CN107464219A - The motion detection and noise-reduction method of consecutive image - Google Patents
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- CN107464219A CN107464219A CN201610392544.6A CN201610392544A CN107464219A CN 107464219 A CN107464219 A CN 107464219A CN 201610392544 A CN201610392544 A CN 201610392544A CN 107464219 A CN107464219 A CN 107464219A
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Abstract
The invention discloses a kind of motion detection of consecutive image and noise-reduction method, including：Obtain former frame and current frame image；Low frequency processing is carried out to the former frame and current frame image, obtains the low-frequency image of former frame and present frame；According to the comparison of the former frame and the low-frequency image of present frame, judge whether to be adapted to carry out noise reduction process to current frame image；According to the judged result, stop or carry out noise reduction process.First judge whether to be appropriate for noise reduction process before noise reduction is carried out, solve x-ray equipment after powering, because dosage is not very stable, the image that these need not be carried out with noise reduction process is excluded continuous several frames when just starting, and resource is saved for equipment.
Description
Technical field
The present invention relates to the method for testing motion of the detection method of image motion, more particularly to medical image.
Background technology
As in X-ray examination medical image in medical diagnostic image, motion detection is carried out to consecutive image,
The purpose of detection image motion is the time domain noise reduction for successive image.It is existing associated with time domain noise reduction
Motion detection technique be largely divided into two ways：One kind is to be based on motion strength detection, and another kind is base
In the detection of motion compensation.Carried out based on the typically with good grounds block of pixels of motion strength detection and according to pixel
The two ways of detection, the block of pixels of block of pixels or pixel and previous frame image to current frame image
Or pixel is compared, whether the block of pixels or pixel that judge current frame image are motion pixel
Block or motion pixel, that is, each block of pixels or pixel for judging current frame image are moving mass
Either motor point or static block or rest point, this motion detection mode are mainly used in shooting position
All there is no significant change or a movement with equipment, only the respiratory movement of human body itself, enterogastric peristalsis motion,
The flowing of contrast agent and the insertion of conduit cause body local the scene such as to change also in contrastographic picture
In image.Motion compensation detection be need to estimate pixel in present frame or block of pixels correspond to it is previous
The motion vector of two field picture, current pixel point or block of pixels can be finally found along movement locus in former frame
Position in image, then need to carry out image overall translation according to vector motion to current frame image.
The application scenarios of this motion compensation, which are generally the generation mass motion of x-ray equipment or scanning bed movement, to be caused
Human body move integrally, current frame image just has overall phase with previous frame image in this case
To motion, there is position deviation, motion compensation is exactly to need to find this motion vector deviation, can be current
Continue on corresponding the corresponding position of frame and former frame.
Based on the motion strength detection, existing technical method is typically all frame differential method, as above institute
State including the method based on block of pixels and the method based on pixel.Fig. 1 is existing to be entered with pixel block method
The schematic flow sheet of row motion strength detection.Method based on block of pixels includes：S11, to former frame and work as
Prior image frame carries out N × M piecemeal, and N and M are default constant；S12, estimate each pixel
The noise criteria of block is poor；S13, calculates former frame and current frame image corresponds to the similarity of piecemeal, the phase
Judged like degree using the average value of former frame and the gray scale difference of current frame image；S14, obtained using in S12
The standard deviation arrived formulates threshold value；The similarity compared with threshold value, is determined whether to move by S15
Block or static block.Another method based on pixel is that calculating former frame and current frame image are corresponding
The gray scale difference of pixel；Given threshold, the gray scale difference of pixel is made a distinction judgement according to threshold value；In advance
Two proportion threshold values are first set, count ratio of the gray scale difference shared by more than the pixel number of a certain threshold value,
The ratio value is more than proportion threshold value set in advance, then it is assumed that motion pixel is more in image, and ratio value is small
Then think that motion pixel is less in image in the proportion threshold value of setting.
Existing technical disadvantages are that estimated noise is more difficult, and estimation result is not accurate enough；When just starting
Continuous a few frame dosage are not stable, it is necessary to adjusting several frames reaches optimal display result, and just dosage is fixed
Get off, the motion detection before dose stability is that image noise reduction is not made much sense；Only utilize gray scale
Average ratio is compared with similitude, and more to variations in detail, the more regional movement of such as marginal point minutiae point judges accurate
True property is not high enough, causes the image effect after noise reduction not ideal enough.
