CN107274372A - Dynamic image Enhancement Method and device based on pyramid local contrast - Google Patents
Dynamic image Enhancement Method and device based on pyramid local contrast Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
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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
- G06T2207/10—Image acquisition modality
- G06T2207/10116—X-ray image
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20016—Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
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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
- G06T2207/20172—Image enhancement details
- G06T2207/20192—Edge enhancement; Edge preservation
Abstract
The present invention is applied to medical image processing technical field there is provided a kind of dynamic image Enhancement Method and device based on pyramid local contrast, and method includes：Pyramid decomposition is carried out to pending dynamic image using pyramid multiresolution overall situation enhancing algorithm, gaussian pyramid and laplacian pyramid is obtained；According to gaussian pyramid and laplacian pyramid generation local contrast pyramid, and enhancing processing is carried out to local contrast pyramid, obtain enhanced local contrast pyramid；Enhancing processing is carried out to gaussian pyramid using enhanced local contrast pyramid, and enhancing processing is carried out to laplacian pyramid using enhanced gaussian pyramid, enhanced laplacian pyramid is obtained；Pyramid Reconstruction is carried out using enhanced laplacian pyramid, enhanced dynamic image is obtained.The dynamic image Enhancement Method that the present invention is provided can sufficiently be strengthened trickle edge, so as to improve the display effect of dynamic image.
Description
Technical field
The invention belongs to medical image processing technical field, more particularly to a kind of dynamic based on pyramid local contrast
Image enchancing method and device.
Background technology
Medical x-ray Imaging enhanced, through frequently with pyramid multiresolution overall situation enhancing algorithm, this algorithm is will be original
Dynamic image is decomposed into gaussian pyramid and laplacian pyramid, then the global enhancing of laplacian pyramid progress is handled,
And be reconstructed using the laplacian pyramid after processing；Dynamic image details protrusion after this processing, good contrast, layer
Secondary sense is strong, but it is that global enhancing processing is carried out to dynamic image during details is enhanced, does not differentiate between edge and noise, all
The enhancing of intensity identical is done, noise is also exaggerated while details is strengthened, it is enhanced to trickle edge far from enough.
The content of the invention
The present invention provides a kind of dynamic image Enhancement Method and device based on pyramid local contrast, it is intended to utilize gold
When word tower multiresolution overall situation enhancing algorithm carries out global enhancing to the details of dynamic image, with the golden word of the local contrast of introducing
Tower strengthens the reference of intensity as pyramid, and trickle edge is sufficiently strengthened, so as to improve the display effect of dynamic image
Really.
The invention provides a kind of dynamic image Enhancement Method based on pyramid local contrast, including：
Pyramid decomposition is carried out to pending dynamic image using pyramid multiresolution overall situation enhancing algorithm, height is obtained
This pyramid and laplacian pyramid；
Local contrast pyramid is generated according to the gaussian pyramid and laplacian pyramid, and it is right to the part
Enhancing processing is carried out than degree pyramid, enhanced local contrast pyramid is obtained；
Enhancing processing is carried out to the gaussian pyramid using enhanced local contrast pyramid, obtains enhanced
Gaussian pyramid；
Enhancing processing is carried out to the laplacian pyramid using enhanced gaussian pyramid, enhanced drawing is obtained
This pyramid of pula；
Pyramid Reconstruction is carried out using enhanced laplacian pyramid, enhanced dynamic image is obtained.
Further, the generation pyramidal formula of local contrast is：
It is to the formula that the local contrast pyramid strengthen processing：
EnLctPyr=LctPyr1-enLctParam,
Wherein, LctPyr is local contrast pyramid, and LaplacePyr is laplacian pyramid, and GaussPyr is height
This pyramid, EnLctPyr is enhanced local contrast pyramid, and enLctParam is preset contrast enhancing intensity
Parameter.
Further, it is described that the gaussian pyramid is carried out at enhancing using enhanced local contrast pyramid
Reason, obtains enhanced gaussian pyramid, including：
According to the value size of the laplacian pyramid, enhanced local contrast pyramid and pre-defined
Small value mask pyramid, small value enhancing gaussian pyramid and big value enhancing gaussian pyramid strengthen the gaussian pyramid
Processing, obtains enhanced gaussian pyramid.
Further, the pre-defined small pyramidal formula of value mask is：
The formula of pre-defined small value enhancing gaussian pyramid is：
The formula of pre-defined big value enhancing gaussian pyramid is：
It is to the formula that the gaussian pyramid strengthen processing：
EnGaussPyr=SmallMaskPyr*SmallEnGaussPyr+ (1-SmallMaskPyr) *
LargeEnGaussPyr
Wherein, SmallMaskPyr is small value mask pyramid, and l is pyramidal layer, and r is row coordinate, and c is row coordinate,
LaplacePyr is laplacian pyramid, and SmallEnGaussPyr is small value enhancing gaussian pyramid, and EnLctPyr is enhancing
Local contrast pyramid afterwards, GaussPyr is gaussian pyramid, and LargeEnGaussPyr is the big golden word of value enhancing Gauss
Tower, EnGaussPyr is enhanced gaussian pyramid.
