CN109242790A - A kind of image deblurring appraisal procedure towards fields of measurement - Google Patents
A kind of image deblurring appraisal procedure towards fields of measurement Download PDFInfo
- Publication number
- CN109242790A CN109242790A CN201810957343.5A CN201810957343A CN109242790A CN 109242790 A CN109242790 A CN 109242790A CN 201810957343 A CN201810957343 A CN 201810957343A CN 109242790 A CN109242790 A CN 109242790A
- Authority
- CN
- China
- Prior art keywords
- image
- measurement
- deblurring
- image deblurring
- distance
- 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.)
- Withdrawn
Links
- 238000005259 measurement Methods 0.000 title claims abstract description 90
- 238000000034 method Methods 0.000 title claims abstract description 66
- 230000000694 effects Effects 0.000 claims abstract description 34
- 238000011156 evaluation Methods 0.000 claims abstract description 30
- 238000010606 normalization Methods 0.000 claims abstract description 6
- 230000008569 process Effects 0.000 claims description 9
- 239000003550 marker Substances 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 4
- 238000013461 design Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 230000006872 improvement Effects 0.000 claims description 3
- 239000000725 suspension Substances 0.000 claims description 3
- 230000000737 periodic effect Effects 0.000 claims description 2
- 230000009897 systematic effect Effects 0.000 claims 1
- 230000000052 comparative effect Effects 0.000 description 6
- 238000002474 experimental method Methods 0.000 description 5
- 238000013441 quality evaluation Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 244000207740 Lemna minor Species 0.000 description 1
- 235000006439 Lemna minor Nutrition 0.000 description 1
- 235000001855 Portulaca oleracea Nutrition 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 238000001259 photo etching Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/73—Deblurring; Sharpening
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/80—Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
-
- 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
- G06T2207/10004—Still image; Photographic image
-
- 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
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30168—Image quality inspection
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Quality & Reliability (AREA)
- Image Analysis (AREA)
Abstract
A kind of image deblurring appraisal procedure towards fields of measurement of the present invention belongs to computer vision measurement technical field, is related to a kind of image deblurring appraisal procedure towards fields of measurement.This method builds two CCD camera measure system first, and driving lathe drives the mark point high-speed motion for characterizing location information, while acquisition sequence image.Secondly fuzzy core is solved using prior information known to time for exposure, frame frequency, image deblurring is realized based on non-blind restoration algorithm.Then image deblurring valuation functions are established, in conjunction with precision index, picture structure numerical analysis and method for objectively evaluating by fluctuating error amplitude, distance of center circle constraint condition, and three kinds of evaluation measures normalization of image structure similarity, qualitative evaluation, the final image deblurring assessment realized towards fields of measurement are carried out to image restoring effect.The method achieve effective assessment to the image deblurring towards fields of measurement, method feasibility and robustness are good.
Description
Technical field
The invention belongs to computer vision measurement technical fields, are related to a kind of image deblurring assessment towards fields of measurement
Method.
Background technique
With the continuous reduction of hardware device, the fast development of software performance and measurement cost, vision measurement technology is because of tool
There are non-contact, dynamic measurement, informative, it is considered to be that realizes live real-time measurement effectively has one of section.Depending on
The principle for feeling measurement is to acquire two dimensional image by camera, and the three-dimensional spatial information of measured target is restored by image procossing,
But in high dynamic fields of measurement, the image that the high-speed motion of measured target will lead to shooting generates motion blur, extreme influence
Positioning measurement precision, scholar furthers investigate for image deblurring problem in recent years, but conventional images restore quality evaluation
Means mainly include mean square deviation, signal-to-noise ratio, Y-PSNR, gray scale AVERAGE GRADIENT METHOD WITH etc., however because actual measurement environment is different,
The factors such as grain background difference, noise all have larger impact to image restoration appraisal procedure, and above-mentioned evaluation measures are commonly view
Feel the improvement in effect or calculate the method for objectively evaluating such as signal noise ratio (snr) of image, fields of measurement can not be applied to, because of the invention one kind
Image deblurring appraisal procedure towards fields of measurement is of great significance.
