CN109461129A - A kind of image enchancing method based on controlled diffusion - Google Patents
A kind of image enchancing method based on controlled diffusion Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000009792 diffusion process Methods 0.000 title claims abstract description 28
- 238000001914 filtration Methods 0.000 claims description 11
- 238000012545 processing Methods 0.000 claims description 11
- 238000009499 grossing Methods 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 9
- 238000005259 measurement Methods 0.000 claims description 5
- 230000002708 enhancing effect Effects 0.000 abstract description 17
- 239000000463 material Substances 0.000 abstract description 4
- 230000009466 transformation Effects 0.000 abstract description 2
- 230000004256 retinal image Effects 0.000 description 16
- 238000004422 calculation algorithm Methods 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 7
- 230000003321 amplification Effects 0.000 description 5
- 238000003199 nucleic acid amplification method Methods 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 4
- 238000003707 image sharpening Methods 0.000 description 4
- 230000002207 retinal effect Effects 0.000 description 4
- 238000003745 diagnosis Methods 0.000 description 3
- 201000010099 disease Diseases 0.000 description 3
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 3
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- G06T5/70—
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- G06T5/80—
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20024—Filtering details
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- 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
- G06T2207/30004—Biomedical image processing
- G06T2207/30041—Eye; Retina; Ophthalmic
Abstract
The present invention relates to a kind of image enchancing methods based on controlled diffusion.This method first, enhances into a grey level range appropriate ophthalmoscopic image application Gamma transformation；Secondly, the area-of-interest for needing to extract in denoising, especially image is carried out to transformed image using self-similarity filter.Finally, it is further sharpened using the controlled diffusion equation method of proposition, the generation of artificial artifact caused by effectively avoiding excessively enhancing while its prominent material particular.
Description
Technical field
The present invention relates to a kind of image enchancing methods based on controlled diffusion, belong to the technical field of image procossing.
Background technique
Retinal image is the efficient diagnosis foundation of many ophthalmology diseases and other diseases, the enhancing for its eye fundus image
It handles significant in clinical medicine domain.Retinal image can be poor due to exposing uneven and vascular contrast in many cases
Etc. reasons and obscure, influence doctor to the acquisition of information and the diagnosis of disease, it is very unfavorable for patient.Therefore, improve view
The visual effect of film image, as far as possible retain original image main feature under the premise of enhance quality, convenient for obtain important information with
Conducive to diagnosis medically.Meanwhile for the subsequent processing of retinal image, equally it is necessary to carry out retinal image enhancing
Research and application.
There is between each grey scale pixel value high correlation in digital picture, and they are mainly by complicated edge and texture
Details reflects.Image sharpening enhancing is handled mainly for these edge details so that the detail textures area in image
Domain comparison is more clear.But there are some intrinsic difficult points for the processing of the sharpening enhancement of retinal image.
Although having had already appeared the method for retinal image image enhancement in the prior art, can not effectively realize
To ophthalmoscopic image sharpening enhancement.Chinese patent, publication number CN104036521A propose a kind of retinal blood managed network segmentation calculation
Method.The novel point of the algorithm is to combine multiple dimensioned linearity test and be calculated most using the Gray Level-Gradient Co-occurrence Matrix of image
The method of good entropy threshold.The present invention is suitable for the segmentation of normal and lesion retinal fundus images.However, for by noise dirt
Dye and ambiguous retinal image data, it is very difficult to judge its tangential and normal orientation.Especially for some small details,
It is difficult to define and estimate their direction.
Chinese patent, publication number CN104077754A disclose the retinal vessel filtering reinforcement method based on symmetry.
The present invention can enhance retinal vasculature, while can effectively inhibit the non-vascular structure such as optic disk, to retinal map
As blood vessel segmentation, retinal images analysis and retinal images registration in have important aid in treatment and practical application valence
Value.However in terms of the minute lesion of enhancing eye ground image, since the gradient value at this is very small, to image ladder
It is inappropriate for spending the shock calculation method enhancing that amplitude is speed.
