CN103870236B - The noise-reduction method and system of computed tomography images - Google Patents
The noise-reduction method and system of computed tomography images Download PDFInfo
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- CN103870236B CN103870236B CN201210544602.4A CN201210544602A CN103870236B CN 103870236 B CN103870236 B CN 103870236B CN 201210544602 A CN201210544602 A CN 201210544602A CN 103870236 B CN103870236 B CN 103870236B
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Abstract
The invention provides a kind of method for reducing the noise in image, in the method, after receiving the image of Noise, the first weight is estimated from the image of the Noise with the first smoothness control parameter and the first retrieval window size, pre-filtering is carried out to the image of the Noise with first weight by non-local mean filter method again, to obtain the image after pre-filtering, then the second weight is estimated from the image after the pre-filtering with the second smoothness control parameter and the second retrieval window size, the image of the Noise is filtered with second weight by non-local mean filter method again, to obtain the image after noise reduction.
Description
Technical field
The present invention relates to a kind of image denoising method and system, in particular it relates to non-local mean filter method to image
Carry out the method and system of noise reduction.
Background technology
Computed tomography (CT) image Noise, noise jamming can reduce lesion detectability.Appropriate noise reduction can
Increase lesion detectability and improve diagnosis accuracy.Therefore noise reduction turns into CT imaging fields research emphasis for many years, people
Problem for noise reduction has made many effort.Non-local mean filter method is that one kind takes average for all of pixel on image
Image noise reduction process.Certain pixel is by noise jamming, if it with neighborhood there is identical noise free pixel value (e.g., to organize phase
Together), then its noise free pixel value can be obtained by itself with its neighborhood weighted average.Especially, when weighted average is done, certain
The weight of one pixel depend on the pocket centered on the pixel and the pocket centered on object pixel it
Between similarity, the weight of the pixel higher with the similarity of object pixel is higher.Non-local mean filter method provides one
The similarity measure of stabilization calculates the weight of each pixel.
However, when noise content is very high, similarity measure may be destroyed by noise, cause the product of artifact, spot
It is raw, reduce the identification of small structure.
The content of the invention
The present invention relates to a kind of method for reducing the noise in image, in the method, after receiving the image of Noise, use
First smoothness control parameter and the first retrieval window size estimate the first weight from the image of the Noise, then by non-office
Domain mean filter method carries out pre-filtering with first weight to the image of the Noise, to obtain the image after pre-filtering,
Then the second weight is estimated from the image after the pre-filtering with the second smoothness control parameter and the second retrieval window size,
The image of the Noise is filtered with second weight by non-local mean filter method again, after obtaining noise reduction
Image.
Brief description of the drawings
It is described for embodiments of the invention by with reference to accompanying drawing, the present invention may be better understood, in the accompanying drawings：
Fig. 1 is the figure of one embodiment of CT imaging devices.
Fig. 2 is the schematic block diagram of CT imaging devices shown in Fig. 1.
Fig. 3 is the flow chart of the noise-reduction method in one embodiment of the invention.
Fig. 4 is the flow chart of the pre-filtering technique in the noise-reduction method shown in Fig. 3.
Fig. 5 is the flow chart of the filtering technique in the noise-reduction method shown in Fig. 3.
Fig. 6 (a) shows that one is mixed with 60% and is obtained with the non-local mean filter method noise reduction for having carried out pre-filtering
The image for obtaining and the output image formed after 40% original image.
Fig. 6 (b) shows that one is mixed with 60% and is obtained with the non-local mean filter method noise reduction for not carrying out pre-filtering
The image for obtaining and the output image formed after 40% original image.
Specific embodiment
Specific embodiment of the invention will be below described in detail.In order to avoid excessive unnecessary details,
Known structure or function will be described in detail in herein below.
The language of approximation used herein can be used for quantitative expression, show in the case where basic function is not changed
Quantity can be allowed certain variation.Therefore, the numerical value corrected with the language such as " about ", " left and right " is not limited to the exact value
Itself.In certain embodiments, " about " or " left and right " represent allow its correct numerical value positive and negative 10 (10%)
In the range of change, such as, what " about 100 " represented can be any numerical value between 90 to 110.Additionally, in " the about first number
Be worth second value " statement in, " about " at the same correct the first numerical value and second value two values.In some cases,
Approximating language may be relevant with the precision of measuring instrument.