The content of the invention
To solve the problems, such as to mention in the prior art, the present invention provides a kind of noise-reduction method of consecutive image,
Including：Obtain former frame and current frame image；Low frequency processing is carried out to the former frame and present frame figure,
Obtain the low-frequency image of former frame and present frame；According to the ratio of the low-frequency image of the former frame and present frame
Compared with, judge whether be adapted to current frame image carry out noise reduction process；According to the judged result, stop or
Carry out noise reduction process.
Optionally, the noise reduction process includes：Motion detection is carried out to present frame low-frequency image；Based on institute
State the moving region that the present frame low-frequency image is divided into different stage by motion detection；According to different levels
Different noise reduction coefficients is not set to carry out noise reduction process.
Optionally, it is described to judge whether to be adapted to include current frame image progress noise reduction process：Calculate previous
The effective coverage of frame and present frame low-frequency image；Contrast the effective of the former frame and present frame low-frequency image
Area grayscale value difference；Be judged as being not suitable for when effective coverage gray value differences are more than presetting first threshold into
Row noise reduction process, if effective coverage gray value differences are judged as being appropriate for noise reduction when being less than the first threshold
Processing.
Optionally, the effective coverage is the remaining area behind removal background area.
Optionally, the background area includes：First background area caused by blocking radiation；And/or directly expose
Caused second background area.
Optionally, first background area is removed by the coordinate relation of equipment；Second background
Area is removed by gray value contrast.
The present invention also provides a kind of method for testing motion of consecutive image, including：Obtain former frame and current
Two field picture；Piecemeal is carried out to the former frame and current frame image；Calculate each piecemeal of current frame image
Variance；Corresponding blocks according to different variances using distinct methods contrast present frame and previous frame image；Root
According to the motion of the comparing result detection image.
Optionally, the method for testing motion, in addition to：The variance of the current frame image piecemeal with it is pre-
If Second Threshold is compared；If being less than Second Threshold, present frame and previous is contrasted by gray value average
The corresponding piecemeal of two field picture；If and/or be more than Second Threshold, pass through Grad and contrast present frame and former frame
The corresponding piecemeal of image.
Optionally, in the method for testing motion, the former frame and current frame image be by low frequency at
Image after reason.
The noise-reduction method of a present invention also same consecutive image, including：Obtain former frame and present frame figure
Picture；Low frequency processing is carried out to the former frame and present frame figure, obtains the low frequency figure of former frame and present frame
Picture；According to the comparison of the former frame and the low-frequency image of present frame, judge whether to be adapted to present frame figure
As carrying out noise reduction process；When being judged as being adapted to carry out noise reduction process to current frame image, to described previous
Frame and current frame image carry out piecemeal；Calculate the variance of each piecemeal of current frame image；According to different sides
Difference contrasts the corresponding blocks of present frame and previous frame image using distinct methods；Detected according to the comparing result
The motion of image；Different level Motions sets different noise reduction coefficients to carry out noise reduction process.
Compared with prior art, the present invention in using low frequency processing after image, improve estimated noise compared with
Difficulty, the problem of estimation result is not accurate enough；First judge whether to be appropriate at noise reduction before noise reduction is carried out
Reason, solve continuous several frames during to just starting because dosage is not stable, the image without noise reduction process
Excluded, resource is saved for equipment；Different comparative approach is used to different moving mass, improved
Motion determination accuracy, further increase the noise reduction picture effect of image.
Brief description of the drawings
Fig. 1 is the existing schematic flow sheet that motion strength detection is carried out with method of partition.
Fig. 2 is the schematic diagram that wavelet decomposition is carried out to image of the present invention；
Fig. 3 is to include background area medical diagnosis figure by what x-ray imaging equipment gathered；
Fig. 4 is extraction former frame and present frame and the schematic diagram of effective coverage；
Fig. 5 is that image similarity judges schematic flow sheet；
Fig. 6 is adjacent two frames X ray schematic diagram；
Fig. 7 is the sport rank mark schematic diagram of adjacent two field pictures.
Embodiment
It is understandable to enable the above objects, features and advantages of the present invention to become apparent, below in conjunction with the accompanying drawings
The embodiment of the present invention is described in detail.Elaborate in the following description detail so as to
In fully understanding the present invention.But the present invention can be come in fact with a variety of different from other manner described here
Apply, those skilled in the art can do similar popularization in the case of without prejudice to intension of the present invention.Therefore originally
Invention is not limited by following public embodiment.
