CN112288785A - Data processing method, system and storage medium for sub-aperture scanning flat field calibration - Google Patents

Data processing method, system and storage medium for sub-aperture scanning flat field calibration Download PDF

Info

Publication number
CN112288785A
CN112288785A CN202011183115.0A CN202011183115A CN112288785A CN 112288785 A CN112288785 A CN 112288785A CN 202011183115 A CN202011183115 A CN 202011183115A CN 112288785 A CN112288785 A CN 112288785A
Authority
CN
China
Prior art keywords
image
sub
aperture
spot
light spot
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202011183115.0A
Other languages
Chinese (zh)
Other versions
CN112288785B (en
Inventor
郑家宁
张晓辉
张宁
咸竞天
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Changchun Institute of Optics Fine Mechanics and Physics of CAS
Original Assignee
Changchun Institute of Optics Fine Mechanics and Physics of CAS
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Changchun Institute of Optics Fine Mechanics and Physics of CAS filed Critical Changchun Institute of Optics Fine Mechanics and Physics of CAS
Priority to CN202011183115.0A priority Critical patent/CN112288785B/en
Publication of CN112288785A publication Critical patent/CN112288785A/en
Application granted granted Critical
Publication of CN112288785B publication Critical patent/CN112288785B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
    • G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
    • G06T7/344—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods involving models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/90—Determination of colour characteristics

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Image Processing (AREA)

Abstract

The invention discloses a data processing method, a system and a storage medium for sub-aperture scanning flat field calibration, wherein the method comprises the following steps: s1: obtaining an image I of a sub-aperture light source passing through a telescopei(ii) a S2: the image IiIdentifying crosstalk information and light spot information to obtain a template A containing the crosstalk information and the light spot information; s3: registering the image I according to the template ARiPerforming spot segmentation to obtain a spot radius RiAnd spot center Oi(ii) a S4: for all spot radii RiAccumulating and averaging to obtain consistent light spot radius R of the image formed by the telescope sub-aperture light source; s5: using R as the radius of the light spot, OiDetermining a spot circle area for the spot center and calculating the IiThe gray level mean value DN of all pixel points in the light spot circle area in the imagei. By the method, the interference of the detector crosstalk on the calibration data processing can be effectively removed, and the flat field calibration precision is effectively improved.