In addition to being defined, technology used herein and scientific terminology are with universal with art personnel of the present invention
The identical meanings of understanding.Term " first " used herein, " second " etc. are not offered as any order, quantity or importance, and
It is only intended to distinguish a kind of element and another element.Also, the "a" or "an" does not indicate that the restriction of quantity, but table
Show the relevant item in the presence of.
Embodiments of the invention are related to a kind of method to computed tomography (CT) image noise reduction, the method typically to use
In various CT imaging systems, such as, a kind of CT imaging systems as described in the patent of the U.S. the 7th, 706,497.
Fig. 1 is a kind of schematic diagram of CT imaging systems 10.Fig. 2 is the schematic block diagram of the system 10 shown in Fig. 1.Show described
In the embodiment of example property, CT imaging systems 10 include a pallet 12 for representing third generation CT imaging systems, and the pallet 12 has one
Individual radiographic source 14, can launch X-ray pencil-beam 16 to the detector array 18 of the opposite side on the pallet 12.
The detector array 18 is made up of a plurality of detector rows (not shown), wherein the detector row includes plural number
Individual detecting element 20, together for sensing through certain object, such as X-ray beam of medical patient 22.The detecting element 20 is each
An electric signal for representing collision radiation beam density is produced, wherein the electric signal for representing collision radiation beam density can table
Show light beam through the Weaken degree after object or patient.During scanning obtains ray projection data, the He of the pallet 12
The element installed thereon rotates around a pivot 24.Fig. 2 only show a line detecting element 20 (i.e. detector row), but
Multi-slice detector array 18 includes the plurality of parallel detector row being made up of detecting element 20, so, just can be by once
Scanning obtains the data for projection of the plurality of parallel layer of correspondence or quasi-parallel layer simultaneously.
The rotation of the pallet 12 and the running of the radiographic source 14 are carried out by the controlling organization 26 of CT imaging systems 10
Control and management.The controlling organization 26 includes being provided the He of ray controller 28 of energy and clock signal to the radiographic source 14
For controlling the pallet engine controller 30 of the position of velocity of rotation and pallet 12.Data collecting system in controlling organization 26
32 from the sampling collection analogue data of the detecting element 20, and converts the data into data signal, for subsequent treatment.Imagescope
(image reconstructor) 34 receives the sampling collection and digitized number of rays from the data collecting system 32
According to carrying out high speed image reproduction.The image of the reproduction is transfused to computer 36, and described image is stored in great Rong by the computer
In amount storage device 38.
The computer also receives the instruction and sweep parameter being input into by operator's console 40, and the console includes key
Disk and (or) other users input unit.By a united display system 42, operator can observe institute from computer 36
State reproduction image and other data.Instruction that computer 36 is provided using operator and parameter come to data collecting system 32, penetrate
Lane controller 28 and pallet engine controller 30 provide control signal and information.Additionally, computer 36 is by running a work
Make platform engine controller 44 to control one for patient 22 to be arranged to the mobile working platform 46 in pallet 12.Especially,
The workbench 46 is the position by the removing patient of opening 48 of the pallet.
In one embodiment, computer 36 includes a medium 52 being used for from machine readable, such as floppy disk, CD (CD-
ROM the device of instruction or data, such as disk drive, CD-ROM drive or CD-ROM device) or in Digital versatile disc (DVD) are read.Ying Li
The thing of solution, also in the presence of other suitable machine readables type of memory (at will fors two examples for, such as CD-rewritable with
Flash memory), any of which is not excluded for herein.In another embodiment, computer 36 is performed and is stored in admittedly
Instruction in part (not shown).Usually, data collecting system 32, imagescope 34 and computer 36 as shown in Figure 2 are installed on
At least one of in processor be composed of the program for performing following method and steps.But following methods and be not limited to the use of CT into
As system 10, can be also connected with other different types of imaging systems and used.In one embodiment, computer 36 is composed of realization
The program of function described herein, therefore, computer referred to herein is not limited to industry commonly called integrated circuit institute's generation
The computer of table, and should have wider scope, including computer, processor, single-chip microcomputer, miniature electronic meter, programmable patrol
Collect controller, application specific integrated circuit and other programming devices.