Guangxi Normal University's beam knows the " a kind of to be based on image of the Patent No. CN 106296619A of inventions such as duckweed, Luo Xiaoshu
The High-motion picture deblurring method of column gray probability consistency ", this method combination genetic algorithm establish image deblurring mould
Type carries out image deblurring and output signal-to-noise ratio using Wiener filtering.But this method assesses image deblurring using signal-to-noise ratio
As a result, being only applicable to static or quasi-static image deblurring and effect assessment.The inventions such as the yellowish green pretty young woman of University Of Nanchang, Wu Lushen it is special
Benefit number is a kind of " the High-motion picture deblurring method based on image column gray probability consistency " of CN107945127A, should
Method carries out image deblurring method based on column gray consistency and uses by being modeled according to camera propulsion blurring process
Gray scale mean square error is as evaluation index.Image deblurring is assessed under the conditions of this method is suitable for camera motion, may not apply to
Field of high-precision measurement.
Summary of the invention
The invention solves technical problem be to overcome the deficiencies of existing technologies problem, invent a kind of towards fields of measurement
Image deblurring appraisal procedure.This method uses two CCD camera measure system, and control lathe drives mark point high-speed motion to adopt
Collect sequence image, complete the solution of fuzzy core using known prior information and image high-precision is realized also based on non-blind restoration algorithm
Then original proposes image deblurring valuation functions for fields of measurement, in conjunction with the crucial from fluctuating error amplitude, circle of fields of measurement
The heart assesses image restoring effect away from three aspects of constraint condition and image structure similarity, and precision index, image is special
Property analysis and method for objectively evaluating normalization, the final image deblurring assessment realized towards fields of measurement.This method combines visitor
Evaluation method and images themselves characteristic are seen, can be to the moving image deblurring effect qualitative evaluation under fields of measurement, and feasibility
It is good.
The technical scheme adopted by the invention is that a kind of image deblurring appraisal procedure towards fields of measurement, feature
It is that this method drives the mark point high-speed motion for characterizing location information using two CCD camera measure system driving lathe, together
When acquisition sequence image, secondly solve fuzzy core using time for exposure, prior information known to frame frequency, it is real based on non-blind restoration algorithm
Existing image deblurring, then establishes image deblurring valuation functions, in conjunction with precision index, picture structure numerical analysis and objective comments
Valence method is by fluctuating error amplitude, and three kinds of evaluation measures of distance of center circle constraint condition and image structure similarity normalize, to image
Reduction effect carries out qualitative evaluation, the final image deblurring assessment realized towards fields of measurement；Specific step is as follows for method:
Step 1: building vision measurement system acquisition image
Two CCD camera measure system is built first and carries out camera calibration, and upper surface is contained to the measurement mesh of circular markers
Mark is fixedly mounted on numerically-controlled machine tool, and two industrial cameras are fixed on above measurement target by profile suspension, in measurement process
The track high-speed motion for driving numerically-controlled machine tool to drive measurement target by setting, while camera shooting measurement target is triggered, to obtain
Mark point sequence image containing motion blur is simultaneously stored into computer.
Step 2: solving fuzzy core and carry out image deblurring
It is very short due to being influenced the time for exposure by environment and camera frame frequency during actual photographed, and become under certain condition
Speed, non-rectilinear and periodic motion are all decomposed into multiple linear uniform motion, therefore the motion blur in the time for exposure can be considered line
Property it is fuzzy, fuzzy core may be characterized as a line segment, be determined by two parameters of blur direction and Blur scale, it is of the invention in basis
Relationship between adjacent two field pictures estimates accurate fuzzy core as known prior information:
Wherein, f is camera frame frequency, and t is time for exposure, (x1, y1) and (x2, y2) it is to mark null circle in the two field pictures of front and back
The pixel coordinate of the heart sets previous frame image Blur scale as θ1, thus ergodic sequence image, which is asked, can acquire blur direction θ2And mould
Scale L is pasted, every accurate fuzzy core of frame image can be solved, then deconvoluted using non-blind restoration algorithm, to realize each frame
Image deblurring；
Step 3: establishing fluctuating error amplitude function
Since motion blur will affect the extraction accuracy of locating mark points information in image acquisition process, surveyed to reduce vision
The measurement error of amount system, the appraisal procedure of image deblurring effect need to consider the influence of precision index, therefore to for measurement
Error proposes function Q1:
Wherein, ei, i=(1,2 ..., n) indicates the Slight measurement errors of each frame image tagged point, i.e. the reality of mark point
The deviation of border measurement position and theoretical position, E indicate the Slight measurement errors acquired in the n frame image shot by blurred picture
blur(ei), i=(1,2 ..., n) and the Slight measurement errors deblur acquired by the clear image that image deblurring is restored
(ei), (1,2 ..., set n), two o'clock obtain fluctuating error amplitude function Q in conjunction with expression to i=1, guarantee Q1∈ (0,1), Q1
And bigger, eiIt is smaller, indicate that Slight measurement errors are smaller, image is fuzzy lower, evaluates image deblurring processing for being with this
The improvement of unified test accuracy of measurement index.
