CN115243017A - Method and equipment for improving image quality - Google Patents
Method and equipment for improving image quality Download PDFInfo
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- CN115243017A CN115243017A CN202210928475.1A CN202210928475A CN115243017A CN 115243017 A CN115243017 A CN 115243017A CN 202210928475 A CN202210928475 A CN 202210928475A CN 115243017 A CN115243017 A CN 115243017A
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- 238000000034 method Methods 0.000 title claims abstract description 39
- 239000006185 dispersion Substances 0.000 claims abstract description 159
- 238000012360 testing method Methods 0.000 claims abstract description 55
- 238000005315 distribution function Methods 0.000 claims abstract description 33
- 230000006870 function Effects 0.000 claims description 12
- 238000003384 imaging method Methods 0.000 claims description 4
- 230000002829 reductive effect Effects 0.000 abstract description 5
- 230000015654 memory Effects 0.000 description 15
- 238000010586 diagram Methods 0.000 description 8
- 230000005291 magnetic effect Effects 0.000 description 5
- 238000004590 computer program Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 239000003086 colorant Substances 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000008030 elimination Effects 0.000 description 2
- 238000003379 elimination reaction Methods 0.000 description 2
- 230000000670 limiting effect Effects 0.000 description 2
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- 230000006978 adaptation Effects 0.000 description 1
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- 238000005286 illumination Methods 0.000 description 1
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N17/00—Diagnosis, testing or measuring for television systems or their details
- H04N17/002—Diagnosis, testing or measuring for television systems or their details for television cameras
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N9/00—Details of colour television systems
- H04N9/64—Circuits for processing colour signals
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Abstract
The method comprises the steps of firstly laying out a test scene, wherein the test scene comprises a background wall, a test chart arranged on the background wall, a camera module to be tested positioned right in front of the test chart and light sources respectively arranged at two sides of the camera module to be tested; adjusting the brightness of a light source, setting different exposure times, and shooting a test chart card through a camera module to be tested so as to obtain dispersion data under different field angles FOV; carrying out dispersion analysis and fitting on dispersion data under different FOVs to obtain dispersion distribution functions corresponding to the dispersion data of the different FOVs; the dispersion data under the target FOV are obtained, the target dispersion distribution function corresponding to the dispersion data of the target FOV is called, and the dispersion value under the target FOV is corrected, so that dynamic adjustment and correction are realized according to the position of the FOV where dispersion occurs, an image is better restored, dispersion restoration is carried out according to an actual scene, and detail loss is reduced.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for improving image quality.
Background
In the prior art, since the white light is formed by mixing light rays with different colors, and the wavelengths of the light rays with different colors are different, when the light rays pass through the lens, the dispersion phenomenon as shown in fig. 1 occurs because of different refractive indexes. In general, dispersion occurs mostly in a high-brightness and high-contrast environment, such as standing under a tree for pitching, which is the case at the edge of foliage and sky.
At present, the mode of less dispersion influence mainly comprises two aspects of hardware and software algorithm, wherein the hardware is provided with a lens for developing and inhibiting dispersion, and the software algorithm is mainly provided with functions of removing purple fringing and the like, so that RGB values are locally adjusted to achieve the purpose of adjustment.
In the implementation mode in the prior art, a three-channel difference threshold value is set according to local RGB distribution, adjustment and adaptation are carried out when the threshold value is exceeded, the influence of chromatic dispersion on vision can be weakened, but the effect is general, and original information can be lost in the removing process.
Disclosure of Invention
An object of the present application is to provide a method and apparatus for improving image quality, which can dynamically adjust and correct a lens field angle, which is covered by a lens of an apparatus, according to a lens field angle at which chromatic dispersion occurs, in consideration of a lens field angle, different illumination, exposure, and the like, for dispersion elimination, thereby better restoring an image and ensuring image quality.
According to an aspect of the present application, there is provided a method of improving image quality, wherein the method includes:
laying out a test scene, wherein the test scene comprises a background wall, a test chart arranged on the background wall, a camera module to be tested positioned right in front of the test chart and light sources respectively arranged at two sides of the camera module to be tested;
adjusting the brightness of the light source, setting different exposure times, and shooting the test chart card through the camera module to be tested so as to obtain dispersion data under different field angles FOV;
carrying out dispersion analysis and fitting on dispersion data under different FOVs to obtain dispersion distribution functions corresponding to the dispersion data of the different FOVs;
acquiring dispersion data under a target FOV;
and calling a target dispersion distribution function corresponding to the target FOV in dispersion distribution functions corresponding to the dispersion data of different FOVs, and correcting the dispersion value in the dispersion data under the target FOV.