The computer 36 can perform (the i.e. non-local average filter of non-local mean filter noise reduction algorithm by program composition
Ripple method) come the noise in the input picture (original image) for reducing Noise.In non-local mean filter method, object pixel
Denoising value is to take what weighted average was calculated by all pixels in the retrieval window centered on the object pixel.Also
It is to say, the pixel in retrieval window is used to estimate the denoising value of object pixel.Retrieval window in different pixels weighting when
Weight depends on its similarity with object pixel, and the weight of the pixel higher with object pixel similarity is higher.In retrieval window
Each pixel be determined by the neighborhood of the neighborhood and object pixel that compare the pixel with the similarity of object pixel, so,
The weight of certain pixel is small depending on the pocket (neighborhood) centered on the pixel and centered on object pixel one
Similarity between block region.
Such as, if being I by the graphical representation of Noise, the denoising value of pixel (i, j) is by centered on the pixel (i, j)
Retrieval window in pixel take weighted average method calculate obtain.The size of wherein retrieval window is m*n (two dimension) or m*n*l
(three-dimensional), the size of the neighborhood for comparing similarity is sx*sy.One typical two dimension non-local average noise reduction algorithm can table
It is shown as：
Wherein w (a, b, i, j) is the weight of pixel (i, j), sometimes alternatively referred to as similarity measure, and it can be by following public affairs
Formula is calculated：
Wherein h is smoothness control parameter, and σ is poor for noise criteria.
Wherein g (x, y) is the weighting function for relying on distance, and it can be Gaussian function or constant.
Similarly, a typical three-dimensional non-local average noise reduction algorithm is represented by：
The weight or similarity measure w (a, b, i, j) needed for non-local average noise-reduction method are estimated from original image
When, noise present in original image often results in the generation of mistake.If noise reduction was carried out to original image before weight is calculated, then
Weight is calculated based on the image after the noise reduction, the influence of noise can be reduced.The embodiment provides one kind in non-office
The method that influence of the noise to weight calculation is reduced in the average noise-reduction method of domain.
Pre-filtering (pre-noise reduction), then the image (pre-filtering from after the noise reduction are carried out to original image with a kind of noise reduction algorithm
Image afterwards) middle calculating weight, non-local average noise reduction is carried out with the weight, it is possible to decrease noise is to weight calculation in original image
Influence.The challenge of pre-filtering is, if for pre-filtering noise reduction algorithm without very strong border holding capacity, it may
The loss of generation resolution ratio in the image exported after pre-filtering can be caused.Further, if existed in image after pre-filtering
Artifact, is used in the image exported after noise reduction also it will be observed that the presence of artifact based on the weight that the image is obtained.Therefore, it is used for
The noise reduction algorithm of pre-filtering need to have very strong border holding capacity, and in the absence of substantially pseudo- in the image obtained after pre-filtering
Shadow.
Non-local average noise reduction algorithm meets above-mentioned requirements.In certain embodiments, can be calculated using non-local average noise reduction
Method carries out pre-filtering.Pre-filtering noise reduction is carried out to original image with non-local average noise reduction algorithm, the figure from after pre-filtering noise reduction
Weight is calculated as in, then original image is filtered (noise reduction) with non-local average noise reduction again with the weight.Specifically,
In pre-filter process, the first weight is estimated from original image with aforesaid equation (2) or (4), with aforesaid equation (1) or
(3) non-local average noise reduction algorithm noise reduction is carried out to original image with first weight, to obtain the image after pre-filtering.Again
The second weight is estimated from the image after the pre-filtering with aforesaid equation (2) or (4), with aforesaid equation (1) or (3) with
Second weight carries out non-local average noise reduction algorithm noise reduction to original image again, to obtain the image after noise reduction.
In certain embodiments, when having much noise in detected pixel, the similarity of the pixel and its neighborhood is very
It is small, and the principle of non-local average noise reduction algorithm is based on, the weight of the smaller pixel of similarity is smaller, therefore, in pre-filtering,
Its filtering level to the pixel with much noise is smaller.If the calculating of similarity is based on after a kind of such pre-filtering
Image, due in the image after original image and pre-filtering with much noise pixel it is at same location, can cause
Artifact is more intensification.
In order to solve this problem, stronger noise reduction capability can be used in pre-filtering so that noise reduction energy during pre-filtering
Power is better than noise reduction capability during actual filtering.So, smoothness image higher can be obtained after pre-filtering, therefore, it is pre- at this
In filtered image, the influence of the big pixel of noisiness has been reduced, by calculating weight from the image after the pre-filtering,
The artifact in the image exported after final filtering noise reduction can be reduced.The noise reduction capability of the non-local average noise reduction algorithm can pass through
Smoothness control parameter h in control aforesaid equation is controlled with least one of window size is retrieved.