Step 4: establishing distance of center circle constraint condition function
Picture characteristics is considered in image processing process, according to the relationship under theoretical image between mark point, introduces single width figure
As the constraint about circle center distance, function Q is proposed to for distance of center circle constraint condition2:
Wherein, d indicates the distance of center circle on every frame image in circular markers between every two adjacent marker point, the survey of design
7 × 7 circular markers, d are shared in amount targetmeanIt is average value of the d in every frame image.Component Q2It is worth bigger, expression dmean
Closer to distance of center circle gauged distance, more accurate for the solution of distance, image blur effects are lower, evaluate image with this and obscure journey
Degree and image deblurring treatment effect.
Step 5: establishing image structure similarity function
In the method for objectively evaluating of image deblurring, structural similarity is from the brightness of reference picture and clear image, right
It is set out than the similitude between degree and structure to evaluate the fog-level of image, can be applied to evaluation image deblurring effect:
Wherein, μxAnd μyRespectively represent the mean value of two images, i.e. gray component, σxAnd σyPair the variance of image is indicated, i.e.,
Than degree component, σxyIndicate the covariance between two images, C1And C2Indicate constant.By multiple for fuzzy original image and deblurring
Original image compared to the structural similarity of clear image, obtains Q respectively3The comparison of component, i.e. Q3It is worth bigger, expression two images
More similar to clear image structure, image deblurring effect is better.
Step 6: establishing image deblurring valuation functions
In conjunction with the image deblurring valuation functions of step 3, step 4 and step 5, by fluctuating error amplitude, distance of center circle constraint
Three kinds of evaluation measures normalizeds of condition and image structure similarity can obtain and assess letter for fields of measurement image deblurring
Number Q:
Wherein, wiFor the respective weights of each function, which has comprehensively considered vision measurement system measurement accuracy
The picture characteristics of index, single image itself is analyzed and to the method for objectively evaluating of picture quality, to image deblurring effect into
Row qualitative evaluation, the final image deblurring assessment realized towards fields of measurement, that is, each frame image being directed to, respectively to fuzzy
Original image and de-blurred image seek valuation functions Q value, and the value of valuation functions Q is relatively bigger, indicate that image deblurring effect is better.
The invention has the beneficial effects that for image deblurring appraisal procedure is established towards fields of measurement, in conjunction with vision
The picture quality of measuring system measurement accuracy index, the structure numerical information for shooting image itself and traditional structural similarity is commented
Valence method, by fluctuating error amplitude, the three kinds of evaluation measures normalization of distance of center circle constraint condition and image structure similarity can
Qualitative evaluation is carried out to image deblurring effect.This method combination method for objectively evaluating and images themselves characteristic, realize towards
The image deblurring of fields of measurement is assessed, so that solving fields of measurement does not have lacking for suitable image restoration quality evaluating method
It falls into, method feasibility and robustness are good.
Detailed description of the invention
Fig. 1 is the flow diagram of the image deblurring appraisal procedure of the invention towards fields of measurement.
Fig. 2 is the measurement target image that circular markers are contained in upper surface.Wherein, d indicates circular mark on every frame image
Distance of center circle in point between every two adjacent marker point.
Fig. 3 is the valuation functions Q comparative result figure of isogonism spiral trajectory in experiment.Wherein, figure a) feeds for 3m/min
Valuation functions Comparative result under speed, figure are b) the valuation functions Comparative result under 5m/min feed speed.
Fig. 4 is the evaluation function Q comparative result figure of circular trace in experiment.Wherein, figure is a) under 3m/min feed speed
Valuation functions Comparative result, figure is b) the valuation functions Comparative result under 5m/min feed speed.
