CN109540946A - Example of transmission electron microscope drift antidote based on intelligent algorithm - Google Patents
Example of transmission electron microscope drift antidote based on intelligent algorithm Download PDFInfo
- Publication number
- CN109540946A CN109540946A CN201811249520.0A CN201811249520A CN109540946A CN 109540946 A CN109540946 A CN 109540946A CN 201811249520 A CN201811249520 A CN 201811249520A CN 109540946 A CN109540946 A CN 109540946A
- Authority
- CN
- China
- Prior art keywords
- value
- picture
- upper left
- size
- template
- 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.)
- Pending
Links
- 230000005540 biological transmission Effects 0.000 title claims abstract description 28
- 239000000729 antidote Substances 0.000 title claims abstract description 9
- 238000012937 correction Methods 0.000 claims abstract description 17
- 238000011066 ex-situ storage Methods 0.000 claims abstract description 9
- 238000011065 in-situ storage Methods 0.000 claims abstract description 9
- 238000003384 imaging method Methods 0.000 claims abstract description 7
- 239000000284 extract Substances 0.000 claims description 18
- 238000003860 storage Methods 0.000 claims description 15
- 238000000034 method Methods 0.000 claims description 4
- 239000000523 sample Substances 0.000 claims 4
- 239000013068 control sample Substances 0.000 claims 1
- 230000000694 effects Effects 0.000 abstract description 2
- 238000002474 experimental method Methods 0.000 description 4
- 238000006073 displacement reaction Methods 0.000 description 2
- 238000004627 transmission electron microscopy Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005684 electric field Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000010894 electron beam technology Methods 0.000 description 1
- 238000000921 elemental analysis Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N23/00—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
- G01N23/22—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by measuring secondary emission from the material
- G01N23/2202—Preparing specimens therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/80—Geometric correction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10056—Microscopic image
- G06T2207/10061—Microscopic image from scanning electron microscope
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20092—Interactive image processing based on input by user
- G06T2207/20104—Interactive definition of region of interest [ROI]
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Image Analysis (AREA)
Abstract
The example of transmission electron microscope that the invention discloses a kind of based on intelligent algorithm drifts about antidote, and specially over the display, the picture size of imaging is m × n for operation transmission electron microscope imaging display；For user according to the region M × N for being less than or equal to picture size is selected in formed picture, selected areas is used as image template；According to the region of selection, extracting record gray value using algorithm is data template；Threshold value A is set.Select correction mode: correction in situ or ex situ correction, in situ that image shift only occurs for the variation of no contrast, ex situ is that there are contrast variations；Calculate its departure degree D；If the minimum value of departure degree D value is greater than set threshold value A, mechanical return is realized by control specimen holder；If the minimum value of departure degree D value is less than set threshold value A, is realized and resetted by algorithm.Gain effect of the invention is the drift and shaking that can effectively prevent the image when being continuously shot multiple transmission electron microscope photos.
Description
Technical field
The present invention relates to transmission electron microscope the field of test technology, it is related to calculating under a kind of nanoscale based on artificial intelligence
The example of transmission electron microscope drift antidote of method.
Background technique
Transmission electron microscope is a kind of important means that the structure of matter can be observed under atomic scale.By giving transmission electricity
Sub- microscope, which applies additional accessory, may be implemented the function of elemental analysis.The specimen holder of transmission electron microscope is to carry sample
Device, different specimen holders can be to applying different outfield, such as thermal field, electric field, the field of force etc..Due to transmission electron microscopy
It is the tool in atomic scale parsing substance, therefore the minimum shake of sample can all cause the difficulty observed, such as the drift of sample
It moves and will lead to the position for needing at every moment to adjust sample in observation, make it at observation center.This undoubtedly increases observation
The difficulty of difficulty and the real time monitoring to area-of-interest.The main reason for causing sample to drift about includes: that electron beam irradiation causes
Thermal drift, drift caused by accumulation drifts about caused by instrument, outside noise, improper operation of operator etc..Therefore such as
The drift what solves sample becomes one of the challenge for perplexing transmission electron microscope operator instantly.