Description

Data processing method, system and storage medium for sub-aperture scanning flat field calibration
Technical Field
The invention relates to the field of radiometric calibration, in particular to a data processing method, a data processing system and a storage medium for sub-aperture scanning flat-field calibration.
Background
In order to improve the resolution and light collection ability of optical telescopes, telescopes have been known in succession since the 21 st century. The accurate flat field calibration of the telescope is an important ring in the development and operation of the telescope. The reliability of the observed data and the depth and breadth of the application depend to a large extent on the flat field calibration accuracy of the instrument.
The flat field calibration method based on sub-aperture scanning simulates a full-aperture uniform and stable flat field light source in a sub-aperture light source scanning mode according to the idea of small splicing. The method can effectively improve the uncertainty of the traditional large-size flat field screen method caused by uneven luminosity and stray light. Meanwhile, the problems that the large-aperture integrating sphere is difficult to prepare and is not suitable for the external field calibration experiment of the foundation telescope can be effectively avoided. The scanning mechanism based on the sub-aperture scanning method is shown in fig. 1. The sub-aperture scanning mechanism can realize horizontal and vertical two-dimensional position adjustment and direction adjustment of pitching and azimuth. The sub-aperture light source irradiates the optical system to be measured after passing through the collimating optical system.
When the emergent light of the sub-aperture light source is a collimated parallel beam, the method has a plurality of remarkable advantages, such as the interference of stray light on flat-field radiation calibration can be reduced, the crosstalk and ghost images of the imaging detector in a calibration image scanned by the sub-aperture can be effectively distinguished, the crosstalk is eliminated, and the influence of the crosstalk of the detector on a calibration result is avoided.
The process of processing the calibration image data based on the scheme is shown in fig. 2, and the full-aperture flat field light source of the full field of view is obtained through the position of the sub-aperture light source and the field of view scanning mode and through the subsequent image processing. Firstly, adjusting and fixing the pointing angle of the sub-aperture light source to enable the sub-aperture light source to be aligned with a certain view field of the telescope; then respectively imaging the telescope according to the scanning path, and obtaining a full-aperture image in a single view field through image processing; then adjusting the pointing angle of the sub-aperture light source, and imaging the telescope according to the scanning path to obtain full-aperture images under different fields of view; finally, a flat field image is obtained through image processing, and then the calibration coefficient of the optical system is solved. Aiming at data processing of sub-aperture scanning flat field calibration, the invention provides a method for quickly and effectively extracting light spots of a single sub-aperture calibration image and eliminating crosstalk.
Data processing of the calibration image based on the sub-aperture scanning method is very demanding, and additional calibration uncertainty may be introduced by improper calibration data processing. For example, crosstalk and nonlinearity of the detector, processing and adjustment errors of an optical system, a Brignter Fatter effect, light source light intensity change, exposure time, environment temperature change, stray light interference and the like in the calibration process all affect the flat-field calibration accuracy. The single sub-aperture calibration image is usually a weak light spot image with a weak gray scale, and in addition, due to the pointing error of the sub-aperture light source, the light spot images at different scanning positions have slight deviation. The extraction of the light spot image and the calculation of the brightness value of the light spot image directly influence the final calibration precision. In addition, how to realize the discrimination of crosstalk and ghost images and eliminate ghost images are also important factors influencing the final calibration precision. The conventional image preprocessing and target recognition and segmentation algorithm is difficult to be directly applied to the calibration algorithm based on the sub-aperture scanning, and all links of the sub-aperture scanning calibration algorithm need to be correspondingly designed, so that the final calibration precision is improved.
Disclosure of Invention
The present invention aims to provide a technical solution for data processing of sub-aperture scanning flat-field calibration, which is used to solve the problem of low accuracy of the existing calibration algorithm based on sub-aperture scanning.