Further, in the above method, the performing dispersion analysis and fitting on the dispersion data under different FOVs to obtain dispersion distribution functions corresponding to the dispersion data of different FOVs includes:
carrying out dispersion analysis on dispersion data under different FOVs to obtain dispersion distribution results corresponding to the dispersion data of the different FOVs;
and performing function fitting on dispersion distribution results corresponding to the dispersion data of different FOVs to obtain dispersion distribution functions corresponding to the dispersion data of different FOVs.
Further, in the above method, when the FOV of the camera module to be tested is 160 ° or less, the test card is a planar card and is used to be tiled on the background wall.
Further, in the method, when the FOV of the camera module to be tested is greater than 160 °, the test graphic card is a circular arc graphic card to adapt to imaging in a focal plane under different FOVs.
According to another aspect of the present application, there is also provided a non-volatile storage medium having computer readable instructions stored thereon, which, when executed by a processor, cause the processor to implement the method of improving image quality as described above.
According to another aspect of the present application, there is also provided an apparatus for improving image quality, wherein the apparatus includes:
one or more processors;
a computer-readable medium for storing one or more computer-readable instructions,
when executed by the one or more processors, cause the one or more processors to implement a method of improving image quality as described above.
Compared with the prior art, the test scene is distributed firstly, and comprises a background wall, a test chart arranged on the background wall, a camera module to be tested positioned right in front of the test chart and light sources respectively arranged at two sides of the camera module to be tested; adjusting the brightness of the light source, setting different exposure times, and shooting the test chart card through the camera module to be tested so as to obtain dispersion data under different field angles FOV; carrying out dispersion analysis and fitting on dispersion data under different FOVs to obtain dispersion distribution functions corresponding to the dispersion data of the different FOVs; in the subsequent picture shooting process, if the target FOV area has dispersion, acquiring dispersion data under the target FOV, calling a target dispersion distribution function corresponding to the target FOV in dispersion distribution functions corresponding to different FOV dispersion data, and correcting the dispersion value in the dispersion data under the target FOV. The multi-scene test is realized, the dispersion distribution functions under different FOVs are recorded, and in an actual scene with dispersion, the dynamic adjustment and correction are carried out according to the position of the FOV where the dispersion occurs, so that an image is better restored, the dispersion restoration is carried out according to the actual scene, and the detail loss is reduced.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is a schematic diagram of a prior art in which different color light passes through a lens and then has different refractive indexes to cause different chromatic dispersion;
FIG. 2 illustrates a flow diagram of a method of improving image quality in accordance with an aspect of the subject application;
FIG. 3 illustrates a schematic diagram of a test scenario for a layout in a method of improving image quality in accordance with an aspect of the subject application;
FIG. 4 illustrates a schematic diagram of an illustration of a point-diagram test chart shot in a method of improving image quality according to one aspect of the present application;
fig. 5 shows a schematic diagram of a dispersion distribution function corresponding to dispersion data of different FOVs in a method of improving image quality according to an aspect of the present application.
The same or similar reference numbers in the drawings identify the same or similar elements.
Detailed Description
The present application is described in further detail below with reference to the attached figures.
In a typical configuration of the present application, the terminal, the device serving the network, and the trusted party each include one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both permanent and non-permanent, removable and non-removable media, may implement the information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include non-transitory computer readable media (transient media), such as modulated data signals and carrier waves.
As shown in fig. 2, one aspect of the present application provides a flowchart of a method for improving image quality, where the method includes steps S11, S12, S13, S14, and S15, and specifically includes the following steps:
step S11, laying out a test scene, wherein the test scene comprises a background wall, a test chart arranged on the background wall, a camera module to be tested (corresponding to the test camera in FIG. 2) positioned right in front of the test chart, and light sources respectively arranged at two sides of the camera module to be tested, as shown in FIG. 2, wherein the background wall can comprise but is not limited to a matte wall or a cloth curtain, and the like, and the distance between the camera module to be tested and the test chart needs to be adjusted to a proper distance so as to better shoot pictures.
It should be noted that the shape of the test card includes, but is not limited to, a plane card, a circular arc card, etc., and the type of the test card may be changed, and the test card including, but not limited to, a dot pattern, a checkerboard, or other cards with similar features may be included within the scope of the present application.
In order to implement the test of different test environments, in step S12, the brightness of the light source in fig. 2 is adjusted, different exposure times are set for the test, and the test chart is photographed by the to-be-tested camera module to obtain dispersion data under different field angles FOV.
And S13, performing dispersion analysis and fitting on the dispersion data under different FOVs to obtain dispersion distribution functions corresponding to the dispersion data of different FOVs, so that multi-scene testing can be performed, and the dispersion distribution functions under different FOVs are recorded, so that dispersion restoration can be performed according to actual scenes in the subsequent process, and the loss of details is reduced.