Summary of the invention
Present invention aim to address preventing sample from drifting about operating transmission electron microscope shooting photo or when video,
It is proposed the example of transmission electron microscope drift antidote under a kind of nanoscale based on intelligent algorithm.
Application scenarios of the invention are to be continuously shot multiple transmission electron microscopies in user's operation transmission electron microscope
Mirror photo.Gain effect of the invention is the drift that can effectively prevent the image when being continuously shot multiple transmission electron microscope photos
It moves and shakes.
Realizing the specific technical solution of the object of the invention is:
A kind of example of transmission electron microscope drift antidote based on intelligent algorithm, this method includes following tool
Body step:
Step 1: operation transmission electron microscope adjusts focal length and enables transmission electron microscope clearly to sample
The picture of imaging, sample is most clearly shown on display by external charge coupled cell, and the picture size of imaging is m × n；
For user according to the region M × N for being less than or equal to picture size is selected in formed picture, selected areas is used as image mould
Plate；
Step 2: according to the region of selection, extracting gray value using algorithm, i.e., to selected areas from the upper left corner to the lower right corner
Gray value is recorded line by line and is stored into memory, and the gray value to record at this time is data template；
Step 3: setting threshold value A, i.e., patient image drift or shaking maximum value；
Step 4: selection correction mode: original position correction or ex situ correction, in situ for the variation of no contrast, only generation image is inclined
It moves, ex situ is that there are contrast variations；
Step 5: calculating its departure degree D；If the minimum value value of departure degree D is greater than set threshold value A, pass through control
Specimen holder processed realizes mechanical return；If the minimum value of departure degree D value is less than set threshold value A, realized by algorithm multiple
Position.
It is described that gray value is recorded line by line from the upper left corner to the lower right corner to selected areas, specifically: it is extracted in selected areas
With (i, j) for the upper left corner, size is the gray value of the subgraph of M × N, and wherein the definition of subgraph is the picture that size is M × N, is obtained
To template data T (s, t), wherein i, j are respectively cross, the ordinate that some in image is put, and s, t are count value.
Calculating its departure degree D, specifically:
Ex situ correction: the size of each picture is m × n；Gray value is extracted in the region that step 2 is chosen and is remembered
For data template；Switching current image enters the shooting and storage of next frame picture, extracts in the frame figure with (i, j) as upper left
Angle, size are the gray value of the figure of M × N and store S (x, y), and wherein x and y is the count value of subgraph；Calculate S (x, y) and template
The deviation value D (i, j) of T (s, t)；Then i and j successively add 1, extract and store the S (x, y) after i and j successively adds 1, and calculate i
The deviation value D (i, j) of S (x, y) and template T (s, t) after successively adding 1 with j；I and j successively add 1, extract and store i and j successively
The purpose of S (x, y) after adding 1 is so that with (i, j) for the upper left corner, and size is that the set of the subgraph gray value of M × N can include
The gray value data of whole picture；It is all get with (i, j) be the upper left corner, size be M × N subgraph gray value in, look for
To the minimum value of deviation value D (i, j)；Whether the value of the D (i, j) of judgement at this time is greater than set threshold value A, if it is greater than the threshold value
A then passes through driving specimen holder and realizes that the mechanical return of sample extracts at this time if the value of D (i, j) at this time is less than threshold value A
I, j value, and translate with (i, j) be the upper left corner, size be M × N subgraph image so that its upper left position (i, j) and mould
The position (i, j) in the plate picture upper left corner is overlapped；Wherein Wherein 1≤i≤m-M+1,1≤j≤n-N+1；Switching current image enters the shooting and storage of next frame picture, weight
Multiple above-mentioned steps, until shooting is completed；