Specific embodiment
Describe the specific embodiment of the method for the present invention in detail below in conjunction with technical solution and attached drawing.
The present invention uses two CCD camera measure system, and driving lathe drives the mark point high speed for characterizing location information to transport
Dynamic and acquisition sequence image.Fuzzy core is solved using prior information known to time for exposure, frame frequency, is realized using non-blind restoration algorithm
Image deblurring.Then in conjunction with precision index, picture characteristics analysis and method for objectively evaluating by fluctuating error amplitude, distance of center circle is about
Three kinds of evaluation measures normalization of beam condition and image structure similarity, to establish image deblurring valuation functions.To image restoring
Effect carries out qualitative evaluation, the final image deblurring assessment realized towards fields of measurement.Fig. 1 is the present invention towards fields of measurement
Image deblurring appraisal procedure flow diagram, specific step is as follows for this method:
Step 1: building vision measurement system acquisition image
Two CCD camera measure system is built first, and embodiment builds experiment measurement system using two high resolution industrial cameras
System, and carry out camera calibration.There is the measurement target of 7*7 circular markers to be fixedly mounted on numerically-controlled machine tool upper surface photoetching
On, two industrial cameras are fixed on above measurement target by profile suspension, and the binocular camera time for exposure is set in measurement process
And frame frequency is 20ms and 25fps, driving numerically-controlled machine tool drives measurement target to press respectively with the feed speed of 3m/min and 5m/min
The track of setting is run, while triggering camera shooting measurement target acquisition sequence image, obtains the mark point containing motion blur
Sequence image is simultaneously stored into computer；
Step 2: solving fuzzy core and carry out image deblurring
Since the motion blur in the time for exposure during actual photographed can be considered Linear Fuzzy, that is, solve each frame image
Blur direction and Blur scale, according to the relationship between adjacent two field pictures as Given information, the combining camera time for exposure,
Prior information known to frame frequency can solve accurate fuzzy core using formula (2), and being deconvoluted based on non-blind restoration algorithm, it is clear to restore
Image traverses each frame image to realize each frame image deblurring；
Step 3: establishing fluctuating error amplitude function
In view of in image acquisition process since motion blur will affect the extraction accuracy of locating mark points information, for reduce
The measurement error of vision measurement system, the appraisal procedure of image deblurring effect need to consider the influence of precision index, therefore to needle
Valuation functions Q is proposed to measurement error1, as shown in formula (2), work as Q1And it is bigger, Slight measurement errors are smaller, image deblurring
Effect is better；
Step 4: establishing distance of center circle constraint condition function
In conjunction with the structure numerical information of shooting image itself, i.e. relationship under theoretical image between mark point, single width is introduced
Constraint of the image about circle center distance, as shown in Figure 2: 7 × 7 circular markers are shared in the measurement target of design, d indicates every
Distance of center circle on frame image in circular markers between every two adjacent marker point, therefore proposed to for distance of center circle constraint condition
Valuation functions Q2, as shown in formula (3), work as Q2It is worth bigger, expression dmeanCloser to distance of center circle gauged distance, i.e., for distance
It is more accurate to solve, and image deblurring treatment effect is better；
Step 5: establishing image structure similarity function
According to the image quality evaluating method of traditional structural similarity, structural similarity is as evaluation index, from reference
Similitude between the brightness of image and clear image, contrast and structure sets out to evaluate the fog-level of image, can answer
Valuation functions Q is proposed for evaluating image deblurring effect, therefore to for image structure similarity3, as shown in formula (4),
By, respectively compared to the structural similarity of clear image, obtaining Q for fuzzy original image and deblurring restored image3The knot of numerical value
Fruit is compared, and Q is worked as3Value is bigger, indicates that image is more similar to clear image structure, image deblurring effect is better；
Step 6: establishing image deblurring valuation functions
In conjunction with three image deblurring valuation functions of step 3, step 4 and step 5, by fluctuating error amplitude, distance of center circle
Three kinds of evaluation measures normalizeds of constraint condition and image structure similarity can be obtained and be commented for fields of measurement image deblurring
Estimate function Q, as shown in formula (5), using qualitative evaluation can be carried out to image deblurring effect, realizes the figure towards fields of measurement
As deblurring is assessed；
The weight chosen in experiment is respectively w1=0.5, w2=0.3 and w3=0.2, respectively using valuation functions Q to
The trace image deblurring effect of isogonism spiral trajectory is assessed under 3m/min, 5m/min feed speed, i.e., for each
Frame image seeks valuation functions Q value to fuzzy original image and de-blurred image respectively, as shown in Figure 3: horizontal axis indicates the sequence of shooting
Image, the longitudinal axis indicate the calculated value of valuation functions Q, and wherein it is clear to represent original image, de-blurred image and theory by evaluation function value Q
The degree of closeness of clear image.Fig. 3 a), b) in the Q value of de-blurred image be all larger than the Q value of original image, the two takes difference energy
Qualitative evaluation deblurring effect illustrates the image deblurring appraisal procedure proposed by the present invention towards fields of measurement, can be right
Image deblurring effect carries out qualitative evaluation, and method robustness is good.