Further, it is to the formula that the laplacian pyramid strengthen processing：
EnLaplacePyr=LaplacePyr+EnGaussPyr-GaussPyr,
Wherein, EnLaplacePyr is enhanced laplacian pyramid, and LaplacePyr is laplacian pyramid,
EnGaussPyr is enhanced gaussian pyramid, and GaussPyr is gaussian pyramid.
Present invention also offers a kind of dynamic image intensifier based on pyramid local contrast, including：
Decomposing module, for carrying out golden word to pending dynamic image using pyramid multiresolution overall situation enhancing algorithm
Tower is decomposed, and obtains gaussian pyramid and laplacian pyramid；
Local contrast strengthens module, for generating local contrast according to the gaussian pyramid and laplacian pyramid
Pyramid is spent, and enhancing processing is carried out to the local contrast pyramid, enhanced local contrast pyramid is obtained；
Gaussian pyramid strengthens module, for being entered using enhanced local contrast pyramid to the gaussian pyramid
Row enhancing is handled, and obtains enhanced gaussian pyramid；
Laplacian pyramid strengthens module, for utilizing enhanced gaussian pyramid to the laplacian pyramid
Enhancing processing is carried out, enhanced laplacian pyramid is obtained；
Reconstructed module, for carrying out Pyramid Reconstruction using enhanced laplacian pyramid, obtains enhanced dynamic
State image.
Further, the generation pyramidal formula of local contrast is：
It is to the formula that the local contrast pyramid strengthen processing：
EnLctPyr=LctPyr1-enLctParam,
Wherein, LctPyr is local contrast pyramid, and LaplacePyr is laplacian pyramid, and GaussPyr is height
This pyramid, EnLctPyr is enhanced local contrast pyramid, and enLctParam is preset contrast enhancing intensity
Parameter.
Further, the local contrast enhancing module, specifically for the value according to the laplacian pyramid
Size, enhanced local contrast pyramid and pre-defined small value mask pyramid, small value enhancing gaussian pyramid and
Big value enhancing gaussian pyramid carries out enhancing processing to the gaussian pyramid, obtains enhanced gaussian pyramid.
Further, the pre-defined small pyramidal formula of value mask is：
The formula of pre-defined small value enhancing gaussian pyramid is：
The formula of pre-defined big value enhancing gaussian pyramid is：
It is to the formula that the gaussian pyramid strengthen processing：
EnGaussPyr=SmallMaskPyr*SmallEnGaussPyr+ (1-SmallMaskPyr) *
LargeEnGaussPyr
Wherein, SmallMaskPyr is small value mask pyramid, and l is pyramidal layer, and r is row coordinate, and c is row coordinate,
LaplacePyr is laplacian pyramid, and SmallEnGaussPyr is small value enhancing gaussian pyramid, and EnLctPyr is enhancing
Local contrast pyramid afterwards, GaussPyr is gaussian pyramid, and LargeEnGaussPyr is the big golden word of value enhancing Gauss
Tower, EnGaussPyr is enhanced gaussian pyramid.
Further, it is to the formula that the laplacian pyramid strengthen processing：
EnLaplacePyr=LaplacePyr+EnGaussPyr-GaussPyr,
Wherein, EnLaplacePyr is enhanced laplacian pyramid, and LaplacePyr is laplacian pyramid,
EnGaussPyr is enhanced gaussian pyramid, and GaussPyr is gaussian pyramid.
Compared with prior art, beneficial effect is the present invention：One kind that the present invention is provided is based on pyramid local contrast
The dynamic image Enhancement Method and device of degree, first, will be moved using pyramid multiresolution overall situation enhancing algorithm to pending
State picture breakdown is gaussian pyramid and laplacian pyramid；Then, given birth to according to gaussian pyramid and laplacian pyramid
Enhancing processing is carried out into local contrast pyramid, and to local contrast pyramid, enhanced local contrast gold is obtained
Word tower；Subsequently, enhancing processing is carried out to gaussian pyramid using enhanced local contrast pyramid, and utilized after enhancing
Gaussian pyramid to laplacian pyramid carry out enhancing processing, obtain enhanced laplacian pyramid；Finally, utilize
Enhanced laplacian pyramid carries out Pyramid Reconstruction, obtains enhanced dynamic image；The present invention and prior art phase
Than strengthening the reference of intensity as pyramid by introducing local contrast pyramid, the trickle edge of different layers being increased
By force so that when being strengthened using pyramid multiresolution overall situation enhancing algorithm details, trickle edge can be carried out
Sufficiently enhancing, so as to improve dynamic image signal to noise ratio so that the display effect of dynamic image is improved.
