CN103473745B - A kind of low-dose CT image processing method based on distinctiveness dictionary - Google Patents

Abstract

The invention discloses a kind of low-dose CT image processing method based on distinctiveness dictionary, first by stationary wavelet transform, pending low-dose CT image is carried out monolayer decomposition, then level, vertical and diagonally opposed high frequency detail image are carried out corresponding distinctiveness dictionary respectively and expresses and go artifact, thus reach to suppress star strip artifact information strength in the high frequency detail image of different directions；Carry out inverse stationary wavelet transform according to level, vertical and diagonally opposed high frequency detail image and the original low-frequency image after processing afterwards and rebuild the CT image after obtaining artifact suppression；Finally utilize existing tradition sparse expression method based on dictionary learning that image is further processed, to remove artifact and the noise of residual.The present invention can effectively suppress the star strip artifact in low-dose CT image and noise, improves low-dose CT picture quality so that it is meet the prescription of clinical diagnosis.

Description

A kind of low-dose CT image processing method based on distinctiveness dictionary

Technical field

The present invention relates to a kind of low-dose CT image processing method, particularly relate to a kind of based on distinctiveness dictionary Low-dose CT image processing method, belongs to computerized tomograph technical field.

Background technology

As a kind of conventional effective clinical diagnose instrument, X ray computer fault imaging (X-ray Computerized Tomography, CT) can obtain clearly on mm-scale human body different tissues for The dampening information of X-ray, thus the diagnosis and prevention for clinician provides the 3 D human body organ group enriched Knit information.Today, as a kind of maturation and the most universally recognized inspection method, CT has become as One of indispensable main tool in radiodiagnosis field.But, along with CT tomoscan is in clinical diagnosis Especially popularizing in routine examination, the radiation dose problem in CT scan has caused people and has got more and more Concern, substantial amounts of clinical research shows that the CT radiation dose exceeding normal range easily induces human metabolism The disease such as exception or even cancer.But, in current CT equipment, often through regulation tube current and voltage To reduce CT scan dosage, so will increase the block distortion rebuilding image and have directive star strip puppet Shadow, reduces the quality that CT rebuilds, thus affects clinician's diagnosis rate to abnormal structure.

The current method improving low-dose CT picture quality is broadly divided into based on projection space data and based on figure The big class of the two of image space data, method based on projection space data is mainly gone by the recovery of CT data for projection Making an uproar provides noise less data for projection for rebuilding, to improve the image reconstruction quality under the conditions of low dosage, example Such as the filtered back projection (Filtered Back projection, FBP) currently commonly used in Clinical CT is rebuild Algorithm, it is simply that processed by built-in frequency domain filtering and suppress artifact and noise, also has some scholars to propose throwing Shadow space is set up data model and sets up recovery algorithms based on this and suppress under the conditions of low dosage in CT data for projection Noise.The research of this type of method is limited owing to cannot be obtained by commercial CT data for projection, difficult in reality To be used widely.Another kind of method is directly to be improved low dose rebuild by image space treatment technology Amount picture quality, has and is independent of Raw projection data and the fast advantage of processing speed, generally use nonlinear Processing method carries out keeping the denoising of image edge information, such as anisotropic filter (nonlinear or Anisotropic filter) or the method for small echo (wavelet), but this quasi-nonlinear side is based primarily upon image Local message, it is difficult to obtain good treatment effect, such as, it is impossible to effectively in suppression low-dose CT image Block distortion and star strip artifact, also can produce new artifact in processes.

Rarefaction representation based on dictionary learning (the Sparse and Redundant Representations being recently proposed Over Learnd Dictionaries) Image denoising algorithm belongs to Equations of The Second Kind method.This method is first by target image Split into the least segment, then each segment encoded, make its with in a complete dictionary of mistake very Few several pieces are indicated by linear combination.In this course, by controlling parameter, can make normal Structure is represented, and artifact noise can not get preferably representing, thus reaches denoising purpose.Finally by these Segment superposed average again, increases denoising effect further.It is the most complete that this method first looks for the optimal overall situation Dictionary, and each artwork block is expressed as in dictionary the linear combination of vector (atom).Linear combination is Number can be calculated by sparse coding process.Sparse representation method target based on dictionary learning is as follows for solving Problem:

$\begin{array}{cccc}\underset{x,D,\mathrm{\α}}{min}{||x-y||}_2^2+\mathrm{\μ}\underset{ij}{\Σ}{||R_{ij}x-{\mathrm{D\α}}_{ij}||}_2^2& s.t.& {||{\mathrm{\α}}_{ij}||}_0\≤T& \∀i,j\end{array}---\left(1\right)$

Wherein x and y represents the pending image of m pixel and original low-dose CT image respectively；Subscript ij indicates Pixel index in image (i, j)；R_ijRepresent that extracting size from image x is that (center is at the segment of (i, j)) for n × n x_ijOperator；Dictionary D is the matrix of a n × K, is made up of K n-dimensional vector atom (column vector). Corresponding n × n the segment of each n dimensional vector；α represents the coefficient sets { α of the rarefaction representation of all pieces_ij}_ij, Each segment x_ijCan be by linear combination D α_ijCarry out approximate representation；||α_ij||₀Represent l⁰Norm, is used for calculating Vector α_ijIn nonzero term number；T is default sparse extent index, is used for limiting α_ijThe number of middle nonzero term. The problem in (1) that solves comprises following (2) and (3) two subproblems:

$\begin{array}{cccc}\underset{D,\mathrm{\α}}{min}\mathrm{\μ}\underset{ij}{\Σ}{||R_{ij}x-{\mathrm{D\α}}_{ij}||}_2^2& s.t.& {||{\mathrm{\α}}_{ij}||}_0\≤T& \∀i,j\end{array}---\left(2\right)$

$\underset{x}{min}{||x-y||}_2^2+\mathrm{\μ}\underset{ij}{\Σ}{||R_{ij}x-{\mathrm{D\α}}_{ij}||}_2^2---\left(3\right)$

Wherein, the purpose of (2) is to train sparse coefficient α and dictionary D from a series of segments, and this problem can be by X known image y is replaced, and utilizes K average singular value decomposition (K-SVD) to solve.Originate at the beginning of one Beginning dictionary (such as DCT dictionary), K-SVD method estimates α and dictionary D by alternately two steps: Sparse coding step based on orthogonal matched transform algorithm (OMP) and the dictionary updating step decomposed based on SVD. Afterwards by obtain dictionary D and factor alpha, by the first derivative asking (3) obtain export image x:

$x={(I+\mathrm{\μ}\underset{ij}{\Σ}{R}_{ij}^T{R}_{ij})}^{-1}(y+\mathrm{\μ}\underset{ij}{\Σ}{R}_{ij}^T{\mathrm{D\α}}_{ij})---\left(4\right)$

Wherein I is unit matrix, and μ is weight coefficient.

Sparse representation method based on dictionary learning has been proved to its process in low dosage abdominal CT images Effect, the abdominal CT images under conditions of tube current is reduced to original 1/5th still can obtain in processing Obtain preferable image recovery effects, it is contemplated that dosage and the linear relationship of tube current, based on dictionary learning sparse Method for expressing can make patient suffered decrease in dose to original 1/5th in abdomen scanning.But this kind Method has certain limitation, easily the star strip artifact in CT image under the conditions of low dosage as image In information, thus cannot effectively suppress in CT image the star strip easily occurred under the conditions of low-dose scanning Artifact, the generation of these star strip artifacts is owing to the x-ray projection of some angle is had bigger by tissue Decay, be generally present in the CT scan image at the more position of high density (such as skeleton), as shoulder, The position such as chest and vertebra.Under the conditions of the low dosage that tube current or voltage reduce, due to wearing of X-ray Property and the decline of energy thoroughly, star strip artifact will be more serious.

Summary of the invention

Goal of the invention: the technical problem to be solved is to overcome existing low-dose CT image processing method The problem of the star strip artifact that can not effectively suppress under the conditions of low dosage in CT image existed, it is provided that Yi Zhongneng Enough low-dose CT image processing methods effectively suppressing star strip artifact, referred to as artifact suppression dictionary learning method (Artifact Suppressed Dictionary Learning,ASDL).The present invention is based on dictionary learning sparse Improved on method for expressing, it is proposed that the concept of distinctiveness dictionary.Distinctiveness dictionary comprises artifact atom With characteristic atomic, artifact composition major part when low dosage image being expressed with distinctiveness dictionary, in image Expressed by artifact atom, the then major part of the normal organization composition in image is by expressed by characteristic atomic. In image after expressing afterwards, the part of artifact expressed atom is artificially removed, and reaches to eliminate image artifacts Purpose.