Correction in situ: the size of each picture is m × n；Gray value is extracted in the region that step 2 is chosen and is denoted as
Data template；Switching current image enters the shooting and storage of next frame picture, extracts in the frame figure with (i, j) as upper left
Angle, size are the gray value of the figure of M × N and store S (x, y), and wherein x and y is the count value of subgraph；Calculate S (x, y) and template
The deviation value D (i, j) of T (s, t)；Then i and j successively add 1, extract and store the S (x, y) after i and j successively adds 1, and calculate i
The deviation value D (i, j) of S (x, y) and template T (s, t) after successively adding 1 with j；I and j successively add 1, extract and store i and j successively
The purpose of S (x, y) after adding 1 is so that with (i, j) for the upper left corner, and size is that the set of the subgraph gray value of M × N can include
The gray value data of whole picture；It is all get with (i, j) be the upper left corner, size be M × N subgraph gray value in, look for
To the minimum value of deviation value D (i, j)；Whether the value of the D (i, j) of judgement at this time is greater than threshold value A set by step 3, if it is greater than
The threshold value A then realizes that the mechanical return of sample mentions if the value of D (i, j) at this time is less than threshold value A by driving specimen holder
Taking i at this time, j value, and translating with (i, j) as the upper left corner, size is the subgraph image of M × N, so that its upper left position (i,
J) it is overlapped with the position (i, j) in the template picture upper left corner；Wherein Wherein 1≤i≤m-M+1,1≤j≤n-N+1；Revised picture is saved as at this time new template, switched
Current image enters the shooting and storage of next frame picture, repeats the above steps, until shooting is completed.
Beneficial effects of the present invention:
The present invention can preferably overcome user's difficulty caused by sample drift when operating transmission electron microscope.And
User can neatly select interested region to operate, and correct to whole-course automation the operation that drift facilitates user.
Detailed description of the invention
Fig. 1 is flow chart of the present invention.
Specific embodiment
Embodiment 1
The present embodiment changes for no contrast, and image shift (ex situ) state only occurs, specifically includes:
Step 1: operation transmission electron microscope adjusts focal length and enables transmission electron microscope clearly to sample
Imaging.The picture of sample is most clearly shown on display by external charge coupled cell at this time.User can be according to formed
As selecting a size for the region of 300 × 300 pixels, selected areas is used as template, that is, guaranteeing should in subsequent operation
Region does not occur drift and shakes.
Step 2: the extracted region characteristic point that algorithm is chosen according to user, i.e., to selected areas from the upper left corner to the lower right corner by
In row record gray value and storage to storage equipment, and the data to record at this time is templates.Specially extracted in the frame figure
With (i, j) be the upper left corner, size be 300 × 300 subgraph gray value and storage, obtain template data T (300,300).
Step 3: setting threshold value is 50, i.e., user patient image drift or shaking maximum position.
Step 4: choice experiment mode is ex situ experiment.
Step 5: calculating its departure degree.The total size of picture is 2000 × 2000 pixels, under switching current image enters
One frame picture, in the frame figure extract with (i, j) be the upper left corner, size be 300 × 300 subgraph gray value and store S (300,
300) similarity of itself and template T (300,300), is calculated；1 purpose is successively added to be by i and j so that with (i, j) for the upper left corner, size is
300 × 300 subgraph gray value can get the gray value data of whole picture, it is all get with (i, j) be the upper left corner,
In the subgraph gray value that size is 300 × 300, find with template difference it is the smallest with (i, j) for the upper left corner, size is M × N
Subgraph be as a result, displacement images are overlapped the position (i, j) in its upper left position (i, j) and the template picture upper left corner, and it is same
When calculate the size of deviant D value, wherein
Wherein 1≤i≤1701,1≤j≤1701.If find with template difference it is the smallest with (i, j) be the upper left corner, size 300
The D value of × 300 subgraph is greater than the threshold value 50 that operator was originally arranged, then carries out mechanical return by software-driven specimen holder.