The track of above-mentioned experimental image movement is the continually changing equiangular helical spiral of curvature, removes mould to verify image of the present invention
Valuation functions Q is pasted to the Evaluated effect for acquiring image under the conditions of constant curvature, controls numerically-controlled machine tool respectively with 3m/min and 5m/
The feed speed operating radius of min is the circular trace of 50mm, selects experiment parameter identical with equiangular spiral line tracking, respectively
Valuation functions Q value is sought to fuzzy original image and de-blurred image, as shown in Figure 4.By Fig. 4 a), b) it can be seen from de-blurred image
Q value be all larger than the Q value of fuzzy original image, i.e. this method can effectively assess image deblurring effect.
This method do not have a suitable image restoration quality evaluation means for fields of measurement, proposition towards fields of measurement
Image deblurring evaluation function, according to the measuring precision index, picture structure numerical information and traditional images quality evaluation side
Method, by fluctuating error amplitude, opposite is realized in the three kinds of evaluation measures normalization of distance of center circle constraint condition and image structure similarity
To effective assessment of the image deblurring of fields of measurement, method feasibility and robustness are good.
Claims (1)
1. a kind of image deblurring appraisal procedure towards fields of measurement, characterized in that this method uses Binocular vision photogrammetry system
System driving lathe drives the mark point high-speed motion for characterizing location information, acquisition sequence image；Utilize time for exposure, frame frequency
Known prior information solves fuzzy core, realizes image deblurring based on non-blind restoration algorithm；Then image deblurring assessment is established
Function, in conjunction with precision index, picture structure numerical analysis and method for objectively evaluating by fluctuating error amplitude, distance of center circle constrains item
The three kinds of evaluation measures normalization of part and image structure similarity carries out qualitative evaluation to image restoring effect, it is final realize towards
The image deblurring of fields of measurement is assessed；Specific step is as follows for method:
Step 1: building vision measurement system acquisition image
Two CCD camera measure system is built first and carries out camera calibration, and the measurement target that circular markers are contained in upper surface is consolidated
On numerically-controlled machine tool, two industrial cameras are fixed on above measurement target by profile suspension for Dingan County, are driven in measurement process
Numerically-controlled machine tool drives measurement target by the track high-speed motion of setting, while triggering camera shooting measurement target, to be contained
The mark point sequence image of motion blur is simultaneously stored into computer；
Step 2: solving fuzzy core and carry out image deblurring
It is very short due to being influenced the time for exposure by environment and camera frame frequency during actual photographed, and under certain condition speed change,
Non-rectilinear and periodic motion are all decomposed into multiple linear uniform motion, therefore the motion blur in the time for exposure can be considered linear mould
Paste, fuzzy core may be characterized as a line segment, determined by two parameters of blur direction and Blur scale, according to adjacent in the present invention
Relationship between two field pictures estimates accurate fuzzy core as known prior information:
Wherein, f is camera frame frequency, and t is time for exposure, (x1, y1) and (x2, y2) it is the picture that the null circle heart is marked in the two field pictures of front and back
Plain coordinate sets previous frame image Blur scale as θ1, thus ergodic sequence image, which is asked, can acquire blur direction θ2And Blur scale
L can solve every accurate fuzzy core of frame image, then be deconvoluted using non-blind restoration algorithm, to realize that each frame image is gone
It is fuzzy；
Step 3: establishing fluctuating error amplitude function
Since motion blur will affect the extraction accuracy of locating mark points information in image acquisition process, to reduce vision measurement system
The measurement error of system, the appraisal procedure of image deblurring effect need to consider the influence of precision index, therefore to for measurement error