Chinese patent, publication number CN106683080A disclose a kind of retinal fundus images preprocess method；This method
The tangential smothing filtering of Gauss in impact filtering calculating taken is easy the process in removal noise and smooth Second order directional
It is middle to eliminate important detailed information.In addition, unnatural artificial artifact phenomenon (also known as overshoot phenomenon) also can be in sharpening enhancement
It is generated in the process in image border, causes the pollution for visual observation.
Greyscale transformation is carried out using Gamma converter technique and belongs to the prior art；For example, " Lee's Bohai Sea, Zhu Mei, Fan Zhongkui wait
Inhomogeneous illumination image adaptive Gamma enhances algorithm [J] University Of Nanchang journal (natural sciences version), 2016,40 (3): 299-
302.2 " and " Zhou Fei, Jia Zhenhong, Yang Jie, wait based on shearing wave zone improve Gamma correction medical image enhancement algorithm [J]
Photoelectron: laser, 2017 (5): 566-572. " discloses relevant technology.
Carrying out denoising to the transformed image of Gamma using self-similarity filter is that field of image processing is common
Image de-noising method, such as " Buades A, Coll B, Morel J M.A Non-Local Algorithm for Image
Denoising. Computer Vision and Pattern Recognition,2005.CVPR 2005.IEEE
Computer Society Conference on.IEEE, 2005:60-65vol.2. " disclose the relevant technologies.
" image denoising model [J] Xi'an electronics technology of Li Min, the Feng Xiangchu based on total variation and wavelet method is big for document
Learn journal (natural science edition), 2006,33 (6): 980-984.5. ", " Wu Yadong, Sun Shixin, Zhang Hongying wait one kind to be based on figure
Total Variation Image Denoising algorithm [J] the electronic letters, vol cut, 2007,35 (2): 265-268. ", " Liu Yuxi, Xiu Xiaoyu, Zhou Guo
Brightness studies [J] computer application and software, 014 (4): 235- based on the SD-OCT imaging algorithm of non-uniform sampling data
238. " and document " Osher S, Sethian JA.Fronts propagating with curvature-dependent
speed: algorithms based on hamilton-jacobi formulations.Journal of
Computational Physics. 1988；79 (1): it is individually disclosed in 12-49. " using total variation reduction technology (TVD)
The relevant technologies of controlled diffusion equation are solved with nonlinear limiter policy control.
Summary of the invention
In view of the deficiencies of the prior art, the present invention provides a kind of image enchancing method based on controlled diffusion.
Summary of the invention:
The present invention proposes a kind of image enchancing method based on controlled diffusion.First, ophthalmoscopic image application Gamma is converted
Enhance into a grey level range appropriate；Secondly, transformed image is carried out at denoising using self-similarity filter
Reason, the area-of-interest for especially needing to extract in image.Finally, it is carried out using the controlled diffusion equation method of proposition further
Sharpening, the generation of artificial artifact caused by effectively avoiding excessively enhancing while its prominent material particular.
The technical solution of the present invention is as follows:
A kind of image enchancing method based on controlled diffusion, comprises the following steps that
1) it is converted in the tonal range that image is transformed to 0~255 by Gamma；
2) the transformed image of Gamma is filtered using self-similarity filter；
Eye ground image is inevitably present noise pollution, in order to remove noise jamming and reduce image sharpening
For the amplification of noise during enhancing, need to carry out retinal image the smoothing processing for keeping characteristics of image；Therefore, it adopts
Similitude filtering is derived to handle image；
Gray value after filtering processing at point p are as follows:
Wherein, u indicates that image to be processed, Ω are the domain of image, uijIndicate ash of the image u at the i-th row jth column
Angle value；It enables point p=(i, j), then upIndicate the gray value at point p；ω (p, q) is measurement p, and two pixel similitudes of q add
Weight average function, is defined as:
ω (p, q)=exp-| | Np-Nq||2/h2}
Wherein, Np,NqRespectively indicate point p, q etc. sizes neighborhood, | | | | for the Gauss weighting between two image blocks
Distance；H is smoothing factor；Smoothing factor is used to control the smoothing capability of exponential function in similarity measurement；Calculation formula
In denominator be a normalization factor.