Brief description of the drawings
Fig. 1 is a kind of reality of dynamic image Enhancement Method based on pyramid local contrast provided in an embodiment of the present invention
Existing process schematic；
Fig. 2 is a kind of stream of dynamic image Enhancement Method based on pyramid local contrast provided in an embodiment of the present invention
Cheng Tu；
Fig. 3 is the schematic diagram decomposed and reconstituted to pending dynamic image progress provided in an embodiment of the present invention；
Fig. 4 is a kind of mould of dynamic image intensifier based on pyramid local contrast provided in an embodiment of the present invention
Block schematic diagram.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, it is right below in conjunction with drawings and Examples
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.
Global increasing is carried out to dynamic image using pyramid multiresolution overall situation enhancing algorithm due to existing in the prior art
When strength is managed, technical problem far from being enough enhanced to trickle edge.
In order to solve the above-mentioned technical problem, the present invention proposes a kind of dynamic image enhancing based on pyramid local contrast
Method and device, as shown in figure 1, being entered using pyramid multiresolution overall situation enhancing algorithm to the pending dynamic image of input
Row pyramid decomposition, obtains gaussian pyramid and laplacian pyramid；According to the gaussian pyramid and the golden word of Laplce
Tower generates local contrast pyramid, and carries out enhancing processing to the local contrast pyramid, obtains enhanced part
Contrast pyramid；Enhancing processing is carried out to the gaussian pyramid using enhanced local contrast pyramid, and utilized
Enhanced gaussian pyramid carries out enhancing processing to the laplacian pyramid, obtains the golden word of enhanced Laplce
Tower；Pyramid Reconstruction is carried out using enhanced laplacian pyramid, enhanced dynamic image is obtained, and export the increasing
Dynamic image after strong.
A kind of dynamic image Enhancement Method based on pyramid local contrast that the lower mask body introduction present invention is provided, such as
Shown in Fig. 2, including：
Step S1, pyramid point is carried out using pyramid multiresolution overall situation enhancing algorithm to pending dynamic image
Solution, obtains gaussian pyramid and laplacian pyramid；
Specifically, when the embodiment of the present invention carries out pyramid decomposition to dynamic image, three layers are decomposed into.As shown in figure 3, being
The schematic diagram of three layers of pyramid decomposition reconstruct, pyramid are carried out to pending dynamic image using pyramid decomposition restructing algorithm
Gaussian pyramid and laplacian pyramid are decomposed into, wherein, gaussian pyramid is that circulation is filtered to pending dynamic image
What down-sampling was got, the G in corresponding diagram0~G2, represented with gaussian pyramid；L in laplacian pyramid corresponding diagram 30
~L2, wherein, first two layers is subtracted each other after being filtered again with next layer of gaussian pyramid up-sampling by current layer gaussian pyramid
Arrive, and last layer of laplacian pyramid is identical with last layer of gaussian pyramid, i.e. L2=G2.It is pyramidal heavy
Structure, is reconstructed with laplacian pyramid, since last layer, circulation to picture up-sampling filtering again with last layer
Image addition, until the 0th layer, because pyramid decomposition reconstruct belongs to image basis knowledge, so will not be repeated here.
Step S2, local contrast pyramid is generated according to the gaussian pyramid and laplacian pyramid, and to institute
State local contrast pyramid and carry out enhancing processing, obtain enhanced local contrast pyramid；
Specifically, every layer of local contrast of laplacian pyramid and gaussian pyramid is calculated, it is every by what is calculated
The local contrast of layer constitutes local contrast pyramid altogether.It is to decompose dynamic image in the embodiment of the present invention
For the 0th layer of three layers, i.e. pyramid, layers 1 and 2, because last layer belongs to low-frequency information, high frequency detail is not belonging to, institute
When carrying out layered shaping, to be the 2nd layer to pyramid last layer and not operate.
Specifically, the generation pyramidal formula of local contrast is：
Specifically, it is right according to each layer of part for calculating each layer of corresponding laplacian pyramid and gaussian pyramid
Than degree, the embodiment of the present invention is the local contrast that the 0th layer and the 1st layer are calculated using above-mentioned formula, so as to generate local contrast
Spend pyramid.
It is to the formula that the local contrast pyramid strengthen processing：
EnLctPyr=LctPyr1-enLctParam,
Wherein, LctPyr is local contrast pyramid, and LaplacePyr is laplacian pyramid, and GaussPyr is height
This pyramid, EnLctPyr is enhanced local contrast pyramid, and enLctParam is preset contrast enhancing intensity
Parameter, its span is 0.0~1.0.