Fig. 2 is the schematic diagram that wavelet decomposition is carried out to image of the present invention.To present frame and previous frame image
A wavelet decomposition is carried out respectively, takes the low-frequency image after decomposing respectively.Wavelet decomposition is exactly to utilize two
Wave filter, a low pass, a high pass, convolution algorithm (filtering process) is carried out to the ranks of image respectively,
After convolution algorithm, then down-sampling is carried out respectively, as described in Figure 1, by such process, artwork can quilt
Resolve into four small figures, a low frequency (LL1), 3 high frequency (HL1、LH1、HH1), 3 high frequencies
In the details comprising noise and some images, the most contents of image, size are almost contained in low frequency
It is the 1/4 of artwork.
Former frame Jing Guo wavelet decomposition and current frame image are carried out using threshold value and variance to background area
Segmentation, specific implementation process are as follows：Background mainly includes two aspects, and one is due to hardware device
Background area caused by camera lens and beam-defining clipper, this background area is mainly dark region on image, into black.
Another background area refers to that direct exposure region, that is, X ray do not pass through region (the Kong Pai areas of human body
Domain), it is in be highlighted that performance of this region on image, which is exactly, i.e., white.Fig. 3 is to pass through X ray
Imaging device collection includes background area medical diagnosis figure, as shown in figure 3, the black region beyond circle is just
It is background area 11 caused by beam-defining clipper, the very bright white in circle the inside is exactly direct exposure region 12.For limiting beam
The removal of background area 11 caused by device, it is that the coordinate relation being passed to according to hardware device carries out background exclusion；
It is as follows for directly exposing minimizing technology：Threshold value and variance are manually set according to image actual grey situation,
For example threshold value is arranged to T, variance is set to VAR, and many lattices (such as 100*100) are divided into image,
The average gray and gray variance of each lattice are calculated, if average gray is more than threshold value T, gray scale
Variance is less than value VAR, then this lattice is taken as direct exposure region background area.
Then former frame and the effective district of current frame image are defined, removes background area by above-mentioned steps
The common factor in former frame and current frame image Qu Liangzheng human bodies area is utilized respectively as effective district, two field pictures
State background method remove background after, here due to remove background inexactness or image have motion
Front and rear image is caused to have an evolution, on the front and rear remaining region of two field pictures not fully corresponds to.But transport
It is dynamic to cause picture position to convert, because equipment shooting is very fast, the scope that position is moved in the very short time
Can very little.Fig. 4 is extraction former frame and present frame and the schematic diagram of effective coverage.As shown in figure 4,
Grey blockage 121 is background area in previous frame image, the same grey blockage 122 in current frame image
It is background area, the background area measured from former frame and current frame image is inconsistent, and common factor is taken to background area
And it is to overlap area 13 (effective district) that the common factor 123 for excluding two background areas, which is left region,.
Then judge that former frame and current frame image whether there is special circumstances, if special circumstances be present, after
Time domain noise reduction can not be done by continuing, that is, motion detection algorithm needs to detect abnormal conditions in advance in advance eventually
Only.Special circumstances mainly include：The unstable ash for causing front and rear two field picture of former frame dosage of consecutive image
Spend it is widely different (during shooting consecutive image, need regulating dosage always when just starting to shoot former frames, one
Optimal display result is arrived in straight regulation, just dose stability is got off, follow-up image then can be under consistent dose
Continue to shoot), or current frame image enters the implants such as substantial amounts of metal on the basis of previous frame image
Situations such as, the gray difference of frame is also larger before and after such case, needs algorithm to make inspection for these situations
Survey.Detection algorithm process is calculating present frame and the gray scale difference of former frame low-frequency image human body effective district entirety
Average value, if the average value of gray scale difference is more than default threshold value, then it represents that front and rear two inter-pixel ash
It is too big to spend difference, belongs to abnormal, current frame image is entirely just flagged as dyskinesia image, and algorithm is whole
Only, subsequent detection algorithm is no longer carried out, subsequently will not also carry out recursive noise reduction.