3) the further sharpening image of controlled diffusion equation is taken；Controlled diffusion equation are as follows:
Wherein, Δ indicates Laplace operator；For further sharpening image, using the method for non-linear back-diffusion equation,
And in order to avoid excessively enhancing the artificial artifact generated while prominent image material particular, take controlled diffusion equation
Method:
There is big fluctuation in numerical solution in order to prevent, asks in conjunction with TVD and using nonlinear limiter governing equation (1) numerical value
One nonlinear limiter M is acted on gradient terms by the variation solved in solution preocessOn, avoid the explosive of image gradient from increasing
It is long；
Using the Laplace operator in centered difference discretization equation (1)；The approximate solution of iteration kth stepAnd back
Approximate solutionMeet relationship:
Wherein, Δ t is time step；
For the gradient terms in equation (1), limiter M is defined first are as follows:
Wherein, λ > 0 is constant；By lambda definition to be greater than the purpose of 0 constant be guarantee it is small important thin in retinal images
Section also can effectively be enhanced in the case where gradient magnitude very little.
Then, gradient terms are approached using following formula:
Wherein,The forwardly and rearwardly difference operator in the direction x is respectively indicated,Respectively indicate the direction y forward
And backward difference operator；The numerical discretization process of formula (1) is the Laplace operator enhancing process limited by iterative steps.
It is preferred according to the present invention, shown step 2)Calculating process in, q is limited in a neighborhood of p and is carried out.
Q is limited in a neighborhood of p and carries out that the operation efficiency of self similarity filtering can be improved in order to reduce computation burden.
It is preferred according to the present invention, λ=0.1 in shown step 3).
The invention has the benefit that
1. the image enchancing method of the present invention based on controlled diffusion eliminates image by self similarity filtering method
Noise avoids noise amplification phenomenon common in image enchancing method, realizes for noise and fuzzy retinal images number
According to the effect for carrying out robust processing；The experimental results showed that (see Fig. 1-4), which is capable of the important of effectively strengthens view data
Details avoids excessively enhancing and noise amplification, brings help for medical image interpretation and subsequent processing；
2. the image enchancing method of the present invention based on controlled diffusion, using based on total variation reduce (TVD) technology and
The resolution policy of nonlinear limiter prevents numerical fluctuations problem；It is controlled using the restricted function in flux correction technology
The variation of the numerical solution of nonlinear equation, the inherent defect for overcoming Laplace operator to enhance, effectively eliminates overshoot phenomenon.
Detailed description of the invention
Fig. 1 is the original image in embodiment 1；
Fig. 2 is in embodiment 1 using the partial enlargement image of image enhancement result after Gamma variation；
Fig. 3 is the partial enlargement image that self similarity filtered image enhances result in embodiment 1；
Fig. 4 is the partial enlargement image of image enhancement result after controlled diffusion equation Enhancement Method in embodiment 1；
Fig. 5 is the explanatory diagram of nonlinear limiter M of the present invention；Figure explanation, if piece image is in point XiAsh
Angle value adjacent two o'clock X on the direction xi-1,Xi+1Gray value, then limiter M is in point XiValue non-zero shows point XiThe ash at place
Angle value has the space of increase；
Fig. 6 is the case where value of limiter M is zero；The figure illustrates when the gray value of intermediate point is adjacent higher than the direction x
When two o'clock, the gray value of intermediate point has been not necessarily to change, and shows that the brightness at edge is more prominent, sharpens purpose and tentatively reach；
Fig. 7 is the image enchancing method flow chart of the present invention based on controlled diffusion.
Specific embodiment
Below with reference to embodiment and Figure of description, the present invention will be further described, but not limited to this.
Embodiment 1
As shown in Figure 7.
It is a kind of that enhancing (original image such as Fig. 1 is sharpened to retinal images based on the image enchancing method of controlled diffusion
It is shown), it comprises the following steps that
1) it is converted in the tonal range that image is transformed to 0~255 by Gamma；
2) the transformed image of Gamma is filtered using self-similarity filter；
Eye ground image is inevitably present noise pollution, in order to remove noise jamming and reduce image sharpening
For the amplification of noise during enhancing, need to carry out retinal image the smoothing processing for keeping characteristics of image；Therefore, it adopts
Similitude filtering is derived to handle image；
Gray value after filtering processing at point p are as follows:
Wherein, u indicates that image to be processed, Ω are the domain of image, uijIndicate ash of the image u at the i-th row jth column
Angle value；It enables point p=(i, j), then upIndicate the gray value at point p；ω (p, q) is measurement p, and two pixel similitudes of q add
Weight average function, is defined as:
ω (p, q)=exp-| | Np-Nq||2/h2}
Wherein, Np,NqRespectively indicate point p, q etc. sizes neighborhood, | | | | for the Gauss weighting between two image blocks
Distance；H is smoothing factor；Smoothing factor is used to control the smoothing capability of exponential function in similarity measurement；Calculation formula
In denominator be a normalization factor.