Step S3, carries out enhancing processing to the gaussian pyramid using enhanced local contrast pyramid, obtains
Enhanced gaussian pyramid；
Specifically, according to the value size of the laplacian pyramid, enhanced local contrast pyramid and pre-
Small value mask pyramid, small value enhancing gaussian pyramid and the big value first defined strengthens gaussian pyramid to the gaussian pyramid
Enhancing processing is carried out, enhanced gaussian pyramid is obtained.
Wherein, the pre-defined small pyramidal formula of value mask is：
Wherein, the formula of pre-defined small value enhancing gaussian pyramid is：
Wherein, the formula of pre-defined big value enhancing gaussian pyramid is：
Wherein, it is to the formula that the gaussian pyramid strengthen processing：
EnGaussPyr=SmallMaskPyr*SmallEnGaussPyr+ (1-SmallMaskPyr) *
LargeEnGaussPyr
Wherein, SmallMaskPyr is small value mask pyramid, and l is pyramidal layer, and r is row coordinate, and c is row coordinate,
LaplacePyr is laplacian pyramid, and SmallEnGaussPyr is small value enhancing gaussian pyramid, and EnLctPyr is enhancing
Local contrast pyramid afterwards, GaussPyr is gaussian pyramid, and LargeEnGaussPyr is the big golden word of value enhancing Gauss
Tower, EnGaussPyr is enhanced gaussian pyramid.
Specifically, the formula for being strengthened processing using above-mentioned gaussian pyramid is carried out at enhancing to every layer of gaussian pyramid
Reason, so as to obtain enhanced gaussian pyramid；More specifically, when some pixel value of a certain layer of laplacian pyramid is small
When 0, corresponding small value mask pyramid takes 1, then strengthen gaussian pyramid using small value is carried out at enhancing to gaussian pyramid
Reason；When some pixel value of a certain layer of laplacian pyramid is more than 0, corresponding small value mask pyramid takes 0, then utilizes
Big value enhancing gaussian pyramid carries out enhancing processing to gaussian pyramid, and enhancing processing is all carried out to every layer (the 0th layer and the 1st layer)
Afterwards, you can obtain enhanced gaussian pyramid.
Step S4, carries out enhancing processing to the laplacian pyramid using enhanced gaussian pyramid, is increased
Laplacian pyramid after strong；
Specifically, it is to the formula that the laplacian pyramid strengthen processing：
EnLaplacePyr=LaplacePyr+EnGaussPyr-GaussPyr,
Wherein, EnLaplacePyr is enhanced laplacian pyramid, and LaplacePyr is laplacian pyramid,
EnGaussPyr is enhanced gaussian pyramid, and GaussPyr is gaussian pyramid.
Specifically, La Pu of the formula to every layer (the 0th layer and the 1st layer) of processing is strengthened using above-mentioned laplacian pyramid
Lars pyramid is strengthened, you can obtain enhanced laplacian pyramid.
Step S5, carries out Pyramid Reconstruction using enhanced laplacian pyramid, obtains enhanced dynamic image.
Specifically, the laplacian pyramid after noise reduction is reconstructed using previously described Pyramid Reconstruction method,
So as to get the dynamic image after noise reduction process.
A kind of dynamic image Enhancement Method based on pyramid local contrast provided in an embodiment of the present invention, by introducing
Local contrast pyramid strengthens the reference of intensity as pyramid, the trickle edge of different layers is strengthened so that in profit
When being strengthened with pyramid multiresolution overall situation enhancing algorithm details, trickle edge can sufficiently be strengthened,
So as to improve dynamic image signal to noise ratio so that the display effect of dynamic image is improved.
A kind of dynamic image intensifier based on pyramid local contrast that the lower mask body introduction present invention is provided, such as
Shown in Fig. 4, including：
Decomposing module 1, for carrying out gold to pending dynamic image using pyramid multiresolution overall situation enhancing algorithm
Word tower is decomposed, and obtains gaussian pyramid and laplacian pyramid；
Specifically, when the embodiment of the present invention carries out pyramid decomposition to dynamic image, three layers are decomposed into.
Local contrast strengthens module 2, local right for being generated according to the gaussian pyramid and laplacian pyramid
Enhancing processing is carried out than degree pyramid, and to the local contrast pyramid, enhanced local contrast pyramid is obtained；
Specifically, every layer of local contrast of laplacian pyramid and gaussian pyramid is calculated, it is every by what is calculated
The local contrast of layer constitutes local contrast pyramid altogether.It is to decompose dynamic image in the embodiment of the present invention
For the 0th layer of three layers, i.e. pyramid, layers 1 and 2, because last layer belongs to low-frequency information, high frequency detail is not belonging to, institute
When carrying out layered shaping, to be the 2nd layer to pyramid last layer and not operate.