If in above-mentioned steps judged result when being appropriate for recursive noise reduction, then to calculate former frame and current
Frame similitude, Fig. 5 are that image similarity judges schematic flow sheet；The step of calculating similitude includes：
S21, piecemeal is carried out to removing the former frame of background area and the low-frequency image of present frame；
S22, calculate the variance of each piecemeal of present frame；
S23, judges whether the variance is more than given threshold；
S24, if variance yields is less than given threshold, compare the gray scale of present frame piecemeal and corresponding former frame piecemeal
Value, different motion region is defined as according to different intensity value ranges；The motor area can be divided into quiescent centre,
Slow motor area, middle motion area and strong movements area, general gray value maximum desirable 4096, in this reality
Apply in example, if the gray value differences of corresponding blocks are within 20, it is believed that the piecemeal is quiescent centre；If 20 to
Within 40, it is believed that the piecemeal is slow motor area；If within 40 to 60, it is believed that the piecemeal is medium
Motor area；If more than strong movements area is considered if 60, in above-mentioned deterministic process, if variance is less than certain
One threshold value, then than shallower, block inner tissue passes through the comparison of gray value than more uniform in the region
Judge the image block is the moving mass of what grade,
S25, if variance yields is more than given threshold, compare the gradient of present frame piecemeal and corresponding former frame piecemeal
Value, different motion area is defined as according to different Grad scopes, identical when compared with above-mentioned gray value, this
Rigen divides into quiescent centre according to motion described in different gradient value differences, slow motor area, middle motion area and
Strong movements area.If a certain Local Deviation is more than a certain threshold value, illustrate in the region variations in detail compared with
It is more, as marginal point, minutiae point are more, present frame and the gradient similitude of former frame corresponding blocks are calculated, is used
Similitude of the gradient similitude as region unit, the gradient of image are to represent gray value between image consecutive points
Change, consecutive points grey scale change is bigger, then it represents that gradient is bigger, and change is smaller, then it represents that gradient is got over
It is small, for example piece image includes bone portion, the edge gradient of bone is just very big, bones ratio
Flatter, then gradient is with regard to very little.
All pieces of sport ranks are recorded by above-mentioned steps.Four difference grades of setting, quiescent centre, slowly
Motor area, middle motion area, strong movements area.To each small piecemeal for the low-frequency image for dividing sports-like area
Up-sampled, corresponded on original image, the motion detection result corresponding to mark in original graph.Often
Individual small segmented areas is four different motion grades according to block difference value and threshold qualitative, and difference is being determined
After sport rank, follow-up recursive noise reduction can sets different pass respectively according to different noise reduction levels
Return coefficient, quiescent centre sets larger recursion coefficient, and strong movements area sets the recursion coefficient of very little, delays
Slow motion, the then setting of moderate strength are in middle recursion coefficient etc..
Fig. 6 is adjacent two frames X ray schematic diagram.6a and 6b is adjacent two frame shot in Same Scene
Image, it can be seen that " pointer " of triangle is there occurs movement, and there occurs trickle change for position.Figure
7 be the sport rank mark schematic diagram of adjacent two field pictures.Motion detection result it can be seen from the figure that is examined
The moving region measured is all located at the band of position where triangle pin.With four kinds in motion detection result figure
Color, four sport ranks are represented respectively, wherein ater represents quiescent centre, and pure white represents strong movements
Area, there are two kinds of grey between black and white, one kind represents slow motor area, during one kind represents
Deng motor area.
Although the present invention is disclosed as above with preferred embodiment, it is not for limiting the present invention, appointing
What those skilled in the art without departing from the spirit and scope of the present invention, may be by the disclosure above
Methods and technical content makes possible variation and modification to technical solution of the present invention, therefore, every not take off
From the content of technical solution of the present invention, the technical spirit according to the present invention is made any to above example
Simple modification, equivalent variation and modification, belong to the protection domain of technical solution of the present invention.
Claims (10)
- A kind of 1. noise-reduction method of consecutive image, it is characterised in that including：Obtain former frame and current frame image；Low frequency processing is carried out to the former frame and current frame image, obtains the low frequency figure of former frame and present frame Picture；According to the comparison of the former frame and the low-frequency image of present frame, judge whether to be adapted to current frame image Carry out noise reduction process；According to the judged result, stop or carry out noise reduction process.
- 2. the noise-reduction method of consecutive image according to claim 1, it is characterised in that the noise reduction process Including：Motion detection is carried out to present frame low-frequency image；The present frame low-frequency image is divided into the moving region of different stage based on the motion detection；Different noise reduction coefficients is set to carry out noise reduction process according to different ranks.