The threshold value 50 being originally arranged if it is less than operator, then the frame picture completes drift correction, then takes the next frame of the frame, in repetition
Step is stated, until correction is completed in the position of all pictures.
Embodiment 2
The present embodiment is the home state for having contrast to change, and is specifically included:
Step 1: operation transmission electron microscope adjusts focal length and enables transmission electron microscope clearly to sample
Imaging.The picture of sample is most clearly shown on display by external charge coupled cell at this time.User can be according to formed
As selecting a size for the region of 300 × 300 pixels, selected areas is used as template, that is, guaranteeing should in subsequent operation
Region does not occur drift and shakes.
Step 2: the extracted region characteristic point that algorithm is chosen according to user, i.e., to selected areas from the upper left corner to the lower right corner by
In row record gray value and storage to storage equipment, and the data to record at this time is templates.Specially extracted in the frame figure
With (i, j) be the upper left corner, size be 300 × 300 subgraph gray value and storage, obtain template data T (300,300).
Step 3: setting threshold value is 50, i.e., user patient image drift or shaking maximum position.
Step 4: choice experiment mode is experiment in situ.
Step 5: calculating its departure degree.The total size of picture is 2000 × 2000 pixels, and switching current image enters next
Frame picture, in the frame figure extract with (i, j) be the upper left corner, size be 300 × 300 subgraph gray value and store S (300,
300) similarity of itself and template T (300,300), is calculated；1 purpose is successively added to be by i and j so that with (i, j) for the upper left corner, size is
300 × 300 subgraph gray value can get the gray value data of whole picture, it is all get with (i, j) be the upper left corner,
In the subgraph gray value that size is 300 × 300, find with template difference it is the smallest with (i, j) for the upper left corner, size is M × N
Subgraph be as a result, displacement images are overlapped the position (i, j) in its upper left position (i, j) and the template picture upper left corner, and it is same
When calculate the size of deviant D value, wherein
Wherein 1≤i≤1701,1≤j≤1701.If find with template difference it is the smallest with (i, j) be the upper left corner, size 300
The D value of × 300 subgraph is greater than the threshold value 50 that operator was originally arranged, then carries out mechanical return by software-driven specimen holder,
The threshold value 50 being originally arranged if it is less than operator, then the frame picture completes drift correction, and revised picture is saved at this time
For new template, switches shooting and storage that current image enters next frame picture, repeat the above steps, until shooting is completed,
The next frame for taking the frame again, repeats the above steps, until correction is completed in the position of all pictures.
Protection content of the invention is not limited to above embodiments.Without departing from the spirit and scope of the invention, originally
Field technical staff it is conceivable that variation and advantage be all included in the present invention, and with appended claims be protect
Protect range.
Claims (3)
- A kind of antidote 1. example of transmission electron microscope based on intelligent algorithm drifts about, which is characterized in that this method Comprising the following specific stepsStep 1: operation transmission electron microscope, adjust focal length enable transmission electron microscope clearly to sample at The picture of picture, sample is most clearly shown on display by external charge coupled cell, and the picture size of imaging is m × n；With According to the region M × N for being less than or equal to picture size is selected in formed picture, selected areas is used as image template at family；Step 2: according to the region of selection, extract gray value using algorithm, i.e., to selected areas from the upper left corner to the lower right corner line by line It records gray value and stores into memory, and the gray value to record at this time is data template；Step 3: setting threshold value A, i.e., patient image drift or shaking maximum value；Step 4: selection correction mode: correction in situ or ex situ correction, it is in situ that image shift only occurs for the variation of no contrast, it is non- In situ is that there are contrast variations；Step 5: calculating its departure degree D；If the minimum value of departure degree D value is greater than set threshold value A, pass through control sample Product bar realizes mechanical return；If the minimum value of departure degree D value is less than set threshold value A, is realized and resetted by algorithm.