Specifically, the generation pyramidal formula of local contrast is：
Specifically, it is right according to each layer of part for calculating each layer of corresponding laplacian pyramid and gaussian pyramid
Than degree, the embodiment of the present invention is the local contrast that the 0th layer and the 1st layer are calculated using above-mentioned formula, so as to generate local contrast
Spend pyramid.
Specifically, it is to the formula that the local contrast pyramid strengthen processing：
EnLctPyr=LctPyr1-enLctParam,
Wherein, LctPyr is local contrast pyramid, and LaplacePyr is laplacian pyramid, and GaussPyr is height
This pyramid, EnLctPyr is enhanced local contrast pyramid, and enLctParam is preset contrast enhancing intensity
Parameter, its span is 0.0~1.0.
Gaussian pyramid strengthens module 3, for utilizing enhanced local contrast pyramid to the gaussian pyramid
Enhancing processing is carried out, enhanced gaussian pyramid is obtained；
Specifically, for the value size according to the laplacian pyramid, enhanced local contrast pyramid
With pre-defined small value mask pyramid, small value enhancing gaussian pyramid and big value enhancing gaussian pyramid to Gauss gold
Word tower carries out enhancing processing, obtains enhanced gaussian pyramid.
Wherein, the pre-defined small pyramidal formula of value mask is：
Wherein, the formula of pre-defined small value enhancing gaussian pyramid is：
Wherein, the formula of pre-defined big value enhancing gaussian pyramid is：
Wherein, it is to the formula that the gaussian pyramid strengthen processing：
EnGaussPyr=SmallMaskPyr*SmallEnGaussPyr+ (1-SmallMaskPyr) *
LargeEnGaussPyr
Wherein, SmallMaskPyr is small value mask pyramid, and l is pyramidal layer, and r is row coordinate, and c is row coordinate,
LaplacePyr is laplacian pyramid, and SmallEnGaussPyr is small value enhancing gaussian pyramid, and EnLctPyr is enhancing
Local contrast pyramid afterwards, GaussPyr is gaussian pyramid, and LargeEnGaussPyr is the big golden word of value enhancing Gauss
Tower, EnGaussPyr is enhanced gaussian pyramid.
Specifically, the formula for being strengthened processing using above-mentioned gaussian pyramid is carried out at enhancing to every layer of gaussian pyramid
Reason, so as to obtain enhanced gaussian pyramid；More specifically, when some pixel value of a certain layer of laplacian pyramid is small
When 0, corresponding small value mask pyramid takes 1, then strengthen gaussian pyramid using small value is carried out at enhancing to gaussian pyramid
Reason；When some pixel value of a certain layer of laplacian pyramid is more than 0, corresponding small value mask pyramid takes 0, then utilizes
Big value enhancing gaussian pyramid carries out enhancing processing to gaussian pyramid, and enhancing processing is all carried out to every layer (the 0th layer and the 1st layer)
Afterwards, you can obtain enhanced gaussian pyramid.
Laplacian pyramid strengthens module 4, for utilizing enhanced gaussian pyramid to the golden word of the Laplce
Tower carries out enhancing processing, obtains enhanced laplacian pyramid；
Specifically, it is to the formula that the laplacian pyramid strengthen processing：
EnLaplacePyr=LaplacePyr+EnGaussPyr-GaussPyr,
Wherein, EnLaplacePyr is enhanced laplacian pyramid, and LaplacePyr is laplacian pyramid,
EnGaussPyr is enhanced gaussian pyramid, and GaussPyr is gaussian pyramid.
Specifically, La Pu of the formula to every layer (the 0th layer and the 1st layer) of processing is strengthened using above-mentioned laplacian pyramid
Lars pyramid is strengthened, you can obtain enhanced laplacian pyramid.
Reconstructed module 5, for carrying out Pyramid Reconstruction using enhanced laplacian pyramid, obtains enhanced dynamic
State image.
Specifically, the laplacian pyramid after noise reduction is reconstructed using previously described Pyramid Reconstruction method,
So as to get the dynamic image after noise reduction process.
A kind of dynamic image intensifier based on pyramid local contrast provided in an embodiment of the present invention, by introducing
Local contrast pyramid strengthens the reference of intensity as pyramid, the trickle edge of different layers is strengthened so that in profit
When being strengthened with pyramid multiresolution overall situation enhancing algorithm details, trickle edge can sufficiently be strengthened,
So as to improve dynamic image signal to noise ratio so that the display effect of dynamic image is improved.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
Any modifications, equivalent substitutions and improvements made within refreshing and principle etc., should be included in the scope of the protection.