In certain embodiments, the smoothness control parameter h for pre-filtering is bigger than for what is filtered, or more appropriately,
It is not less than 1.The smoothness control parameter h used in pre-filtering is bigger, and the image obtained after pre-filtering is more smooth.Additionally,
Because non-local average Method of Noise has very strong border holding capacity, pre-filtering is carried out with larger smoothness control parameter h
Resolution ratio will not be caused to be reduced.
In certain embodiments, the retrieval window size for pre-filtering is bigger than for what is filtered, or is big more appropriately
In 3*3 (two dimension) or 3*3*3 (three-dimensional).Herein, retrieval window refers at least one-dimensional size of the retrieval window compared with " big "
Size more than less retrieval window, and remaining dimension is at least not less than less retrieval window.When the size of retrieval window
When larger, non-local average noise reduction algorithm has stronger noise reduction capability, and the artifact of generation is less.Additionally, increase retrieval window
Size can't obscure high-contrast structures.The use of larger retrieval window is to reduce former in Similarity Measure in pre-filtering
Another method of the influence of the noise in beginning image.
As shown in figure 3, a kind of noise-reduction method is comprised the following steps：In step sl, an image for Noise is received；
In step S2, the first power is estimated from the image of the Noise with the first smoothness control parameter and the first retrieval window size
Weight；In step s3, pre-filtering is carried out to the image of the Noise with first weight by non-local mean filter method,
To obtain the image after pre-filtering；In step s 4, with the second smoothness control parameter and the second retrieval window size from described
The second weight is estimated in image after pre-filtering；In step s 5, by non-local mean filter method with second weight pair
The image of the Noise is filtered, to obtain the image after noise reduction.In certain embodiments, the first smoothness control
Parameter be more than the second smoothness control parameter, and (or) it is described first retrieval window size more than described second retrieval window
Size.
As shown in figure 4, step S2 may include following steps：In the step s 21, the image in the Noise determines
One object pixel (f, g) and the neighborhood of the object pixel；In step S22, centered on the object pixel (f, g)
First retrieval window in determine one other pixel (c, d) and the pixel neighborhood；In step S23, with the first smoothness control
Parameter processed calculates the difference between the neighborhood and the neighborhood of pixel (c, d) of the object pixel (f, g)；In step s 24, it is based on
The difference calculates weight of the pixel (c, d) relative to object pixel (f, g).
As shown in figure 5, step S4 may include following steps：In step S41, determine in the image of the Noise
One object pixel (r, s) and the neighborhood of the object pixel；In step S42, centered on the object pixel (r, s)
Second retrieval window in determine one other pixel (t, u) and the pixel neighborhood；In step S43, with the second smoothness control
Parameter processed calculates the difference between the neighborhood and the neighborhood of pixel (t, u) of the object pixel (r, s)；In step S44, it is based on
The difference calculates weight of the pixel (t, u) relative to object pixel (r, s).
In certain embodiments, seem more natural Noise texture to obtain, the figure after the noise reduction can be synthesized
As obtaining output image with the original image of Noise.
Methods described can improve the quality of the image for noise reduction algorithm, there is provided a more preferable noise reduction algorithm, efficiently
Noise reduction is carried out to image.The non-local average for the carrying out pre-filtering filter described in previous embodiment is compared in Fig. 6 (a) and 6 (b)
The output image that the output image that ripple method is obtained is obtained with the non-local mean filter method for not carrying out pre-filtering.Fig. 6 (a) shows
One be mixed with 60% with the image and 40% original graph obtained by the non-local mean filter method noise reduction for having carried out pre-filtering
As the output image for obtaining, Fig. 6 (b) show one be mixed with 60% with the non-local mean filter method for not carrying out pre-filtering
The output image that image and 40% original image obtained by noise reduction are obtained.With the non-local mean filter method for having carried out pre-filtering
The image that noise reduction is obtained reduces 49% noise compared with original image, remains more fine structures, and do not exist
Other artifacts.The image obtained with the non-local mean filter method noise reduction for not carrying out pre-filtering is reduced compared with original image
40% noise, but it is compared with the image of pre-filtering noise reduction has been carried out, and has some fine structures not to be retained clearly.