The object of the invention can be achieved by the following technical measures:
a first aspect of the present application provides a data processing method for sub-aperture scanning flat-field scaling, the method comprising:
s1: obtaining an image I of a sub-aperture light source passing through a telescopei；
S2: the image IiIdentifying crosstalk information and light spot information to obtain a template A containing the crosstalk information and the light spot information; the method specifically comprises the following steps: s21: for the image IiCarrying out registration to obtain a registration image IRi；
S3: registering the image I according to the template ARiPerforming spot segmentation to obtain a spot radius RiAnd spot center Oi；
S4: for all spot radii RiAccumulating and averaging to obtain consistent light spot radius R of the image formed by the telescope sub-aperture light source;
s5: using R as the radius of the light spot, OiDetermining a spot circle area for the spot center and calculating the IiThe gray level mean value DN of all pixel points in the light spot circle area in the imagei。
S6: for all DN calculated based on sub-apertureiAnd summing, and calculating the full aperture gray scale mean value DN under the current field according to the following formula: DN ═ k ∑ DNiWherein k is a correction coefficient;
s7: and acquiring a full-aperture gray mean value DN in the current field, and establishing a mapping relation between the DN and the radiation brightness of the light source.
Further, step S1 includes:
s11: controlling the opening of the sub-aperture light source, imaging the telescope, and acquiring a background noise image I about the environment and the systemnoise1；
S12: controlling the sub-aperture light source to be closed, imaging the telescope and acquiring the sub-aperture light source at LiImaging at a location I0；
S13: controlling the sub-aperture light source to be closed, imaging the telescope, and acquiring a background noise image I about the environment and the systemnoise2；
S14: calculating to obtain an image I according to the following formulai：
Ii＝I0-(Inoise1+Inoise2)/2,(i＝1,2,3,…,n)。
Further, step S21 includes:
with image I1As a standard image, with image Ii(I-2, 3, … n, n being a positive integer) as the image to be registered, for said image I1And image I to be registeredi(I is 2, 3, … n, n is a positive integer) is registered based on image characteristics, and a registered image set I is obtainedRiWherein, IR1＝I1。
Further, step S2 includes:
s22: with image IR1For reference picture IrFor image set IRiAll other images I in (1)RiAre all consistent with reference image IrTaking difference and absolute value to obtain difference image IDi；
S23: establishment and said IDiTwo-dimensional matrix A1 of the same size for the IDiSumming the gray values of the corresponding pixels, and filling the sum of the obtained gray values to the position of the corresponding pixel of the two-dimensional matrix A1;
s24: and performing binarization processing on the two-dimensional matrix A1, and performing morphological closed operation to obtain a template A containing crosstalk information and spot information.
Further, step S3 includes:
s31: for image IRiAnd the template A is subjected to logical AND operation to obtain an image I with crosstalk information and light spot informationRi；
S32: gradually increasing the radius of the light spot by a preset length step, taking the mean value of the light spot gray scale of the edge area as a constraint condition, taking the mean value of the light spot gray scale of the edge area as a target value, and solving the radius R of the light spot when the mean value of the light spot gray scale is maximumiAnd a spot center Oi(ii) a The constraint conditions include: the gray average value of the edge area corresponding to the light spot area is within a preset range.
Further, the preset length step is 1.
Further, "calculate said IiThe mean value DN of the gray levels of all pixels in the image which are positioned in the light spot circular areai"comprises:
traverse the IiAll pixel points in the image are determined as the position of a certain pixel point and the center O of the light spotiIs less than the spot radius RiAnd judging that the current pixel point is positioned in the light spot circle region, and calculating the I after all pixel points are traversediThe gray level mean value DN of all pixel points in the light spot circle area in the imagei。
Further, the method comprises:
establishing the mean value DNiCorresponding relation with the radiation brightness of the light source.
A second aspect of the present application provides a computer storage medium having stored thereon a computer program which, when executed by a processor, performs the method steps according to the first aspect of the present application.
A third aspect of the present application provides a data processing system for sub-aperture scanning flat-field scaling, said system comprising a memory, a processor and a computer program stored on said memory and running on said processor, characterized in that said processor, when executing said computer program, implements the steps of the method according to the first aspect of the present application.