In an actual scene, if there is dispersion in the target FOV, step S14 acquires dispersion data in the target FOV.
And S15, calling a target dispersion distribution function corresponding to the target FOV in dispersion distribution functions corresponding to the dispersion data of different FOVs, and correcting the dispersion value in the dispersion data under the target FOV.
Through the steps S11 to S15, multi-scene testing is realized, dispersion distribution functions under different FOVs are recorded, and in an actual scene with dispersion, dynamic adjustment and correction are performed according to the position of the FOV where the dispersion occurs, so that an image is better restored.
In an actual application scenario of the present application, as shown in fig. 4, a dot diagram is taken as an illustration diagram of a test chart for shooting, dispersion data under different field angles FOVs are obtained, dispersion analysis and fitting are performed on the dispersion data under the different field angles FOVs, dispersion distribution conditions corresponding to the dispersion data of the different field angles FOVs are obtained, and corresponding dispersion distribution functions are recorded, as shown in fig. 5. In fig. 5, the lower horizontal axis represents pixel coordinates in a diagonal direction from the center of a picture pixel; the upper horizontal axis represents the proportion of pixels in the diagonal direction from the center of the picture pixel to the length of the diagonal pixel; the left vertical axis represents pixel offset error; the right vertical axis represents the percentage of vertical pixels of the picture that are occupied by pixel offset errors. The first fitted curve from top to bottom of the curves in fig. 5 represents the red channel minus the pixel value of the blue channel, the second fitted curve represents the red channel minus the pixel value of the green channel at each position, and the third fitted curve represents the blue channel minus the pixel value of the green channel.
The distribution conditions of the dispersion data under different FOVs are known through the fitted dispersion distribution functions corresponding to the dispersion data under different FOVs, and the dispersion distribution functions are recorded into functions, so that in a later shot picture, if the dispersion condition of a certain target FOV area occurs, the corresponding dispersion value in the dispersion distribution function corresponding to the target FOV can be directly called to correct the actual shot value.
It should be noted that, in the field with strict data requirements, more detailed tests can be performed to obtain more dispersion functions. The light source and the exposure time are adjusted, various practical scenes can be simulated, and after data are obtained, the method can be applied to various modes, scenery, night scenes and other scenes shot by mobile equipment such as a mobile phone, an iPad and the like.
Next to the above embodiment of the present application, the step S13 performs dispersion analysis and fitting on the dispersion data under different FOVs to obtain dispersion distribution functions corresponding to the dispersion data of different FOVs, and specifically includes:
carrying out dispersion analysis on dispersion data under different FOVs to obtain dispersion distribution results corresponding to the dispersion data of the different FOVs;
and performing function fitting on dispersion distribution results corresponding to the dispersion data of different FOVs to obtain dispersion distribution functions corresponding to the dispersion data of different FOVs.
As shown in fig. 5, after obtaining the dispersion data under different FOVs, first, performing dispersion analysis on the dispersion data under different FOVs to obtain dispersion distribution results corresponding to the dispersion data of different FOVs, as shown in fig. 5, for facilitating accurate description of the distribution conditions of the dispersion data of different FOVs, it is also necessary to perform function fitting on the dispersion distribution results corresponding to the dispersion data of different FOVs, as shown in fig. 5, the dispersion distribution results corresponding to the dispersion functions of different FOVs are fitted into dispersion distribution functions corresponding to different FOVs, so as to implement function fitting on the dispersion data under different FOVs.
Next, in the above embodiment of the present application, when the FOV of the camera module to be tested is less than or equal to 160 °, the test chart is a plane chart and can be flatly laid on the background wall shown in fig. 3.
Next, in the above embodiment of the present application, when the FOV of the camera module to be tested is greater than 160 °, the test chart is an arc chart so as to adapt to imaging in one focal plane under different FOVs. For example, for a vehicle-mounted camera module to be tested with a large FOV, the FOV can reach about 200 °, and in order to take a picture of a full picture of a test chart, the test chart with an arc shape can be replaced to adapt to imaging in one focal plane under different FOVs; meanwhile, the shot dispersion data can be original Raw data and the like, dispersion analysis is carried out on the Raw data to obtain a dispersion distribution result, and then a dispersion elimination step can be added during Image Signal Processing (ISP), so that the obtained data is more excellent, wherein the Raw data is all gray data of an Image recorded by a photosensitive element and is data visible to human eyes after being processed by the ISP, the camera module to be detected which is better suitable for a large FOV is realized, the data format support is richer (such as Raw data), dispersion is reduced from the source through the Raw data, and the processed Image effect is better.