Propose function Q1:
Wherein, ei, i=(1,2 ..., n) indicates the Slight measurement errors of each frame image tagged point, i.e. the practical survey of mark point
The deviation of position and theoretical position is measured, E indicates the Slight measurement errors blur acquired in the n frame image shot by blurred picture
(ei), i=(1,2 ..., n) and the Slight measurement errors deblur (e acquired by the clear image that image deblurring is restoredi),i
=(1,2 ..., set n), two o'clock obtain fluctuating error amplitude function Q in conjunction with expression1, guarantee Q1∈ (0,1), Q1And it is bigger,
eiIt is smaller, indicate that Slight measurement errors are smaller, image is fuzzy lower, evaluates image deblurring processing for systematic survey essence with this
Spend the improvement of index；
Step 4: establishing distance of center circle constraint condition function
Picture characteristics is considered in image processing process, according to the relationship under theoretical image between mark point, is introduced single image and is closed
Function Q is proposed in the constraint of circle center distance, therefore to for distance of center circle constraint condition2:
Wherein, d indicates the distance of center circle on every frame image in circular markers between every two adjacent marker point, the measurement mesh of design
It puts on and shares 7 × 7 circular markers, dmeanIt is average value of the d in every frame image；Component Q2It is worth bigger, expression dmeanMore connect
Nearly distance of center circle gauged distance, more accurate for the solution of distance, image blur effects are lower, with this evaluate image fog-level and
Image deblurring treatment effect；
Step 5: establishing image structure similarity function
In the method for objectively evaluating of image deblurring, structural similarity is from the brightness of reference picture and clear image, contrast
And the similitude between structure is set out to evaluate the fog-level of image, can be applied to evaluation image deblurring effect:
Wherein, μxAnd μyRespectively represent the mean value of two images, i.e. gray component, σxAnd σyIndicate the variance of image, i.e. contrast
Component, σxyIndicate the covariance between two images, C1And C2Indicate constant；By for fuzzy original image and deblurring restored map
As obtaining Q respectively compared to the structural similarity of clear image3The comparison of component, i.e. Q3It is worth bigger, indicates two images and clear
Clear picture structure is more similar, and image deblurring effect is better；
Step 6: establishing image deblurring valuation functions
In conjunction with the image deblurring valuation functions of step 3, step 4 and step 5, by fluctuating error amplitude, distance of center circle constraint condition,
And three kinds of evaluation measures normalizeds of image structure similarity, it can obtain and be directed to fields of measurement image deblurring valuation functions Q:
Wherein, wiFor the respective weights of each function, the evaluation function comprehensively considered vision measurement system measurement accuracy index,
The picture characteristics analysis of single image itself and the method for objectively evaluating to picture quality carry out image deblurring effect qualitative
Evaluation, the final image deblurring assessment realized towards fields of measurement, that is, each frame image being directed to, respectively to fuzzy original image and
De-blurred image seeks valuation functions Q value, and the value of valuation functions Q is relatively bigger, indicates that image deblurring effect is better.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810957343.5A CN109242790A (en) | 2018-08-22 | 2018-08-22 | A kind of image deblurring appraisal procedure towards fields of measurement |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810957343.5A CN109242790A (en) | 2018-08-22 | 2018-08-22 | A kind of image deblurring appraisal procedure towards fields of measurement |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109242790A true CN109242790A (en) | 2019-01-18 |
Family
ID=65071081
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810957343.5A Withdrawn CN109242790A (en) | 2018-08-22 | 2018-08-22 | A kind of image deblurring appraisal procedure towards fields of measurement |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109242790A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111626946A (en) * | 2020-04-23 | 2020-09-04 | 武汉理工大学 | Motion blur kernel measurement method for high-speed material transmission visual detection system |