Technical scheme: a kind of low-dose CT image processing method based on distinctiveness dictionary, comprises the following steps:

Step 1, use stationary wavelet transform are to some low dosages and high dose CT image f^TDo the decomposition of monolayer, Respectively obtain the level after decomposition, vertical and diagonally opposed high frequency detail imageAnd it is low Frequently imageHigh frequency detail image to the low dosage in training set image library and high dose the most respectively Carry out dictionary learning, obtain three distinctiveness dictionariesWith(this step only need to be carried out Once, the dictionary trained can remove Reusability in artifact afterwards)；

If pending low-dose CT image f is done the decomposition of monolayer by step 2, use stationary wavelet transform, Respectively obtain the level after decomposition, vertical and diagonally opposed high frequency detail image f_chd、f_cvd、f_cddAnd it is low Frequently image f_ca, to level, vertical and diagonally opposed high frequency detail image f_chd、f_cvd、f_cddRespectively with right The distinctiveness dictionary answeredWithIt is indicated, afterwards by artifact atom in correspondence distinctiveness dictionary The part expressed is removed, the level after being processed, vertical and diagonally opposed high frequency detail image (this step can carry out twice to strengthen going artifact effect)；

Step 3, the level to after processing, vertical and diagonally opposed high frequency detail imageWith And original low-frequency image f_caCarry out inverse stationary wavelet transform to rebuild, obtain the CT image of artifact suppression

Further, technique scheme can be tied with existing tradition sparse expression method based on dictionary learning Close, thus improve the treatment effect of low-dose CT image, particularly as be in technique scheme step 3 it Rear increase step 4, the image that step 3 is obtainedUtilize existing tradition sparse expression based on dictionary learning Image is further processed by method, to remove artifact and the noise of residual.

Beneficial effect: compared with prior art, the inventive method first by stationary wavelet transform to pending Low-dose CT image carries out monolayer decomposition, then divides level, vertical and diagonally opposed high frequency detail image It is not indicated with corresponding distinctiveness dictionary, and represents to partially remove by artifact atom and suppress star strip artifact Information strength in the high frequency detail image of different directions, then according to the level after processing, vertical and diagonal angle The high frequency detail image in direction and original low-frequency image carry out rebuilding against stationary wavelet transform obtaining artifact The CT image being inhibited, then utilizes existing sparse representation method based on dictionary learning to carry out image Process further.The inventive method can effectively suppress the star strip artifact in low-dose CT image and noise, Improve low-dose CT picture quality so that it is meet the prescription of clinical diagnosis.

Accompanying drawing explanation

Fig. 1 is low-dose CT image in the embodiment of the present invention；

Fig. 2 is high dose CT image in the embodiment of the present invention；

Fig. 3 is the low-dose CT image using tradition dictionary method to process；

Fig. 4 is the low-dose CT image that the inventive method ASDL processes.

It is specially embodiment

Below in conjunction with specific embodiment, it is further elucidated with the present invention, it should be understood that these embodiments are merely to illustrate this Invention rather than restriction the scope of the present invention, after having read the present invention, those skilled in the art are to this The amendment of the bright various equivalent form of values all falls within the application claims limited range.

ASDL low-dose CT image processing method, comprises the following steps:

Step 1, use stationary wavelet transform are to some low dosages and high dose CT image f^TDo the decomposition of monolayer, Respectively obtain the level after decomposition, vertical and diagonally opposed high frequency detail imageAnd it is low Frequently imageHigh frequency detail image to low dosage and high dose the most respectivelyCarry out dictionary Study, obtains three distinctiveness dictionariesWith(this step only need to be carried out once, trains Dictionary can remove Reusability in artifact afterwards)；

Concrete, use the high and low frequency wave filter of Haar small echo, along low-dose CT image f^TLLevel Carry out high and low frequency filtering with vertical direction respectively, the image obtained taken the zone line of original image size, Obtain the high frequency detail image of horizontal directionAlong image f^TLDo the most respectively high frequency and Low frequency filtering, takes the zone line of original image size to the image obtained, and obtains the high frequency detail figure of vertical direction PictureAlong image f^TLDo High frequency filter the most respectively, the image obtained is taken original image The zone line of size, obtains diagonally opposed high frequency detail imageArtificial extractionIn The artifact segment of flat site, and utilize K-SVD algorithm to carry out dictionary training, obtain corresponding to level, hang down Straight and three artifact dictionaries of diagonal detailWithTo high-dose images f^THCarry out small echo equally Decompose, obtain level, vertical and diagonal detail imageDirectly it is carried out K-SVD Dictionary training, obtains corresponding to level, vertical and three characteristics dictionaries of diagonal detailWith Two kinds of dictionaries are merged, Obtain level, vertical and three distinctiveness dictionaries of diagonal detailWith