Different from the prior art, the invention provides a data processing method, a system and a storage medium for sub-aperture scanning flat field calibration, wherein the method comprises the following steps: s1: obtaining an image I of a sub-aperture light source passing through a telescopei(ii) a S2: the image IiIdentifying crosstalk information and light spot information to obtain a template A containing the crosstalk information and the light spot information; the method specifically comprises the following steps: s21: for the image IiCarrying out registration to obtain a registration image IRi(ii) a S3: registering the image I according to the template ARiPerforming spot segmentation to obtain a spot radius RiAnd light spotCenter Oi(ii) a S4: for all spot radii RiAccumulating and averaging to obtain consistent light spot radius R of the image formed by the telescope sub-aperture light source; s5: using R as the radius of the light spot, OiDetermining a spot circle area for the spot center and calculating the IiThe gray level mean value DN of all pixel points in the light spot circle area in the imagei(ii) a S6: for all DN calculated based on sub-apertureiAnd summing, and calculating the full aperture gray scale mean value DN under the current field according to the following formula: DN ═ k ∑ DNiWherein k is a correction coefficient; s7: and acquiring a full-aperture gray mean value DN in the current field, and establishing a mapping relation between the DN and the radiation brightness of the light source.
The method has the advantages that the data processing process is simple and efficient when the full-aperture light source imaging data is solved, and the gray value information of the original image can be effectively reserved; meanwhile, images obtained by the same sub-aperture light source at different scanning positions have consistent light spot areas, and interference caused by inconsistent light spot areas can be effectively eliminated; and the crosstalk and ghost images can be identified, and the influence of the crosstalk of the image detector on the calibration result is eliminated.
Drawings
FIG. 1 is a schematic view of a scanning mechanism for sub-aperture scanning flat field calibration and sub-aperture scanning flat field calibration according to the prior art;
FIG. 2 is a schematic flow diagram of a sub-aperture scanning flat field scaling principle according to the prior art;
FIG. 3 is a flow chart of a data processing method of sub-aperture scanning flat field scaling according to the present invention;
FIG. 4 is a flow chart of another data processing method of sub-aperture scanning flat field scaling according to the present invention;
FIG. 5 is a flow chart of another data processing method of sub-aperture scanning flat field scaling according to the present invention;
FIG. 6 is a flow chart of another data processing method of sub-aperture scanning flat field scaling according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In order to make the description of the present disclosure more complete and complete, the following description is given for illustrative purposes with respect to the embodiments and examples of the present invention; it is not intended to be the only form in which the embodiments of the invention may be practiced or utilized. The embodiments are intended to cover the features of the various embodiments as well as the method steps and sequences for constructing and operating the embodiments. However, other embodiments may be utilized to achieve the same or equivalent functions and step sequences.
Fig. 3 is a flowchart of a data processing method for sub-aperture scanning flat-field calibration according to the present invention. The method comprises the following steps:
s1: obtaining an image I of a sub-aperture light source passing through a telescopei；
S2: the image IiIdentifying crosstalk information and light spot information to obtain a template A containing the crosstalk information and the light spot information; the method specifically comprises the following steps: s21: for the image IiCarrying out registration to obtain a registration image IRi；
S3: registering the image I according to the template ARiPerforming spot segmentation to obtain a spot radius RiAnd spot center Oi；
S4: for all spot radii RiAccumulating and averaging to obtain consistent light spot radius R of the image formed by the telescope sub-aperture light source;
s5: using R as the radius of the light spot, OiDetermining a spot circle area for the spot center and calculating the IiThe gray level mean value DN of all pixel points in the light spot circle area in the imagei；
S6: for all DN calculated based on sub-apertureiSummed and calculated according to the following formulaCalculating the mean value DN of the full aperture gray scale under the current view field: DN ═ k ∑ DNiWherein k is a correction coefficient;
s7: and acquiring a full-aperture gray mean value DN in the current field, and establishing a mapping relation between the DN and the radiation brightness of the light source.