| CN113327206A (en) * | 2021-06-03 | 2021-08-31 | 江苏电百达智能科技有限公司 | Image fuzzy processing method of intelligent power transmission line inspection system based on artificial intelligence |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104408707A (en) * | 2014-10-28 | 2015-03-11 | 哈尔滨工业大学 | Rapid digital imaging fuzzy identification and restored image quality assessment method |
| CN106296619A (en) * | 2016-10-08 | 2017-01-04 | 广西师范大学 | A kind of based on genetic algorithm with the image deblurring method of Wiener filtering |
| CN107945127A (en) * | 2017-11-27 | 2018-04-20 | 南昌大学 | A kind of High-motion picture deblurring method based on image column gray probability uniformity |
-
2018
- 2018-08-22 CN CN201810957343.5A patent/CN109242790A/en not_active Withdrawn
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104408707A (en) * | 2014-10-28 | 2015-03-11 | 哈尔滨工业大学 | Rapid digital imaging fuzzy identification and restored image quality assessment method |
| CN106296619A (en) * | 2016-10-08 | 2017-01-04 | 广西师范大学 | A kind of based on genetic algorithm with the image deblurring method of Wiener filtering |
| CN107945127A (en) * | 2017-11-27 | 2018-04-20 | 南昌大学 | A kind of High-motion picture deblurring method based on image column gray probability uniformity |
Non-Patent Citations (1)
| Title |
|---|
| 刘巍等: "《机床动态检测中的高速图像运动去模糊还原》", 《仪器仪表学报》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111626946A (en) * | 2020-04-23 | 2020-09-04 | 武汉理工大学 | Motion blur kernel measurement method for high-speed material transmission visual detection system |
| CN113327206A (en) * | 2021-06-03 | 2021-08-31 | 江苏电百达智能科技有限公司 | Image fuzzy processing method of intelligent power transmission line inspection system based on artificial intelligence |
| CN113327206B (en) * | 2021-06-03 | 2022-03-22 | 江苏电百达智能科技有限公司 | Image fuzzy processing method of intelligent power transmission line inspection system based on artificial intelligence |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109459750B (en) | Front multi-vehicle tracking method integrating millimeter wave radar and deep learning vision | |
| CN109272524B (en) | Small-scale point cloud noise denoising method based on threshold segmentation | |
| CN106863014B (en) | A kind of five-axle number control machine tool linear axis geometric error detection method | |
| CN108198224B (en) | Linear array camera calibration device and calibration method for stereoscopic vision measurement | |
| CN110189257B (en) | Point cloud acquisition method, device, system and storage medium | |
| CN108413917B (en) | Non-contact three-dimensional measurement system, non-contact three-dimensional measurement method and measurement device | |
| DE102018108027A1 (en) | Object detecting device | |
| DE102015005267A1 (en) | Information processing apparatus, method therefor and measuring apparatus | |
| DE112011100652T5 (en) | THREE-DIMENSIONAL MEASURING DEVICE, PROCESSING METHOD AND NON-VOLATILE COMPUTER-READABLE STORAGE MEDIUM | |
| CN110260795B (en) | Absolute displacement detection method based on incremental absolute grating ruler | |
| DE112010002843T5 (en) | Surface tracking on image basis | |
| CN115482195B (en) | Train part deformation detection method based on three-dimensional point cloud | |
| CN102798456B (en) | Method, device and system for measuring working amplitude of engineering mechanical arm support system | |
| AT509103A1 (en) | METHOD AND DEVICE FOR SUPPORTING THE MEASURING ACCURACY OF GPS / IMU SYSTEMS | |
| CN106127145A (en) | Pupil diameter and tracking | |
| DE102016216196A1 (en) | Sensor system with an optical sensor unit and an automation system | |
| DE102019128814B4 (en) | Camera for detecting an object flow and method for determining the height of objects | |
| CN104236498A (en) | Method for measuring end size of switch rail | |
| CN113554697A (en) | Cabin section profile accurate measurement method based on line laser | |
| DE102020007045A1 (en) | Object detection system with transit time sensor | |
| CN109242790A (en) | A kind of image deblurring appraisal procedure towards fields of measurement | |
| CN111536872A (en) | Two-dimensional plane distance measuring device and method based on vision and mark point identification device | |
| CN106846372A (en) | Human motion quality visual A+E system and method | |
| Liu et al. | Outdoor camera calibration method for a GPS & camera based surveillance system | |
| CN111121637A (en) | Grating displacement detection method based on pixel coding |
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 | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20190118 |
|
| WW01 | Invention patent application withdrawn after publication |