Claims (10)
1. a kind of dynamic image Enhancement Method based on pyramid local contrast, it is characterised in that including：
Pyramid decomposition is carried out to pending dynamic image using pyramid multiresolution overall situation enhancing algorithm, Gauss gold is obtained
Word tower and laplacian pyramid；
According to the gaussian pyramid and laplacian pyramid generation local contrast pyramid, and to the local contrast
Pyramid carries out enhancing processing, obtains enhanced local contrast pyramid；
Enhancing processing is carried out to the gaussian pyramid using enhanced local contrast pyramid, enhanced Gauss is obtained
Pyramid；
Enhancing processing is carried out to the laplacian pyramid using enhanced gaussian pyramid, enhanced drawing pula is obtained
This pyramid；
Pyramid Reconstruction is carried out using enhanced laplacian pyramid, enhanced dynamic image is obtained.
2. dynamic image Enhancement Method as claimed in claim 1, it is characterised in that the generation pyramidal formula of local contrast
For：
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It is to the formula that the local contrast pyramid strengthen processing：
EnLctPyr=LctPyr1-enLctParam,
Wherein, LctPyr is local contrast pyramid, and LaplacePyr is laplacian pyramid, and GaussPyr is Gauss gold
Word tower, EnLctPyr is enhanced local contrast pyramid, and enLctParam is preset contrast enhancing intensive parameter.
3. dynamic image Enhancement Method as claimed in claim 1 or 2, it is characterised in that described right using enhanced part
Enhancing processing is carried out to the gaussian pyramid than degree pyramid, enhanced gaussian pyramid is obtained, including：
According to the value size of the laplacian pyramid, enhanced local contrast pyramid and pre-defined small value
Mask pyramid, small value enhancing gaussian pyramid and big value enhancing gaussian pyramid are carried out at enhancing to the gaussian pyramid
Reason, obtains enhanced gaussian pyramid.
4. dynamic image Enhancement Method as claimed in claim 3, it is characterised in that the golden word of pre-defined small value mask
The formula of tower is：
The formula of pre-defined small value enhancing gaussian pyramid is：
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The formula of pre-defined big value enhancing gaussian pyramid is：
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<mi>r</mi>
<mo>=</mo>
<mfrac>
<mrow>
<mn>1</mn>
<mo>+</mo>
<mi>E</mi>
<mi>n</mi>
<mi>L</mi>
<mi>c</mi>
<mi>t</mi>
<mi>P</mi>
<mi>y</mi>
<mi>r</mi>
</mrow>
<mrow>
<mn>1</mn>
<mo>-</mo>
<mi>E</mi>
<mi>n</mi>
<mi>L</mi>
<mi>c</mi>
<mi>t</mi>
<mi>P</mi>
<mi>y</mi>
<mi>r</mi>
</mrow>
</mfrac>
<mo>*</mo>
<mrow>
<mo>(</mo>
<mi>G</mi>
<mi>a</mi>
<mi>u</mi>
<mi>s</mi>
<mi>s</mi>
<mi>P</mi>
<mi>y</mi>
<mi>r</mi>
<mo>-</mo>
<mi>L</mi>
<mi>a</mi>
<mi>p</mi>
<mi>l</mi>
<mi>a</mi>
<mi>c</mi>
<mi>e</mi>
<mi>P</mi>
<mi>y</mi>
<mi>r</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
</mrow>
It is to the formula that the gaussian pyramid strengthen processing：
EnGaussPyr=SmallMaskPyr*SmallEnGaussPyr+ (1-SmallMaskPyr) * LargeEnGaussPyr
Wherein, SmallMaskPyr is small value mask pyramid, and l is pyramidal layer, and r is row coordinate, and c is row coordinate,
LaplacePyr is laplacian pyramid, and SmallEnGaussPyr is small value enhancing gaussian pyramid, and EnLctPyr is enhancing
Local contrast pyramid afterwards, GaussPyr is gaussian pyramid, and LargeEnGaussPyr is the big golden word of value enhancing Gauss
Tower, EnGaussPyr is enhanced gaussian pyramid.
5. dynamic image Enhancement Method as claimed in claim 1, it is characterised in that increase to the laplacian pyramid
Strength reason formula be：
EnLaplacePyr=LaplacePyr+EnGaussPyr-GaussPyr,
Wherein, EnLaplacePyr is enhanced laplacian pyramid, and LaplacePyr is laplacian pyramid,
EnGaussPyr is enhanced gaussian pyramid, and GaussPyr is gaussian pyramid.
6. a kind of dynamic image intensifier based on pyramid local contrast, it is characterised in that including：
Decomposing module, for carrying out pyramid point to pending dynamic image using pyramid multiresolution overall situation enhancing algorithm
Solution, obtains gaussian pyramid and laplacian pyramid；
Local contrast strengthens module, for generating local contrast gold according to the gaussian pyramid and laplacian pyramid
Word tower, and enhancing processing is carried out to the local contrast pyramid, obtain enhanced local contrast pyramid；
Gaussian pyramid strengthens module, for being increased using enhanced local contrast pyramid to the gaussian pyramid
Strength is managed, and obtains enhanced gaussian pyramid；
Laplacian pyramid strengthens module, for being carried out using enhanced gaussian pyramid to the laplacian pyramid
Enhancing is handled, and obtains enhanced laplacian pyramid；
Reconstructed module, for carrying out Pyramid Reconstruction using enhanced laplacian pyramid, obtains enhanced Dynamic Graph
Picture.
