row data, or merging the column data of all the data in the target image into one column data, to obtain corresponding one-dimensional accumulated data.
For example, the target image is 2000 × 1000, that is, 2000 columns of data, each column of data includes 1000, 1000 rows of data, each row of data includes 2000, row accumulation of the target image to obtain one-dimensional accumulated data includes 2000 data, each data is a sum of 1000 data included in one column, and row accumulation of the target image to obtain one-dimensional accumulated data includes 1000 data, each data is a sum of 2000 data included in one row.
Further, before step S12, the method further includes: and performing rotation correction on the target image to obtain a corrected target graph.
Accordingly, step S12 includes: and performing data accumulation on the corrected data in the target image according to rows or columns to obtain one-dimensional accumulated data.
It should be noted that, because the position and the angle of the screen to be detected may not be very accurate in the process of being photographed, a certain rotation angle may exist in the effective area of the screen to be detected on the target image, and the image needs to be rotation-corrected for the accuracy when the image data is accumulated in rows or columns in the later period.
Referring to fig. 3, fig. 3 is a schematic diagram of the target image after the rotation correction processing of the present invention, the upper part of fig. 3 is a schematic diagram of the target image, the lower part of fig. 3 is a schematic diagram of the corrected target image, the rotation correction is performed by using a 4-point correction method, four intersection points A, B, C, D generated by four upper, lower, left and right edges of the screen to be detected are used as affine transformation input, and straight lines AB and CD are transformed into equal and parallel lines; the straight lines AD, BC are transformed to be equally long and parallel.
Further, before step S12, the method further includes: acquiring a region of interest of the corrected target image; and obtaining a display area image according to the region of interest.
Accordingly, step S12 includes: and performing data accumulation on the data in the display area image according to rows or columns to obtain one-dimensional accumulated data.
It should be noted that the corrected target image includes an effective region (an area of interest) and a non-effective region corresponding to the display state of the screen to be detected, and the effective region of the screen to be detected needs to be screened out, so as to continue to perform the data accumulation processing on the effective region.
Referring to fig. 4, fig. 4 is a schematic view of a display area image corresponding to a region of interest of the present invention, the upper portion of fig. 4 is a corrected target image, the lower portion of fig. 4 is a display area image, referring to fig. 3, an area included in ABCD is a region of interest, the region of interest is removed from the corrected target image, and an image corresponding to the region of interest is a display area image.
It can be understood that, when the shape of the screen to be detected is other shapes, that is, when the screen is not a rectangle according to the embodiment of the present invention, the calibration method refers to the calibration method of the present invention, and the region-of-interest obtaining method refers to the embodiment of the present invention; for example, the screen to be detected is a regular hexagon, six sides of the screen to be detected correspond to six points and three pairs of parallel lines; the present invention is not particularly limited.
Further, before step S12, the method further includes: carrying out mean value filtering processing on the display area image to obtain a filtered display area image; and carrying out contrast enhancement processing on the filtered display area image by utilizing a histogram equalization method to obtain a preprocessed image.
Accordingly, step S12 includes: and performing data accumulation on the data in the preprocessed image according to rows or columns to obtain one-dimensional accumulated data.
It should be noted that before performing data accumulation on data in an image according to rows or columns to obtain one-dimensional accumulated data, mean filtering processing needs to be performed on a display area image, and the filtered display area image is processed by using a histogram equalization method to obtain a contrast-enhanced preprocessed image, so as to continue performing data accumulation on the data in the preprocessed image according to rows or columns to obtain the one-dimensional accumulated data; it is understood that the histogram equalization method is a method of adjusting contrast using an image histogram.
In a specific application, the filtering process may be a 3 × 3 mean filtering process performed on the display area image once, or may be another filtering process, which is not limited in the present invention.
Referring to fig. 5, fig. 5 is a schematic diagram of a preprocessed image after contrast enhancement according to the present invention, referring to a lower display region image in fig. 4, such as a region image before filtering and contrast enhancement, and fig. 5 is a preprocessed image after filtering and contrast enhancement.
Further, before step S12, the method further includes: carrying out multiple mean filtering treatments on the preprocessed image to obtain a filtered preprocessed image; and obtaining a detection image according to a preset size and the filtered preprocessing image.
Accordingly, step S12 includes: and performing data accumulation on the data in the detection image according to rows or columns to obtain one-dimensional accumulated data.
It should be noted that, the histogram equalization method is also used to enhance the image noise in the filtered display area image processing process, so that 3-5 times of 3 × 3 mean filtering is performed on the preprocessed image to eliminate the noise in the image, it can be understood that the user can adjust the filtering times according to the requirement, the present invention is not limited, and 3-5 times of filtering processing is a better choice.