In step S7, the mapping relationship between the DN and the luminance of the light source radiation may be specifically expressed by the following formula: DNj＝Rj·LjWhere j is the number of sampled fields of view, RjThe coefficients are scaled for flat fields. And establishing a physical mapping relation between the obtained gray level mean value DN under a certain field of view and the actual light source radiation brightness of the calibration light source calibrated to the primary standard, thereby completing the flat field calibration process.
The invention relates to a data processing method of sub-aperture scanning flat field radiometric calibration, which is applied to flat field calibration data processing based on sub-aperture scanning. The sub-aperture light source is white (or a fixed spectrum component) collimated light, and an integrating sphere light source emits a collimated light beam through a reticle and a collimating optical system and irradiates an entrance pupil of a telescope optical system.
When the sub-aperture light source is designed, the area SA of the sub-aperture light source, the number n of scanning position points and the area SB of the full-aperture light source to be simulated need to satisfy the following relations:
SB＝n×SA
the sub-aperture light source realizes horizontal and vertical position scanning through a two-dimensional scanning mechanism, and realizes scanning view field adjustment through pitching and azimuth adjustment.
The invention provides a flat-field radiometric calibration data processing method for sub-aperture scanning, which is mainly a method for quickly and effectively extracting and eliminating light spots of a single sub-aperture calibration image. The process of scaling data based on sub-aperture scanning is shown in fig. 3. The method specifically comprises the following steps: denoising the sub-aperture light source, discriminating crosstalk information and ghost image information (namely light spot information), segmenting a light spot image, adjusting the consistency of the radius of the light spot, and solving the gray level mean value of the light spot by 5 steps. According to the scheme, crosstalk and ghost images are discriminated through a data processing means, and light spot image segmentation is achieved by combining crosstalk shapes and gray value characteristics. Meanwhile, the fact that all the sub-aperture light source calibration images have the same light spot size is guaranteed. The method has accurate and efficient calibration and wide applicability in the field of flat-field radiometric calibration based on sub-aperture scanning.
In certain embodiments, as shown in fig. 4, step S1 includes:
s11: controlling the opening of the sub-aperture light source, imaging the telescope, and acquiring a background noise image I about the environment and the systemnoise1；
S12: controlling the sub-aperture light source to be closed, imaging the telescope and acquiring the sub-aperture light source at LiImaging at a location I0；
S13: controlling the sub-aperture light source to be closed, imaging the telescope, and acquiring a background noise image I about the environment and the systemnoise2；
S14: calculating to obtain an image I according to the following formulai：
Ii＝I0-(Inoise1+Inoise2)/2,(i＝1,2,3,…,n)。
Preferably, the telescope is a large-caliber telescope.
In this way, the imaging image I is obtained by recording the background noise images of the environment and the system before and after the imaging of the telescope and removing the background noise from the imaging image after the imaging of the telescopeiThe interference of other factors such as environment, the system and the like can be effectively eliminated.
Imaging of spot I due to all path sub-aperturesiThe imaging position at the detector remains substantially unchanged (scan field of view unchanged). Thus, IiThe crosstalk in (2) is invariant with the scanning position, while ghost images vary with the scanning position (different optical paths of light). The above-mentioned imaging position difference is discriminated based on the crosstalk and the ghost image, and the specific process is shown in fig. 5.
As shown in fig. 5, in certain embodiments, step S21 includes:
with image I1As a standard image, with image Ii(I-2, 3, … n, n being a positive integer) as the image to be registered, for said image I1And image I to be registeredi(I is 2, 3, … n, n is a positive integer) is registered based on image characteristics, and a registered image set I is obtainedRiWherein, IR1＝I1。
The image registration process in step S21 is configured by using a registration algorithm based on image features, which include image edges, contours, statistical features (such as center of gravity), and the like.
Preferably, step S2 includes:
s22: with image IR1For reference picture IrFor image set IRiAll other images I in (1)RiAre all consistent with reference image IrTaking difference and absolute value to obtain difference image IDi；