The present invention can be summarized with others without prejudice to the concrete form of spirit or essential characteristics of the invention.Therefore, nothing
By from the point of view of which point, the embodiment above of the invention can only all be considered the description of the invention and can not limit this hair
Bright, the scope of the present invention is to be defined by tbe claims, and is defined rather than by above-mentioned, therefore, will with right of the invention
Any change in book suitable implication and scope is asked, is all considered as being included within the scope of the claims.
Claims (18)
1. it is a kind of reduce image in noise method, it includes：
Receive the image of Noise；
The first weight is estimated from the image of the Noise with the first smoothness control parameter and the first retrieval window size；
Pre-filtering is carried out to the image of the Noise with first weight by non-local mean filter method, to obtain pre-flock
Image after ripple；
The second weight is estimated from the image after the pre-filtering with the second smoothness control parameter and the second retrieval window size；
And
The image of the Noise is filtered with second weight by non-local mean filter method, after obtaining noise reduction
Image,
Wherein meet at least one in following conditions：1) the first smoothness control parameter is higher than the second smoothness control
Parameter processed, and 2) the first retrieval window size retrieves window size more than described second.
2. the method for claim 1, wherein the first smoothness control parameter is not less than 1.
3. the method for claim 1, wherein the first retrieval window size of two dimension is more than 3*3, three-dimensional is described
First retrieval window size is more than 3*3*3.
4. the method for claim 1, further includes to close the image after the noise reduction with the image of the Noise
Into generation output image.
5. the method for claim 1, wherein the step of the first weight is estimated in the image from Noise includes：
An object pixel (f, g) and the neighborhood of the object pixel (f, g) are determined in the image of the Noise；
Determine in the first retrieval window centered on the object pixel (f, g) one other pixel (c, d) and the pixel (c,
D) neighborhood；
With between the neighborhood and the neighborhood of pixel (c, d) of the first smoothness control parameter calculating object pixel (f, g)
Difference；And
The weight of the relatively described object pixel (f, g) of pixel (c, d) is calculated based on the difference.
6. the method for claim 1, wherein the step of estimating the second weight in the image after pre-filtering includes：
An object pixel (r, s) and the neighborhood of the object pixel (r, s) are determined in image after the pre-filtering；
Determine in the second retrieval window centered on the object pixel (r, s) one other pixel (t, u) and the pixel (t,
U) neighborhood；
With between the neighborhood and the neighborhood of pixel (t, u) of the second smoothness control parameter calculating object pixel (r, s)
Difference；And
The weight of the relatively described object pixel (r, s) of pixel (t, u) is calculated based on the difference.
7. a kind of equipment for reducing the noise in image, including：
Machine readable medium or medium, are stored thereon with instruction；And
Processor, itself and the machine readable medium or medium couples, and be provided for：
Receive the image of Noise；
The first weight is estimated from the image of the Noise with the first smoothness control parameter and the first retrieval window size；
Pre-filtering is carried out to the image of the Noise with first weight by non-local mean filter method, to obtain pre-flock
Image after ripple；
The second weight is estimated from the image after the pre-filtering with the second smoothness control parameter and the second retrieval window size；
And
The image of the Noise is filtered with second weight by non-local mean filter method, after obtaining noise reduction
Image,
Wherein meet at least one in following conditions：1) the first smoothness control parameter is more than the second smoothness control
Parameter processed, and 2) the first retrieval window size retrieves window size more than described second.
8. equipment as claimed in claim 7, wherein the first smoothness control parameter is not less than 1.
9. equipment as claimed in claim 7, wherein the first retrieval window size of two dimension is more than 3*3, three-dimensional is described
First retrieval window size is more than 3*3*3.
10. equipment as claimed in claim 7, the instruction for storing thereon is for further indicating computer by after the noise reduction
Image and the synthetically produced output image of the image of the Noise.
11. equipment as claimed in claim 7, the instruction for storing thereon is used to refer to computer by following steps come from noisy
The first weight is estimated in the image of sound：
An object pixel (f, g) and the neighborhood of the object pixel (f, g) are determined in the image of the Noise；
Determine in the first retrieval window centered on the object pixel (f, g) one other pixel (c, d) and the pixel (c,
D) neighborhood；
With between the neighborhood and the neighborhood of pixel (c, d) of the first smoothness control parameter calculating object pixel (f, g)
Difference；And
The weight of the relatively described object pixel (f, g) of pixel (c, d) is calculated based on the difference.