If pending low-dose CT image f is done the decomposition of monolayer by step 2, use stationary wavelet transform, Respectively obtain the level after decomposition, vertical and diagonally opposed high frequency detail image f_chd、f_cvd、f_cddAnd it is low Frequently image f_ca, to level, vertical and diagonally opposed high frequency detail image f_chd、f_cvd、f_cddRespectively with right The distinctiveness dictionary answeredWithIt is indicated, afterwards by artifact atom table in correspondence distinctiveness dictionary The part reached is removed, the level after being processed, vertical and diagonally opposed high frequency detail image (this step can carry out twice to strengthen going artifact effect)；

Concrete, for level detail image f_chd, substitute into the f of following (1) formula,

$\begin{array}{cccc}\underset{\mathrm{\α}}{\min }\underset{ij}{\Σ}{||{\mathrm{\α}}_{ij}||}_0& s.t.& {||R_{ij}f-{\mathrm{D\α}}_{ij}||}_2^2\≤\mathrm{\ϵ}& \∀i,j\end{array}---\left(1\right)$

By dictionaryBring the D in (1) formula into, utilize OMP algorithm to ask (1) formula to obtain sparse coefficient α, afterwards The coefficient of the artifact atom of M row before distinctiveness dictionary corresponding in sparse coefficient is set to zero, will in α before M Row is set to zero, the factor alpha after being processed^p, then by α^pWith f_chdFor following enter α with f in (2) formula obtain To the level detail image going pseudo-movie queen

$\tilde{f}={(I+\mathrm{\μ}\underset{ij}{\Σ}{R}_{ij}^T{R}_{ij})}^{-1}(f+\mathrm{\μ}\underset{ij}{\Σ}{R}_{ij}^T{\mathrm{D\α}}_{ij})---\left(2\right)$

In like manner, to f_cvdAnd f_cddUtilizeWithTake identical step, obtain the vertical detail figure of pseudo-movie queen PictureWith diagonal detail image

Step 3, the level to after processing, vertical and diagonally opposed high frequency detail image And original low-frequency image f_caCarry out inverse stationary wavelet transform to rebuild, obtain the CT image of artifact suppression

Rebuilding for inverse stationary wavelet, concrete is calculated as: the high frequency of same use above Haar small echo and Low-frequency filter, first to untreated wavelet space low-frequency image component f above_caAlong horizontal and vertical side To carrying out low frequency respectively and low frequency filtering obtainsWavelet space horizontal direction high frequency after step 2 is processed Detail pictures componentObtain along carrying out high and low frequency filtering the most respectivelyTo step 2 Wavelet space vertical direction high frequency detail picture content after processAlong carrying out the most respectively High and low frequency filtering obtainsWavelet space diagonally opposed high frequency detail image after processing step 2 divides AmountSame edge carries out high and low frequency filtering the most respectively and obtainsFinally will obtain 'sWithIt is added, the image obtained is taken the zone line of original image size, it is possible to Obtain inverse stationary wavelet to rebuild, the image that i.e. artifact is inhibited

Step 4, the image that step 3 is obtainedCarry out tradition sparse expression method based on dictionary learning to press down Noise processed；

Concrete, definitionWithRespectively represent preceding step three obtain artifact suppression low-dose CT figure and Final image after the inventive method process, computing formula is as follows:

$\begin{array}{cccc}\underset{D,\mathrm{\α}}{min}\mathrm{\μ}\underset{ij}{\Σ}{||R_{ij}x-{\mathrm{D\α}}_{ij}||}_2^2& s.t.& {||{\mathrm{\α}}_{ij}||}_0\≤T& \∀i,j\end{array}---\left(2\right)$

$\hat{f}={(I+\mathrm{\μ}\underset{ij}{\Σ}{R}_{ij}^T{R}_{ij})}^{-1}(\tilde{f}+\mathrm{\μ}\underset{ij}{\Σ}{R}_{ij}^T{\mathrm{D\α}}_{ij})---\left(4\right)$

Wherein (3) formula can be solved by K-SVD algorithm and obtain dictionary D and sparse coefficient α, substitutes in (4) Final result image can be tried to achieve

5. recruitment evaluation criterion

First obtain the low-dose CT image (Fig. 1) at same position, high dose CT image (Fig. 2), use Tradition dictionary method (Dictionary Learning, DL) the low-dose CT image (Fig. 3) that processes and with The low-dose CT image (Fig. 4) that inventive method ASDL processes.Used in experiment, CT equipment is one 16 row CT (Somatom Sensation 16), the condition of scanning is 120kVp and 5mm thickness, uses FBP Method is rebuild, and other parameters use machine default value, and the CT image of high dose and the CT image of low dosage divide Do not obtain by tube current parameter is set to 270mA and 70mA.