According to another aspect of the present application, there is also provided a non-volatile storage medium having computer readable instructions stored thereon, which, when executed by a processor, cause the processor to implement the method of improving image quality as described above.
According to another aspect of the present application, there is also provided an apparatus for improving image quality, wherein the apparatus includes:
one or more processors;
a computer-readable medium for storing one or more computer-readable instructions,
when executed by the one or more processors, cause the one or more processors to implement a method of improving image quality as described above.
Here, for details of each embodiment of the apparatus for improving image quality, reference may be made to corresponding parts of the embodiments of the method for improving image quality, and details are not repeated here.
In summary, according to the method, a test scene is firstly distributed, wherein the test scene comprises a background wall, a test chart arranged on the background wall, a camera module to be tested positioned right in front of the test chart and light sources respectively arranged at two sides of the camera module to be tested; adjusting the brightness of the light source, setting different exposure times, and shooting the test chart card through the camera module to be tested so as to obtain dispersion data under different field angles FOV; carrying out dispersion analysis and fitting on dispersion data under different FOVs to obtain dispersion distribution functions corresponding to the dispersion data of the different FOVs; in the subsequent picture shooting process, if the target FOV area has dispersion, acquiring dispersion data under the target FOV, calling a target dispersion distribution function corresponding to the target FOV in dispersion distribution functions corresponding to different FOV dispersion data, and correcting the dispersion value in the dispersion data under the target FOV. The multi-scene test is realized, the dispersion distribution functions under different FOVs are recorded, and in an actual scene with dispersion, the dynamic adjustment and correction are carried out according to the position of the FOV where the dispersion occurs, so that an image is better restored, the dispersion restoration is carried out according to the actual scene, and the detail loss is reduced.
It should be noted that the present application may be implemented in software and/or a combination of software and hardware, for example, implemented using Application Specific Integrated Circuits (ASICs), general purpose computers or any other similar hardware devices. In one embodiment, the software programs of the present application may be executed by a processor to implement the steps or functions described above. Likewise, the software programs (including associated data structures) of the present application may be stored in a computer readable recording medium, such as RAM memory, magnetic or optical drive or diskette and the like. Additionally, some of the steps or functions of the present application may be implemented in hardware, for example, as circuitry that cooperates with the processor to perform various steps or functions.
In addition, some of the present application may be implemented as a computer program product, such as computer program instructions, which when executed by a computer, may invoke or provide methods and/or techniques in accordance with the present application through the operation of the computer. Program instructions which invoke the methods of the present application may be stored on a fixed or removable recording medium and/or transmitted via a data stream on a broadcast or other signal-bearing medium and/or stored within a working memory of a computer device operating in accordance with the program instructions. An embodiment according to the present application comprises an apparatus comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein the computer program instructions, when executed by the processor, trigger the apparatus to perform a method and/or a solution according to the aforementioned embodiments of the present application.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the apparatus claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Claims (6)
1. A method of improving image quality, wherein the method comprises:
laying out a test scene, wherein the test scene comprises a background wall, a test chart arranged on the background wall, a camera module to be tested positioned right in front of the test chart and light sources respectively arranged at two sides of the camera module to be tested;
adjusting the brightness of the light source, setting different exposure times, and shooting the test chart card through the camera module to be tested so as to obtain dispersion data under different field angles FOV;
carrying out dispersion analysis and fitting on dispersion data under different FOVs to obtain dispersion distribution functions corresponding to the dispersion data of the different FOVs;
acquiring dispersion data under a target FOV;
and calling a target dispersion distribution function corresponding to the target FOV in dispersion distribution functions corresponding to the dispersion data of different FOVs, and correcting the dispersion value in the dispersion data under the target FOV.
2. The method of claim 1, wherein the performing dispersion analysis and fitting on the dispersion data under different FOVs to obtain dispersion distribution functions corresponding to the dispersion data of different FOVs comprises:
carrying out dispersion analysis on dispersion data under different FOVs to obtain dispersion distribution results corresponding to the dispersion data of the different FOVs;
and performing function fitting on dispersion distribution results corresponding to the dispersion data of different FOVs to obtain dispersion distribution functions corresponding to the dispersion data of different FOVs.
3. The method of claim 1, wherein when the FOV of the camera module under test is 160 ° or less, the test card is a plane card for tiling placement on the background wall.
4. The method of claim 1, wherein when the FOV of the camera module under test is greater than 160 °, the test card is a circular arc card to adapt to imaging in one focal plane under different FOVs.
5. A non-transitory storage medium having stored thereon computer readable instructions which, when executed by a processor, cause the processor to implement the method of any one of claims 1 to 4.
6. An apparatus for improving image quality, wherein the apparatus comprises:
one or more processors;
a computer-readable medium for storing one or more computer-readable instructions,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-4.
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