3) the further sharpening image of controlled diffusion equation is taken；Controlled diffusion equation are as follows:
Wherein, Δ indicates Laplace operator；For further sharpening image, using the method for non-linear back-diffusion equation,
And in order to avoid excessively enhancing the artificial artifact generated while prominent image material particular, take controlled diffusion equation
Method:
There is big fluctuation in numerical solution in order to prevent, asks in conjunction with TVD and using nonlinear limiter governing equation (1) numerical value
One nonlinear limiter M is acted on gradient terms by the variation solved in solution preocessOn, avoid the explosive of image gradient from increasing
It is long；
Using the Laplace operator in centered difference discretization equation (1)；The approximate solution of iteration kth stepAnd back
Approximate solutionMeet relationship:
Wherein, Δ t is time step；
For the gradient terms in equation (1), limiter M is defined first are as follows:
Wherein, λ > 0 is constant；By lambda definition to be greater than the purpose of 0 constant be guarantee it is small important thin in retinal images
Section also can effectively be enhanced in the case where gradient magnitude very little.The present embodiment takes λ=0.1 to have extraordinary effect.
Then, gradient terms are approached using following formula:
Wherein,The forwardly and rearwardly difference operator in the direction x is respectively indicated,Respectively indicate the direction y forward
And backward difference operator；The numerical discretization process of formula (1) is the Laplace operator enhancing process limited by iterative steps.
Processing result image is as in Figure 2-4；The experimental results showed that the present embodiment effectively strengthens view data is important
Details avoids excessively enhancing and noise amplification.
Embodiment 2
The method that enhancing is sharpened to retinal images as described in Example 1, further, shown step 2)'s
In calculating process, q is limited in a neighborhood of p and is carried out.Q is limited in carry out in a neighborhood of p can be in order to reduce
Computation burden improves the operation efficiency of self similarity filtering.
Claims (3)
1. a kind of image enchancing method based on controlled diffusion, which is characterized in that comprise the following steps that
1) it is converted in the tonal range that image is transformed to 0~255 by Gamma；
2) the transformed image of Gamma is filtered using self-similarity filter；
Gray value after filtering processing at point p are as follows:
Wherein, u indicates that image to be processed, Ω are the domain of image, uijIndicate gray scale of the image u at the i-th row jth column
Value；It enables point p=(i, j), then upIndicate the gray value at point p；ω (p, q) is measurement p, the weighting of two pixel similitudes of q
Average function, is defined as:
ω (p, q)=exp-| | Np-Nq||2/h2}
Wherein, Np, NqRespectively indicate point p, q etc. sizes neighborhood, | | | | for the Gauss weighting between two image blocks away from
From；H is smoothing factor；
3) the further sharpening image of controlled diffusion equation is taken；Controlled diffusion equation are as follows:
Wherein, Δ indicates Laplace operator；
Using the Laplace operator in centered difference discretization equation (1)；The approximate solution of iteration kth stepIt is close with back
Like solutionMeet relationship:
Wherein, Δ t is time step；
For the gradient terms in equation (1), limiter M is defined first are as follows:
Wherein, λ > 0 is constant；
Then, gradient terms are approached using following formula:
Wherein,The forwardly and rearwardly difference operator in the direction x is respectively indicated,Respectively indicate the direction y forward and to
Difference operator afterwards.
2. the image enchancing method according to claim 1 based on controlled diffusion, which is characterized in that shown step 2)'s
In calculating process, q is limited in a neighborhood of p and is carried out.
3. the image enchancing method according to claim 1 based on controlled diffusion, which is characterized in that λ in shown step 3)
=0.1.
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Application publication date: 20190312 |