5.1 visual assessment

By observing the low dosage of Fig. 1 to Fig. 4 and the CT image of high dose, and DL method and the present invention The low-dose CT image that method processes, it can be seen that DL method cannot effectively press down while suppression noise Star strip artifact processed, and the picture quality after using the inventive method to process significantly improves, star strip artifact and making an uproar Sound has all obtained effective suppression.

5.2 quantitative evaluation

The effectiveness of the checking the inventive method in order to quantify, the intensity profile that we calculate certain selected is homogeneous Standard deviation in background area (region shown in red frame in Fig. 1 to Fig. 4), the definition of standard deviation here is:

$STD=\sqrt{\frac{1}{np-1}{\Σ}_{j=1}^{np}{(x_j-\stackrel{\‾}{x})}^2}---\left(10\right)$

Number of pixels in wherein np represents selection area, x_jWithRepresent the single pixel in this selection area respectively Point CT value and mean CT-number (Housfield units, HU), from table 1 below it can be seen that processing method can be big The standard deviation of amplitude reduction original low-dose CT image, it is thus achieved that close to normal dosage CT image in selected homogeneous district The standard deviation in territory.

Table 1

From above-mentioned experiment it will be seen that use the inventive method can effectively suppress the star low-dose CT image Strip artifact and noise, obtain the CT image close to high dose level under the conditions of low dosage, meet clinic The prescription of diagnosis.And the inventive method cannot not obtained by commercial CT data for projection and be limited, and has The bigger scope of application.

Claims (3)

1. a low-dose CT image processing method based on distinctiveness dictionary, it is characterised in that include with Lower step:

Pending low-dose CT image f is done the decomposition of monolayer by step 1, use stationary wavelet transform, point Level after not decomposed, vertical and diagonally opposed high frequency detail image f_chd、f_cvd、f_cddAnd low frequency Image f_ca；High frequency detail image to the low dosage in training set image library and high dose the most respectively Carry out dictionary learning, obtain three distinctiveness dictionariesWithConcrete, use The high and low frequency wave filter of Haar small echo, along low-dose CT image f^TLEnter the most respectively Row high and low frequency filters, and the image obtained takes the zone line of original image size, obtains the height of horizontal direction Frequently detail picturesAlong image f^TLDo the most respectively high and low frequency filtering, to obtaining Image take the zone line of original image size, obtain the high frequency detail image of vertical directionAlong image f^TL Do High frequency filter the most respectively, the image obtained is taken the zone line of original image size, To diagonally opposed high frequency detail imageArtificial extractionThe puppet of middle flat site Shadow segment, and utilize K-SVD algorithm to carry out dictionary training, obtain corresponding to level, vertical and diagonal detail Three artifact dictionariesWithTo high-dose images f^THCarry out wavelet decomposition equally, obtain water Flat, vertical and diagonal detail imageDirectly it is carried out K-SVD dictionary training, To corresponding to level, vertical and three characteristics dictionaries of diagonal detailWithTwo kinds of dictionaries are entered Row merges,Obtain level, Vertical and three distinctiveness dictionaries of diagonal detailWith

Step 2, to level, vertical and diagonally opposed high frequency detail image f_chd、f_cvd、f_cddRespectively with right The distinctiveness dictionary answered is indicated, and the part of artifact expressed atom in corresponding distinctiveness dictionary is gone afterwards Remove, the level after being processed, vertical and diagonally opposed high frequency detail image

Concrete, for level detail image f_chd, substitute into the f of following (1) formula,

$\begin{array}{cccc}\underset{D,\mathrm{\α}}{min}\underset{ij}{\Σ}\left|\right|{\mathrm{\α}}_{ij}|{|}_0& s.t.& \left|\right|R_{ij}f-{\mathrm{D\α}}_{ij}|{|}_2^2\≤\mathrm{\ϵ}& \∀i,j\end{array}---\left(1\right)$