12. equipment as claimed in claim 7, the instruction for storing thereon is used to refer to computer by following steps come from pre-flock
The second weight is estimated in image after ripple：
An object pixel (r, s) and the neighborhood of the object pixel (r, s) are determined in image after the pre-filtering；
Determine in the second retrieval window centered on the object pixel (r, s) one other pixel (t, u) and the pixel (t,
U) neighborhood；
With between the neighborhood and the neighborhood of pixel (t, u) of the second smoothness control parameter calculating object pixel (r, s)
Difference；And
The weight of the relatively described object pixel (r, s) of pixel (t, u) is calculated based on the difference.
A kind of 13. medical imaging apparatus, it includes：
Radiographic source and ray detector on rotary pallet are installed on, wherein the ray detector is used for receiving being penetrated by described
Line source passes through the ray of scanned object after sending；
Data collecting system, sets and is used for receiving the data from the ray detector when object is scanned, and calculated to one
Machine provides data for projection collection；
The display device of the image that the computer is obtained with the data for projection collection is shown by responding the computer；With
And
Processor, setting is used for：
Receive the image of Noise；
The first weight is estimated from the image of the Noise with the first smoothness control parameter and the first retrieval window size；
Pre-filtering is carried out to the image of the Noise with first weight by non-local mean filter method, to obtain pre-flock
Image after ripple；
The second weight is estimated from the image after the pre-filtering with the second smoothness control parameter and the second retrieval window size；
And
The image of the Noise is filtered with second weight by non-local mean filter method, after obtaining noise reduction
Image, wherein at least one in meeting following conditions：1) the first smoothness control parameter is smoothed more than described second
Degree control parameter, and 2) the first retrieval window size retrieves window size more than described second.
14. medical imaging apparatus as claimed in claim 13, wherein the first smoothness control parameter is not less than 1.
15. medical imaging apparatus as claimed in claim 13, wherein the first retrieval window size of two dimension is more than 3*3,
Three-dimensional the first retrieval window size is more than 3*3*3.
16. medical imaging apparatus as claimed in claim 13, it further sets and is used for the image after the noise reduction and institute
State the synthetically produced output image of image of Noise.
17. medical imaging apparatus as claimed in claim 13, it sets and is used for by following steps come from the image of Noise
The first weight of middle estimation：
An object pixel (f, g) and the neighborhood of the object pixel (f, g) are determined in the image of the Noise；
Determine in the first retrieval window centered on the object pixel (f, g) one other pixel (c, d) and the pixel (c,
D) neighborhood；
With between the neighborhood and the neighborhood of pixel (c, d) of the first smoothness control parameter calculating object pixel (f, g)
Difference；And
The weight of the relatively described object pixel (f, g) of pixel (c, d) is calculated based on the difference.
18. medical imaging apparatus as claimed in claim 13, it sets and is used for by following steps come the figure from after pre-filtering
The second weight is estimated as in：
An object pixel (r, s) and the neighborhood of the object pixel (r, s) are determined in image after the pre-filtering；
Determine in the second retrieval window centered on the object pixel (r, s) one other pixel (t, u) and the pixel (t,
U) neighborhood；
With between the neighborhood and the neighborhood of pixel (t, u) of the second smoothness control parameter calculating object pixel (r, s)
Difference；And
The weight of the relatively described object pixel (r, s) of pixel (t, u) is calculated based on the difference.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102376084A (en) * | 2010-08-12 | 2012-03-14 | 西门子公司 | Iterative image filtering with anisotropic noise model for a CT image |
| CN102622731A (en) * | 2012-03-11 | 2012-08-01 | 西安电子科技大学 | Contourlet domain Wiener filtering image denoising method based on two-dimensional Otsu |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2003009227A2 (en) * | 2001-07-19 | 2003-01-30 | Koninklijke Philips Electronics N.V. | Method of reducing noise in volume imaging |
-
2012
- 2012-12-14 CN CN201210544602.4A patent/CN103870236B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102376084A (en) * | 2010-08-12 | 2012-03-14 | 西门子公司 | Iterative image filtering with anisotropic noise model for a CT image |
| CN102622731A (en) * | 2012-03-11 | 2012-08-01 | 西安电子科技大学 | Contourlet domain Wiener filtering image denoising method based on two-dimensional Otsu |
Non-Patent Citations (1)
| Title |
|---|
| 基于非局部信息的信号与图像处理算法及其应用研究;孙伟峰;《中国博士学位论文全文数据库》;20101015;正文第3.3.2节、第4.3、4.4节 * |
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| CN103870236A (en) | 2014-06-18 |
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