In addition, the preset size is a standard size of the detected image, different requirements correspond to different preset sizes, and a user can set the preset size according to the requirements of the user, so that the method is not limited; the preset size depends on the size of a screen to be detected and the resolution of the Mura camera, the filtered preprocessed image smaller than the preset size is processed by an interpolation method, and the filtered preprocessed image larger than the preset size is processed by a down-sampling method.
Step S13: and processing the one-dimensional accumulated data by using a time series decomposition algorithm to obtain a trend component and a period component of the one-dimensional accumulated data.
Step S14: based on the trend component and the period component, a remainder component having an abnormal value of the one-dimensional accumulated data is obtained.
It should be noted that the abnormal value in the remainder component is the value corresponding to the Mura of the screen to be detected.
Specifically, step S14 includes: obtaining a remainder component with an abnormal value of the one-dimensional accumulated data by using a formula I, the trend component and the period component;
the first formula is as follows:
Yt＝Tt+St+Rt
wherein, YtFor said one-dimensional accumulated data, TtAs the trend component, StIs the said circumferential component, RtIs the remainder component.
It should be noted that the length of the one-dimensional accumulated data is N, and each data in the one-dimensional accumulated data is considered as a trend component T according to the idea of the time-series decomposition algorithmtPeriodic component StAnd a remainder component RtIs expressed as:
Yt＝Tt+St+Rt t＝1,...,N
the time sequence decomposition algorithm comprises an inner circulation part and an outer circulation part, wherein the inner circulation mainly carries out trend fitting and calculation of a period component, so that the algorithm process depends on period setting, the length of each subsequence sample is taken as a period set value of period analysis and is represented by n (p), the total data comprises data of a plurality of periods, sample points at the same position of each period form a subsequence which is called a period subsequence, and the total n (p) of the subsequences are easily known; the outer loop is mainly used for adjusting the regression weight and reducing the influence of outliers on the fitting of the periodic component and the trend component.
The trend component and the period component at the k-1 th end of the internal circulation part are initially
After the processing of the time series decomposition algorithm, the residual component R comprising the abnormal value can be obtainedt。
Step S15: and obtaining a Mura detection result of the screen to be detected according to the residual component with the abnormal value.
It should be noted that, when the residual component with the abnormal value is obtained, the residual component with the abnormal value is processed to obtain the Mura detection result of the screen to be detected, and the abnormal value in the residual component is a value corresponding to the abnormal display state of the Mura area of the screen to be detected.
Further, step S15 includes: carrying out abnormal value positioning on the residual component with the abnormal value by utilizing a preset threshold value to obtain the position information of the abnormal value; and obtaining a Mura detection result of the screen to be detected according to the position information.
It should be noted that an abnormal value in the remainder component corresponds to a display area of Mura, a normal value in the remainder component corresponds to a normal display area, and a value of the remainder component corresponding to the normal display area is within a preset threshold range; it can be understood that, in order to ensure that the Mura detection result is better, the preset threshold is not too large, and the user can set the threshold according to the own requirements and the specific detection conditions, which is not limited by the present invention.
It can be understood that the position of the abnormal value screened by using the preset threshold is the position information, the position information corresponds to the area of the Mura, the area where the Mura is located can be obtained according to the position, and the Mura detection result can be obtained according to the area where the Mura is located, and the detection result includes the area information where the Mura is located.
Referring to fig. 6, fig. 6 is a schematic diagram of the remainder component having an abnormal value in the present invention, where the first line is the one-dimensional accumulated data corresponding to the screen to be detected, the second line trend component, the third line is the periodic component, and the fourth line is the remainder component, and the over-high point and the over-location point where the remainder component fluctuates greatly are the points corresponding to the abnormal value.
According to the technical scheme, a Mura detection method is adopted, and a target image of a screen to be detected is obtained; performing data accumulation on the data in the target image according to rows or columns to obtain one-dimensional accumulated data; processing the one-dimensional accumulated data by using a time series decomposition algorithm to obtain a trend component and a period component of the one-dimensional accumulated data; obtaining a remainder component having an abnormal value of the one-dimensional accumulated data based on the trend component and the period component; and obtaining a Mura detection result of the screen to be detected according to the residual component with the abnormal value. The method comprises the steps of performing data accumulation on a display area image corresponding to a target image to obtain corresponding one-dimensional accumulated data, processing the one-dimensional accumulated data by utilizing a time series decomposition algorithm to obtain a trend component and a periodic component, obtaining a remainder component with an abnormal value corresponding to the one-dimensional accumulated data based on the trend component and the periodic component, and obtaining a Mura detection result according to the remainder component with the abnormal value; compared with the current Mura observation result, the accuracy of the residual component with the abnormal value is higher, so the Mura detection method has higher accuracy of the Mura detection result.