Wherein, subscript ij indicate pixel index in image (i, j)；R_ijRepresent that extracting size from image x is n × n Center is at (i, segment x j)_ijOperator；Dictionary D is the matrix of a n × K, by K n-dimensional vector atom Composition；Corresponding n × n the segment of each n dimensional vector；α represents the coefficient sets of the rarefaction representation of all pieces {α_ij}_ij, each segment x_ijBy linear combination D α_ijCarry out approximate representation；||α_ij||₀Represent l⁰Norm, is used for counting Calculate vector α_ijIn nonzero term number；By dictionaryBring the D in (1) formula into, utilize OMP algorithm to ask (1) formula obtains sparse coefficient α, afterwards by the artifact atom of M row before distinctiveness dictionary corresponding in sparse coefficient Coefficient is set to zero, will in α before M row be set to zero, the factor alpha after being processed^p, then by α^pWith f_chdGeneration Enter α Yu f in (2) formula below and obtain the level detail image of pseudo-movie queen

$\tilde{f}={(I+\mathrm{\μ}\underset{ij}{\Σ}{R}_{ij}^T{R}_{ij})}^{-1}(f+\mathrm{\μ}\underset{ij}{\Σ}{R}_{ij}^T{\mathrm{D\α}}_{ij})---\left(2\right)$

Wherein, I is unit matrix, and μ is weight coefficient, in like manner, to f_cvdAnd f_cddUtilizeWithAdopt Take identical step, obtain the vertical detail image of pseudo-movie queenWith diagonal detail image

Step 3, the level to after processing, vertical and diagonally opposed high frequency detail image And original low-frequency image f_caCarry out inverse stationary wavelet transform to rebuild, obtain the CT image of artifact suppression

Rebuilding for inverse stationary wavelet, concrete is calculated as: use the high and low frequency wave filter of Haar small echo, First to untreated wavelet space low-frequency image component f_caAlong carrying out low frequency and low the most respectively Frequency filtering obtainsWavelet space horizontal direction high frequency detail picture content after step 2 is processedAlong water Gentle vertical direction carries out high and low frequency filtering respectively and obtainsWavelet space after processing step 2 hangs down Nogata is to high frequency detail picture contentObtain along carrying out high and low frequency filtering the most respectivelyWavelet space diagonally opposed high frequency detail picture content after step 2 is processedSame along level Carry out high and low frequency filtering respectively to obtain with vertical directionFinally will obtainWith It is added, the image obtained is taken the zone line of original image size, it is possible to obtain inverse stationary wavelet and rebuild, i.e. The image that artifact is inhibited

2. low-dose CT image processing method based on distinctiveness dictionary as claimed in claim 1, its feature It is, the most also includes:

Step 4, the image that step 3 is obtainedUtilize sparse expression method based on dictionary learning that image is entered Row processes further, to remove artifact and the noise of residual；

DefinitionWithThe low-dose CT figure of the artifact suppression that expression preceding step three obtains respectively and side of the present invention Final image after method process, computing formula is as follows:

$\begin{array}{cccc}\underset{D,\mathrm{\α}}{min}\mathrm{\μ}\underset{ij}{\Σ}\left|\right|R_{ij}\tilde{f}-{\mathrm{D\α}}_{ij}|{|}_2^2& s.t.& \left|\right|{\mathrm{\α}}_{ij}|{|}_0\≤T& \∀i,j\end{array}---\left(3\right)$

$\hat{f}={(I+\mathrm{\μ}\underset{ij}{\Σ}{R}_{ij}^T{R}_{ij})}^{-1}(\tilde{f}+\mathrm{\μ}\underset{ij}{\Σ}{R}_{ij}^T{\mathrm{D\α}}_{ij})---\left(4\right)$

Wherein (3) formula can be solved by K-SVD algorithm and obtain dictionary D and sparse coefficient α, substitutes into (4) In can try to achieve final result image

3. low-dose CT image processing method based on distinctiveness dictionary as claimed in claim 1, its feature Being, distinctiveness dictionary used in step 2 is to be merged with " characteristics dictionary " by " artifact dictionary " to form, Front M atom pair in each distinctiveness dictionary answers the atom in artifact dictionary, atom to be afterwards characterized word Atom in allusion quotation；Wherein artifact dictionary is to train from the artificial artifact sample extracted and obtain, and characteristics dictionary is Train from high dose CT image pattern and obtain.