7. dynamic image intensifier as claimed in claim 6, it is characterised in that the generation pyramidal formula of local contrast
For：
<mrow>
<mi>L</mi>
<mi>c</mi>
<mi>t</mi>
<mi>P</mi>
<mi>y</mi>
<mi>r</mi>
<mo>=</mo>
<mfrac>
<mrow>
<mo>|</mo>
<mi>L</mi>
<mi>a</mi>
<mi>p</mi>
<mi>l</mi>
<mi>a</mi>
<mi>c</mi>
<mi>e</mi>
<mi>P</mi>
<mi>y</mi>
<mi>r</mi>
<mo>|</mo>
</mrow>
<mrow>
<mo>|</mo>
<mn>2</mn>
<mo>*</mo>
<mi>G</mi>
<mi>a</mi>
<mi>u</mi>
<mi>s</mi>
<mi>s</mi>
<mi>P</mi>
<mi>y</mi>
<mi>r</mi>
<mo>-</mo>
<mi>L</mi>
<mi>a</mi>
<mi>p</mi>
<mi>l</mi>
<mi>a</mi>
<mi>c</mi>
<mi>e</mi>
<mi>P</mi>
<mi>y</mi>
<mi>r</mi>
<mo>|</mo>
</mrow>
</mfrac>
<mo>,</mo>
</mrow>
It is to the formula that the local contrast pyramid strengthen processing：
EnLctPyr=LctPyr1-enLctParam,
Wherein, LctPyr is local contrast pyramid, and LaplacePyr is laplacian pyramid, and GaussPyr is Gauss gold
Word tower, EnLctPyr is enhanced local contrast pyramid, and enLctParam is preset contrast enhancing intensive parameter.
8. dynamic image intensifier as claimed in claims 6 or 7, it is characterised in that the local contrast strengthens module,
Specifically for the value size according to the laplacian pyramid, enhanced local contrast pyramid and pre-defined
Small value mask pyramid, small value enhancing gaussian pyramid and big value enhancing gaussian pyramid strengthen the gaussian pyramid
Processing, obtains enhanced gaussian pyramid.
9. dynamic image intensifier as claimed in claim 8, it is characterised in that the golden word of pre-defined small value mask
The formula of tower is：
The formula of pre-defined small value enhancing gaussian pyramid is：
<mrow>
<mi>S</mi>
<mi>m</mi>
<mi>a</mi>
<mi>l</mi>
<mi>l</mi>
<mi>E</mi>
<mi>n</mi>
<mi>G</mi>
<mi>a</mi>
<mi>u</mi>
<mi>s</mi>
<mi>s</mi>
<mi>P</mi>
<mi>y</mi>
<mi>r</mi>
<mo>=</mo>
<mfrac>
<mrow>
<mn>1</mn>
<mo>-</mo>
<mi>E</mi>
<mi>n</mi>
<mi>L</mi>
<mi>c</mi>
<mi>t</mi>
<mi>P</mi>
<mi>y</mi>
<mi>r</mi>
</mrow>
<mrow>
<mn>1</mn>
<mo>+</mo>
<mi>E</mi>
<mi>n</mi>
<mi>L</mi>
<mi>c</mi>
<mi>t</mi>
<mi>P</mi>
<mi>y</mi>
<mi>r</mi>
</mrow>
</mfrac>
<mo>*</mo>
<mrow>
<mo>(</mo>
<mi>G</mi>
<mi>a</mi>
<mi>u</mi>
<mi>s</mi>
<mi>s</mi>
<mi>P</mi>
<mi>y</mi>
<mi>r</mi>
<mo>-</mo>
<mi>L</mi>
<mi>a</mi>
<mi>p</mi>
<mi>l</mi>
<mi>a</mi>
<mi>c</mi>
<mi>e</mi>
<mi>P</mi>
<mi>y</mi>
<mi>r</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
</mrow>
The formula of pre-defined big value enhancing gaussian pyramid is：
<mrow>
<mi>L</mi>
<mi>arg</mi>
<mi>e</mi>
<mi>E</mi>
<mi>n</mi>
<mi>G</mi>
<mi>a</mi>
<mi>u</mi>
<mi>s</mi>
<mi>s</mi>
<mi>P</mi>
<mi>y</mi>
<mi>r</mi>
<mo>=</mo>
<mfrac>
<mrow>
<mn>1</mn>
<mo>+</mo>
<mi>E</mi>
<mi>n</mi>
<mi>L</mi>
<mi>c</mi>
<mi>t</mi>
<mi>P</mi>
<mi>y</mi>
<mi>r</mi>
</mrow>
<mrow>
<mn>1</mn>
<mo>-</mo>
<mi>E</mi>
<mi>n</mi>
<mi>L</mi>
<mi>c</mi>
<mi>t</mi>
<mi>P</mi>
<mi>y</mi>
<mi>r</mi>
</mrow>
</mfrac>
<mo>*</mo>
<mrow>
<mo>(</mo>
<mi>G</mi>
<mi>a</mi>
<mi>u</mi>
<mi>s</mi>
<mi>s</mi>
<mi>P</mi>
<mi>y</mi>
<mi>r</mi>
<mo>-</mo>
<mi>L</mi>
<mi>a</mi>
<mi>p</mi>
<mi>l</mi>
<mi>a</mi>
<mi>c</mi>
<mi>e</mi>
<mi>P</mi>
<mi>y</mi>
<mi>r</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
</mrow>
It is to the formula that the gaussian pyramid strengthen processing：