Referring to fig. 7, fig. 7 is a block diagram of a first embodiment of the Mura detection apparatus according to the present invention, the apparatus including:
the acquisition module 10 is used for acquiring a target image of a screen to be detected;
the accumulation module 20 is configured to perform data accumulation on the data in the target image according to rows or columns to obtain one-dimensional accumulated data;
a first obtaining module 30, configured to process the one-dimensional accumulated data by using a time series decomposition algorithm, so as to obtain a trend component and a period component of the one-dimensional accumulated data;
a second obtaining module 40, configured to obtain a remainder component having an abnormal value of the one-dimensional accumulated data based on the trend component and the period component;
and a third obtaining module 50, configured to obtain a Mura detection result of the screen to be detected according to the abnormal value.
The above description is only an alternative embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications and equivalents of the present invention, which are made by the contents of the present specification and the accompanying drawings, or directly/indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (6)
1. A Mura detection method, comprising the steps of:
acquiring a target image of a screen to be detected in a luminous state;
performing rotation correction on the target image to obtain a corrected target graph;
acquiring a region of interest of the corrected target image;
obtaining a display area image according to the region of interest;
carrying out mean value filtering processing on the display area image to obtain a filtered display area image;
carrying out contrast enhancement processing on the filtered display area image by utilizing a histogram equalization method to obtain a preprocessed image;
3-5 times of 3 × 3 mean filtering processing is carried out on the preprocessed image to obtain a filtered preprocessed image;
obtaining a detection image according to a preset size and the filtered preprocessing image;
performing data accumulation on the data in the detection image according to rows or columns to obtain one-dimensional accumulated data;
processing the one-dimensional accumulated data by using a time series decomposition algorithm to obtain a trend component and a period component of the one-dimensional accumulated data;
obtaining a remainder component having an abnormal value of the one-dimensional accumulated data based on the trend component and the period component;
and obtaining a Mura detection result of the screen to be detected according to the residual component with the abnormal value.
2. The Mura detection method of claim 1, wherein the step of obtaining the remainder component of the one-dimensional accumulated data having an outlier based on the trend component and the periodic component comprises:
obtaining a remainder component with an abnormal value of the one-dimensional accumulated data by using a formula I, the trend component and the period component;
the first formula is as follows:
Yt＝tt+st+rt
wherein, YtFor said one-dimensional accumulated data, TtAs the trend component, StIs the periodic component, RtIs the remainder component.
3. The Mura detection method of claim 2, wherein the step of obtaining the Mura detection result of the screen to be detected according to the residual component having the abnormal value comprises:
carrying out abnormal value positioning on the residual component with the abnormal value by utilizing a preset threshold value to obtain the position information of the abnormal value;
and obtaining a Mura detection result of the screen to be detected according to the position information.
4. A Mura detection apparatus, comprising:
the acquisition module is used for acquiring a target image of a screen to be detected in a luminous state; performing rotation correction on the target image to obtain a corrected target graph; acquiring a region of interest of the corrected target image; obtaining a display area image according to the region of interest; carrying out mean value filtering processing on the display area image to obtain a filtered display area image; carrying out contrast enhancement processing on the filtered display area image by utilizing a histogram equalization method to obtain a preprocessed image; 3-5 times of 3 × 3 mean filtering processing is carried out on the preprocessed image to obtain a filtered preprocessed image; obtaining a detection image according to a preset size and the filtered preprocessing image;
the accumulation module is used for carrying out data accumulation on the data in the detection image according to rows or columns to obtain one-dimensional accumulated data;
the first obtaining module is used for processing the one-dimensional accumulated data by utilizing a time series decomposition algorithm to obtain a trend component and a period component of the one-dimensional accumulated data;
a second obtaining module for obtaining a remainder component having an abnormal value of the one-dimensional accumulated data based on the trend component and the period component;
and the third obtaining module is used for obtaining a Mura detection result of the screen to be detected according to the abnormal value.
5. A terminal device, characterized in that the terminal device comprises: a memory, a processor, and a Mura detection program stored on the memory and executable on the processor, the Mura detection program configured to implement the steps of the Mura detection method of any of claims 1-3.
6. A storage medium having stored thereon a Mura detection program which, when executed by a processor, implements the steps of the Mura detection method according to any one of claims 1 to 3.
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