- The antidote 2. example of transmission electron microscope according to claim 1 based on intelligent algorithm drifts about, It is characterized in that, it is described that gray value is recorded line by line from the upper left corner to the lower right corner to selected areas, specifically: it is extracted in selected areas With (i, j) for the upper left corner, size is the gray value of the subgraph of M × N, and wherein the definition of subgraph is the picture that size is M × N, is obtained To template data T (s, t), wherein i, j are respectively cross, the ordinate that some in image is put, and s, t are count value.
- The antidote 3. example of transmission electron microscope according to claim 1 based on intelligent algorithm drifts about, It is characterized in that, calculating its departure degree D, specifically:Ex situ correction: the size of each picture is m × n；Gray value is extracted in the region that step 2 is chosen and is denoted as number According to template；Switching current image enters the shooting and storage of next frame picture, extracts in the frame figure with (i, j) as the upper left corner, Size is the gray value of the figure of M × N and stores S (x, y), and wherein x and y is the count value of subgraph；Calculate S (x, y) and template T The deviation value D (i, j) of (s, t)；Then i and j successively add 1, extract and store the S (x, y) after i and j successively adds 1, and calculate i and J successively add 1 after S (x, y) and template T (s, t) deviation value D (i, j)；I and j successively add 1, extract and store i and j successively adds The purpose of S (x, y) after 1 is so that with (i, j) for the upper left corner, and size is that the set of the subgraph gray value of M × N can include whole The gray value data of picture；It is all get with (i, j) be the upper left corner, size be M × N subgraph gray value in, find The minimum value of deviation value D (i, j)；Whether the value of the D (i, j) of judgement at this time is greater than set threshold value A, if it is greater than the threshold value A Then realize that the mechanical return of sample extracts at this time if the value of D (i, j) at this time is less than threshold value A by driving specimen holder I, j value, and translate with (i, j) as the upper left corner, size is the subgraph image of M × N, so that its upper left position (i, j) and template The position (i, j) in the picture upper left corner is overlapped；Wherein Wherein 1≤i≤m-M+1,1≤j≤n-N+1；Switching current image enters the shooting and storage of next frame picture, repeats above-mentioned step Suddenly, until shooting is completed；Correction in situ: the size of each picture is m × n；Gray value is extracted in the region that step 2 is chosen and is denoted as data Template；Switching current image enters the shooting and storage of next frame picture, extracts in the frame figure with (i, j) for the upper left corner, greatly The gray value of the small figure for M × N simultaneously stores S (x, y), and wherein x and y is the count value of subgraph；Calculate S (x, y) and template T (s, T) deviation value D (i, j)；Then i and j successively add 1, extract and store the S (x, y) after i and j successively adds 1, and calculate i and j according to It is secondary plus 1 after S (x, y) and template T (s, t) deviation value D (i, j)；I and j successively add 1, extract and store after i and j successively adds 1 S (x, y) purpose be so that with (i, j) be the upper left corner, size be M × N subgraph gray value set can include whole The gray value data of picture；It is all get with (i, j) be the upper left corner, size be M × N subgraph gray value in, find partially Minimum value from value D (i, j)；Whether the value of the D (i, j) of judgement at this time is greater than threshold value A set by step 3, if it is greater than the threshold Value A then realizes that the mechanical return of sample extracts this if the value of D (i, j) at this time is less than threshold value A by driving specimen holder When i, j value, and translate with (i, j) be the upper left corner, size be M × N subgraph image so that its upper left position (i, j) with The position (i, j) in the template picture upper left corner is overlapped；Wherein Wherein 1≤i≤m-M+1,1≤j≤n-N+1；Revised picture is saved as at this time new template, switched Current image enters the shooting and storage of next frame picture, repeats the above steps, until shooting is completed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811249520.0A CN109540946A (en) | 2018-10-25 | 2018-10-25 | Example of transmission electron microscope drift antidote based on intelligent algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811249520.0A CN109540946A (en) | 2018-10-25 | 2018-10-25 | Example of transmission electron microscope drift antidote based on intelligent algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109540946A true CN109540946A (en) | 2019-03-29 |