S23: establishment and said IDiTwo-dimensional matrix A1 of the same size for the IDiSumming the gray values of the corresponding pixels, and filling the sum of the obtained gray values to the position of the corresponding pixel of the two-dimensional matrix A1;
s24: and performing binarization processing on the two-dimensional matrix A1, and performing morphological closed operation to obtain a template A containing crosstalk information and spot information.
The threshold value setting of the binarization processing process of the two-dimensional matrix A is that firstly, an initial threshold value is solved based on a maximum inter-class variance method, and then, targeted fine adjustment is carried out according to the characteristics of crosstalk and circular specks which are actually observed.
As shown in fig. 6, step S3 includes:
s31: for image IRiAnd the template A is subjected to logical AND operation to obtain an image I with crosstalk information and light spot informationRi；
S32: gradually increasing the radius of the light spot by a preset length step, taking the mean value of the light spot gray scale of the edge area as a constraint condition, taking the mean value of the light spot gray scale of the edge area as a target value, and solving the radius R of the light spot when the mean value of the light spot gray scale is maximumiAnd a spot center Oi(ii) a The constraint conditions include: the gray average value of the edge area corresponding to the light spot area is within a preset range.Preferably, the preset length step is 1.
The constraint condition of the gray value of the light spot in the edge area is 0.6-0.7 of the central brightness, and the set principle is determined according to the characteristics of the optical system and the subaperture light source device. Preferably, the edge area is an annular area between the radius R and the radius 0.95R.
In certain embodiments, "calculating the IiThe mean value DN of the gray levels of all pixels in the image which are positioned in the light spot circular areai"comprises:
traverse the IiAll pixel points in the image are determined as the position of a certain pixel point and the center O of the light spotiIs less than the spot radius RiAnd judging that the current pixel point is positioned in the light spot circle region, and calculating the I after all pixel points are traversediThe gray level mean value DN of all pixel points in the light spot circle area in the imagei。
Preferably, the method comprises: establishing the mean value DNiCorresponding relation with the radiation brightness of the light source.
DNiThat is, the pixel gray values of the sub-aperture light source at different scanning positions under the field of view, thereby establishing a corresponding relationship with the light source radiation brightness. The processing process of other fields is consistent with the method, and finally the relationship between the light source and the pixel gray value under the full aperture of the full field is obtained based on field interpolation or other methods.
A second aspect of the invention provides a computer storage medium having stored thereon a computer program which, when executed by a processor, performs the method steps according to the first aspect of the application.
The storage medium is a Memory, which may be a nonvolatile storage medium, and may exemplarily include, but not be limited to, a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), or a Flash Memory (Flash Memory), such as any one of the following: embedded multimedia cards (EMMC), Nor Flash, Nand Flash, and the like.
The memory may also illustratively include a buffer device for buffering data, such as a signal queue. The cache device may be a volatile storage medium, and may exemplarily include, but is not limited to, a Random Access Memory (RAM), a Static RAM (Static RAM, SRAM), a Dynamic RAM (Dynamic RAM, DRAM), a Synchronous DRAM (Synchronous DRAM, SDRAM), a Double Data Rate SDRAM (Double Data Rate SDRAM, DDR SDRAM), a DDR2, a DDR3, an Enhanced SDRAM (Enhanced SDRAM, ESDRAM), a Synchronous Link DRAM (SLDRAM), a Direct RAM (DRAM), and the like.
A third aspect of the present invention provides a data processing system for sub-aperture scanning flat-field scaling, said system comprising a memory, a processor and a computer program stored in said memory and running on said processor, wherein said processor implements the steps of the method according to the first aspect of the present application when executing said computer program.
Illustratively, the memory may include, for example, a memory card of a smart phone, a storage component of a tablet computer, a hard disk of a personal computer, a Read Only Memory (ROM), an Erasable Programmable Read Only Memory (EPROM), a portable compact disc read only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media.
Illustratively, the processor may be a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the system to perform desired functions. For example, a processor may include one or more embedded processors, processor cores, microprocessors, logic circuits, hardware Finite State Machines (FSMs), Digital Signal Processors (DSPs), or a combination thereof.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (9)