- 3. the noise-reduction method of consecutive image according to claim 1, it is characterised in that described to judge whether It is adapted to include current frame image progress noise reduction process：Calculate the effective coverage of former frame and present frame low-frequency image；Contrast the former frame and the effective coverage gray value differences of present frame low-frequency image；It is judged as being not suitable for carrying out noise reduction process when effective coverage gray value differences are more than presetting first threshold, If effective coverage gray value differences are judged as being appropriate for noise reduction process when being less than the first threshold.
- 4. the noise-reduction method of consecutive image according to claim 3, it is characterised in thatThe effective coverage is the remaining area behind removal background area.
- 5. the noise-reduction method of consecutive image according to claim 4, it is characterised in thatThe background area includes：First background area caused by blocking radiation；And/orSecond background area caused by directly exposing.
- 6. the noise-reduction method of consecutive image according to claim 5, it is characterised in thatFirst background area is removed by the coordinate relation of equipment；Second background area is removed by gray value contrast.
- A kind of 7. method for testing motion of consecutive image, it is characterised in that including：Obtain former frame and current frame image；Piecemeal is carried out to the former frame and current frame image；Calculate the variance of each piecemeal of current frame image；Corresponding blocks according to different variances using distinct methods contrast present frame and previous frame image；According to the motion of the comparing result detection image.
- 8. method for testing motion according to claim 7, it is characterised in that also include：The variance of the current frame image piecemeal is compared with default Second Threshold；If being less than Second Threshold, the corresponding piecemeal of present frame and previous frame image is contrasted by gray value average； And/orIf being more than Second Threshold, the corresponding piecemeal of present frame and previous frame image is contrasted by Grad.
- 9. method for testing motion according to claim 8, it is characterised in thatThe former frame and current frame image are the image after low frequency is handled.
- A kind of 10. noise-reduction method of consecutive image, it is characterised in that including：Obtain former frame and current frame image；Low frequency processing is carried out to the former frame and current frame image, obtains the low frequency figure of former frame and present frame Picture；According to the comparison of the former frame and the low-frequency image of present frame, judge whether to be adapted to current frame image Carry out noise reduction process；When being judged as being adapted to carry out noise reduction process to current frame image,Piecemeal is carried out to the former frame and current frame image；Calculate the variance of each piecemeal of current frame image；Corresponding blocks according to different variances using distinct methods contrast present frame and previous frame image；According to the motion of the comparing result detection image；Different level Motions sets different noise reduction coefficients to carry out noise reduction process.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108335278A (en) * | 2018-03-18 | 2018-07-27 | 广东欧珀移动通信有限公司 | Processing method, device, storage medium and the electronic equipment of image |
| CN108391097A (en) * | 2018-04-24 | 2018-08-10 | 冼汉生 | A kind of video image method for uploading, device and computer storage media |
| CN109248378A (en) * | 2018-09-09 | 2019-01-22 | 深圳硅基仿生科技有限公司 | Video process apparatus, method and the retina stimulator of retina stimulator |
| CN112819788A (en) * | 2021-02-01 | 2021-05-18 | 上海悦易网络信息技术有限公司 | Image stability detection method and device |
-
2016
- 2016-06-03 CN CN201610392544.6A patent/CN107464219A/en active Pending
Non-Patent Citations (2)
| Title |
|---|
| 余成波编著: "《数字图像处理技术及MATLAB实现》", 30 June 2003, 重庆大学出版社 * |
| 曾峦编著: "《侦察图像获取与融合技术》", 30 May 2015, 国防工业出版社 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108335278A (en) * | 2018-03-18 | 2018-07-27 | 广东欧珀移动通信有限公司 | Processing method, device, storage medium and the electronic equipment of image |
| CN108335278B (en) * | 2018-03-18 | 2020-07-07 | Oppo广东移动通信有限公司 | Image processing method and device, storage medium and electronic equipment |
| CN108391097A (en) * | 2018-04-24 | 2018-08-10 | 冼汉生 | A kind of video image method for uploading, device and computer storage media |
| CN109248378A (en) * | 2018-09-09 | 2019-01-22 | 深圳硅基仿生科技有限公司 | Video process apparatus, method and the retina stimulator of retina stimulator |
| CN109248378B (en) * | 2018-09-09 | 2020-10-16 | 深圳硅基仿生科技有限公司 | Video processing device and method of retina stimulator and retina stimulator |
| CN112819788A (en) * | 2021-02-01 | 2021-05-18 | 上海悦易网络信息技术有限公司 | Image stability detection method and device |
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