EnGaussPyr=SmallMaskPyr*SmallEnGaussPyr+ (1-SmallMaskPyr) * LargeEnGaussPyr
Wherein, SmallMaskPyr is small value mask pyramid, and l is pyramidal layer, and r is row coordinate, and c is row coordinate,
LaplacePyr is laplacian pyramid, and SmallEnGaussPyr is small value enhancing gaussian pyramid, and EnLctPyr is enhancing
Local contrast pyramid afterwards, GaussPyr is gaussian pyramid, and LargeEnGaussPyr is the big golden word of value enhancing Gauss
Tower, EnGaussPyr is enhanced gaussian pyramid.
10. dynamic image intensifier as claimed in claim 6, it is characterised in that carried out to the laplacian pyramid
Strengthening the formula handled is：
EnLaplacePyr=LaplacePyr+EnGaussPyr-GaussPyr,
Wherein, EnLaplacePyr is enhanced laplacian pyramid, and LaplacePyr is laplacian pyramid,
EnGaussPyr is enhanced gaussian pyramid, and GaussPyr is gaussian pyramid.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110232668A (en) * | 2019-06-17 | 2019-09-13 | 首都师范大学 | A kind of multi-scale image Enhancement Method |
| CN110415188A (en) * | 2019-07-10 | 2019-11-05 | 首都师范大学 | A kind of HDR image tone mapping method based on Multiscale Morphological |
| CN111428753A (en) * | 2020-02-26 | 2020-07-17 | 北京国电通网络技术有限公司 | Training set acquisition method, and electric power facility detection method and device |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101673396A (en) * | 2009-09-07 | 2010-03-17 | 南京理工大学 | Image fusion method based on dynamic object detection |
| CN103778616A (en) * | 2012-10-22 | 2014-05-07 | 中国科学院研究生院 | Contrast pyramid image fusion method based on area |
| CN106339998A (en) * | 2016-08-18 | 2017-01-18 | 南京理工大学 | Multi-focus image fusion method based on contrast pyramid transformation |
-
2017
- 2017-06-26 CN CN201710492041.0A patent/CN107274372B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101673396A (en) * | 2009-09-07 | 2010-03-17 | 南京理工大学 | Image fusion method based on dynamic object detection |
| CN103778616A (en) * | 2012-10-22 | 2014-05-07 | 中国科学院研究生院 | Contrast pyramid image fusion method based on area |
| CN106339998A (en) * | 2016-08-18 | 2017-01-18 | 南京理工大学 | Multi-focus image fusion method based on contrast pyramid transformation |
Non-Patent Citations (2)
| Title |
|---|
| ALI SAAD: "Visual enhancement of digital ultrasound images: Wavelet versus Gauss-Laplace contrast pyramid", 《INTERNATIONAL JOURNAL OF COMPUTER RADIOLOGY AND SURGERY》 * |
| ZHENGFANG FU等: "A Multi-resolution Image Fusion Algorithm Based on Multi-factor Weights", 《JOURNAL OF DIGITAL INFORMATION MANAGEMENT》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110232668A (en) * | 2019-06-17 | 2019-09-13 | 首都师范大学 | A kind of multi-scale image Enhancement Method |
| CN110232668B (en) * | 2019-06-17 | 2021-04-09 | 首都师范大学 | Multi-scale image enhancement method |
| CN110415188A (en) * | 2019-07-10 | 2019-11-05 | 首都师范大学 | A kind of HDR image tone mapping method based on Multiscale Morphological |
| CN111428753A (en) * | 2020-02-26 | 2020-07-17 | 北京国电通网络技术有限公司 | Training set acquisition method, and electric power facility detection method and device |
| CN111428753B (en) * | 2020-02-26 | 2024-01-09 | 北京国电通网络技术有限公司 | Training set acquisition method, electric power facility detection method and device |
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