1. A data processing method for flat-field scaling of sub-aperture scans, said method comprising:
s1: obtaining an image I of a sub-aperture light source passing through a telescopei；
S2: for the image IiCarrying out registration to obtain a registration image IRi(ii) a For the image IiIdentifying crosstalk information and light spot information to obtain a template A containing the crosstalk information and the light spot information;
s3: registering the image I according to the template ARiPerforming spot segmentation to obtain a spot radius RiAnd spot center Oi；
S4: for all spot radii RiAccumulating and averaging to obtain consistent light spot radius R of the image formed by the telescope sub-aperture light source;
s5: using R as the radius of the light spot, OiDetermining a spot circle area for the spot center and calculating the image IiThe mean value DN of the gray levels of all the pixel points in the light spot circle areai；
S6: for all DN calculated based on sub-apertureiAnd summing, and calculating the full aperture gray scale mean value DN under the current field according to the following formula: DN ═ k ∑ DNiWherein k is a correction coefficient;
s7: and acquiring a full-aperture gray mean value DN in the current field, and establishing a mapping relation between the DN and the radiation brightness of the light source.
2. The data processing method of sub-aperture scanning flat-field scaling according to claim 1, wherein the step S1 includes:
s11: controlling the opening of the sub-aperture light source, imaging the telescope, and acquiring a background noise image I about the environment and the systemnoise1；
S12: controlling the sub-aperture light source to be closed, imaging the telescope and acquiring the sub-aperture light source at LiImaging at a location I0；
S13: controlling the sub-aperture light source to be closed, imaging the telescope, and acquiring a background noise image I about the environment and the systemnoise2；
S14: calculating to obtain an image I according to the following formulai：
Ii＝I0-(Inoise1+Inoise2)/2,(i＝1,2,3,…,n)。
3. The data processing method for sub-aperture scanning flat-field scaling according to claim 1, wherein "for said image IiCarrying out registration to obtain a registration image IRi"comprises:
s21: with image I1As a standard image, with image Ii(I-2, 3, … n, n being a positive integer) as the image to be registered, for said image I1And image I to be registeredi(I is 2, 3, … n, n is a positive integer) is registered based on image characteristics, and a registered image set I is obtainedRiWherein, IR1＝I1。
4. The data processing method for sub-aperture scanning flat-field scaling according to claim 1 or 3, characterized in that "for said image IiIdentifying crosstalk information and light spot information to obtain a template A' containing the crosstalk information and the light spot information comprises the following steps:
s22: with image IR1For reference picture IrFor image set IRiAll other images I in (1)RiAre all consistent with reference image IrTaking difference and absolute value to obtain difference image IDi；
S23: establishment and said IDiTwo-dimensional matrix A1 of the same size for the IDiSumming the gray values of the corresponding pixels, and filling the sum of the obtained gray values to the position of the corresponding pixel of the two-dimensional matrix A1;
s24: and performing binarization processing on the two-dimensional matrix A1, and performing morphological closed operation to obtain a template A containing crosstalk information and spot information.
5. The data processing method of sub-aperture scanning flat-field scaling according to claim 1, wherein the step S3 includes:
s31: for image IRiAnd the template A is subjected to logical AND operation to obtain an image I with crosstalk information and light spot informationRi；
S32: gradually increasing the radius of the light spot by a preset length step, taking the mean value of the light spot gray scale of the edge area as a constraint condition, taking the mean value of the light spot gray scale of the edge area as a target value, and solving the radius R of the light spot when the mean value of the light spot gray scale is maximumiAnd a spot center Oi(ii) a The constraint conditions include: the gray average value of the edge area corresponding to the light spot area is within a preset range.
6. The data processing method for sub-aperture scanning flat-field scaling according to claim 1, wherein said preset length step is 1.
7. The data processing method for sub-aperture scanning flat-field scaling according to claim 1, wherein "calculating said IiThe mean value DN of the gray levels of all pixels in the image which are positioned in the light spot circular areai"comprises:
traverse the IiAll pixel points in the image are determined as the position of a certain pixel point and the center O of the light spotiIs less than the spot radius RiAnd judging that the current pixel point is positioned in the light spot circle region, and calculating the I after all pixel points are traversediThe gray level mean value DN of all pixel points in the light spot circle area in the imagei。
8. A computer storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
9. A data processing system for flat-field scaling of sub-aperture scans, said system comprising a memory, a processor and a computer program stored on said memory and running on said processor, wherein said processor when executing said computer program implements the method of any of claims 1 to 7.
CN202011183115.0A 2020-10-29 2020-10-29 Data processing method, system and storage medium for subaperture scanning flat field calibration Active CN112288785B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011183115.0A CN112288785B (en) 2020-10-29 2020-10-29 Data processing method, system and storage medium for subaperture scanning flat field calibration