Family
ID=65844786
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811249520.0A Pending CN109540946A (en) | 2018-10-25 | 2018-10-25 | Example of transmission electron microscope drift antidote based on intelligent algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109540946A (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101064836A (en) * | 2007-05-29 | 2007-10-31 | 王海燕 | Method for tracking target in video image |
| CN105046251A (en) * | 2015-08-04 | 2015-11-11 | 中国资源卫星应用中心 | Automatic ortho-rectification method based on remote-sensing image of environmental No.1 satellite |
| CN106384350A (en) * | 2016-09-28 | 2017-02-08 | 中国科学院自动化研究所 | Neuron activity image dynamic registration method based on CUDA acceleration and neuron activity image dynamic registration device thereof |
| CN108088653A (en) * | 2017-11-30 | 2018-05-29 | 哈尔滨工业大学 | Confocal microscope pattern aberration correction method |
-
2018
- 2018-10-25 CN CN201811249520.0A patent/CN109540946A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101064836A (en) * | 2007-05-29 | 2007-10-31 | 王海燕 | Method for tracking target in video image |
| CN105046251A (en) * | 2015-08-04 | 2015-11-11 | 中国资源卫星应用中心 | Automatic ortho-rectification method based on remote-sensing image of environmental No.1 satellite |
| CN106384350A (en) * | 2016-09-28 | 2017-02-08 | 中国科学院自动化研究所 | Neuron activity image dynamic registration method based on CUDA acceleration and neuron activity image dynamic registration device thereof |
| CN108088653A (en) * | 2017-11-30 | 2018-05-29 | 哈尔滨工业大学 | Confocal microscope pattern aberration correction method |
Non-Patent Citations (1)
| Title |
|---|
| 杨新安等: "透射电子显微镜原位磁场双倾样品杆的研制", 《电子显微学报》 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Georgiev et al. | Lytro camera technology: theory, algorithms, performance analysis | |
| TWI777162B (en) | Image processing method and apparatus, electronic device and computer-readable storage medium | |
| Jacobs et al. | Automatic high-dynamic range image generation for dynamic scenes | |
| US8194995B2 (en) | Fast camera auto-focus | |
| CN101542353B (en) | Image processing device, image processing method, and observation system | |
| US10721414B2 (en) | All-in-focus implementation | |
| US9053546B2 (en) | Information processing apparatus, control method therefor, and computer-readable storage medium | |
| JP2013165475A (en) | Imaging apparatus for generating auxiliary information on photographed image, image processor and image processing method | |
| JP2007194819A5 (en) | ||
| DE112013005334T5 (en) | Super resolution based on optical image stabilization | |
| US20210400359A1 (en) | Method and system of presenting moving images or videos corresponding to still images | |
| US20060038144A1 (en) | Method and apparatus for providing optimal images of a microscope specimen | |
| JP2006295626A (en) | Fish-eye image processing apparatus, method thereof and fish-eye imaging apparatus | |
| CN102572492B (en) | Image processing device and method | |
| CN110163804A (en) | Image defogging method, device, computer equipment and storage medium | |
| US20140016827A1 (en) | Image processing device, image processing method, and computer program product | |
| CN104919490B (en) | For the structured descriptor of image procossing | |
| JP2023540273A (en) | Image processing method, device, and storage medium using eye condition detection | |
| CN106998424A (en) | Camera device and image capture method | |
| TWI271998B (en) | Image processing method and apparatus thereof | |
| JP6230386B2 (en) | Image processing apparatus, image processing method, and image processing program | |
| CN109540946A (en) | Example of transmission electron microscope drift antidote based on intelligent algorithm | |
| CN112241940B (en) | Fusion method and device for multiple multi-focus images | |
| CN112136311B (en) | Image processing method, image processing device, imaging system and storage medium | |
| Zhu et al. | Breaking the spatio-angular trade-off for light field super-resolution via lstm modelling on epipolar plane 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 | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20190329 |