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011183115.0A CN112288785B (en) 2020-10-29 2020-10-29 Data processing method, system and storage medium for subaperture scanning flat field calibration

Publications (2)

Publication Number Publication Date
CN112288785A true CN112288785A (en) 2021-01-29
CN112288785B CN112288785B (en) 2022-07-15

Family

ID=74353212

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011183115.0A Active CN112288785B (en) 2020-10-29 2020-10-29 Data processing method, system and storage medium for subaperture scanning flat field calibration

Country Status (1)

Country Link
CN (1) CN112288785B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112884666A (en) * 2021-02-02 2021-06-01 杭州海康慧影科技有限公司 Image processing method, image processing device and computer storage medium

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102799887A (en) * 2012-06-19 2012-11-28 上海地铁盾构设备工程有限公司 Automatic calibration method of structural distortion detection image sensor sensitivity
CN103033338A (en) * 2012-12-12 2013-04-10 中国科学院长春光学精密机械与物理研究所 Flat field calibrating device and flat field calibrating method of vacuum ultraviolet band imaging system
CN106096474A (en) * 2015-04-21 2016-11-09 手持产品公司 System and method for imaging
US20190012564A1 (en) * 2012-01-17 2019-01-10 Leap Motion, Inc. Enhanced Contrast for Object Detection and Characterization By Optical Imaging Based on Differences Between Images
CN109785245A (en) * 2018-12-06 2019-05-21 江苏大学 A kind of light spot image dressing method
US20200257940A1 (en) * 2019-02-12 2020-08-13 Canon Kabushiki Kaisha Method, system and apparatus for generating training samples for matching objects in a sequence of images

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190012564A1 (en) * 2012-01-17 2019-01-10 Leap Motion, Inc. Enhanced Contrast for Object Detection and Characterization By Optical Imaging Based on Differences Between Images
CN102799887A (en) * 2012-06-19 2012-11-28 上海地铁盾构设备工程有限公司 Automatic calibration method of structural distortion detection image sensor sensitivity
CN103033338A (en) * 2012-12-12 2013-04-10 中国科学院长春光学精密机械与物理研究所 Flat field calibrating device and flat field calibrating method of vacuum ultraviolet band imaging system
CN106096474A (en) * 2015-04-21 2016-11-09 手持产品公司 System and method for imaging
CN109785245A (en) * 2018-12-06 2019-05-21 江苏大学 A kind of light spot image dressing method
US20200257940A1 (en) * 2019-02-12 2020-08-13 Canon Kabushiki Kaisha Method, system and apparatus for generating training samples for matching objects in a sequence of images

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
逯诗桐，张天一，张晓辉: "大口径空间巡天望远镜子孔径拼接平场定标法", 《中国光学》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112884666A (en) * 2021-02-02 2021-06-01 杭州海康慧影科技有限公司 Image processing method, image processing device and computer storage medium
CN112884666B (en) * 2021-02-02 2024-03-19 杭州海康慧影科技有限公司 Image processing method, device and computer storage medium

Also Published As

Publication number Publication date
CN112288785B (en) 2022-07-15

Similar Documents

Publication Publication Date Title
US20070127036A1 (en) Interference measurement system self-alignment method
CN108426640B (en) A kind of bearing calibration for infrared detector defect pixel
CN108961184B (en) Method, device and equipment for correcting depth image
JP5906188B2 (en) Brightness adjustment of digital images using range information
TWI480578B (en) Method for detecting optical center of wide-angle lens, and optical center detection apparatus
CN110553665A (en) automatic measuring device and method for optical axis deviation of laser range finder
Ten Kate et al. Method for counting mitoses by image processing in Feulgen stained breast cancer sections
CN113034612B (en) Calibration device, method and depth camera
CN111508011A (en) Depth data calibration method of flight time camera
CN112288785B (en) Data processing method, system and storage medium for subaperture scanning flat field calibration
CN115225820A (en) Automatic shooting parameter adjusting method and device, storage medium and industrial camera
EP3422073B1 (en) Phase-contrast microscope and imaging method
JP6141497B2 (en) Method and measuring device for specifying dimensional characteristics of measurement object
WO2022142243A1 (en) Apparatus for use in calibrating laser level
US6493073B2 (en) System and method for measuring properties of an optical component
JP2015204004A (en) Image formation device, calibration program and calibration system
CN115423808B (en) Quality detection method for speckle projector, electronic device, and storage medium
CN116563298A (en) Cross line center sub-pixel detection method based on Gaussian fitting
CN110927181A (en) Method for detecting foreign matters in part hole, terminal device and computer-readable storage medium
WO2023284320A1 (en) Three-dimensional displacement compensation method for photothermal reflectance microscopic thermal imaging, and control apparatus
CN112634298B (en) Image processing method and device, storage medium and terminal
CN109813531A (en) The debugging apparatus and its adjustment method of optical system
CN109754365B (en) Image processing method and device
CN115393371A (en) Image processing method, image processing apparatus, and optical detection device
Taylor et al. Model‐free quantification and visualization of colocalization in fluorescence images

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant