CN102014243B - Method and device for enhancing images - Google Patents

Abstract

The invention provides a method for enhancing images, which comprises the following steps: processing original images to obtain detail images; calculating local enhancement coefficients of an area which takes each pixel point as a center in the original images; and enhancing each pixel point corresponding to the original images in accordance with the obtained detail images and each calculatedly obtained local enhancement coefficient so as to obtain enhanced detail images. The invention also discloses a device for enhancing the images. The method and device for enhancing the images, which are provided by the invention, are used, thereby being capable of enhancing detail information of the images, such as edges and the like, and improving the accuracy of intelligent analysis and processing.

Description

A kind of image enchancing method and device

Technical field

The present invention relates to technical field of image processing, particularly a kind of image enchancing method and device.

Background technology

Intelligent monitoring is applied to various fields, like road traffic, city security protection etc.Fig. 1 is the structural representation of existing intelligent monitor system, and as shown in Figure 1, this intelligent monitor system comprises image capture module 11, video encoding module 12, network transmission module 13, video decode module 14, image display 15 and intelligent processing module 16.Wherein, image capture module 11 is used for through the camera acquisition video image; Video encoding module 12 is used for the video image that collects is carried out compressed encoding; Network transmission module 13 is used for transmitting through the image code stream that network obtains after to compressed encoding; Video decode module 14 is used for the image code stream that obtains through Network Transmission is decoded, and obtains decoded video image; Image display 15 is used for the decoded video image that is obtained by video decode module 14 is shown through display device; Intelligent processing module 16 is used for that the decoded video image that is obtained by video decode module 14 is carried out intellectual analysis and handles, like candid photograph, car plate identification etc.

But; Video camera receives external environment (greasy weather for example when video image is gathered; Rainy day, night etc.) and the influence of factor such as the quality of video camera own, thus caused decoded video image is shown or the intellectual analysis process result can not satisfy user's demand; For this reason can be through decoded video image is strengthened; So that after handling the enhanced information that provides extra for the demonstration of back and intellectual analysis, again the video image after strengthening is shown and intellectual analysis is handled, show and the intellectual analysis accuracy for processing to improve.

At present; Can be when decoded video image is strengthened through strengthening two kinds of methods based on the figure image intensifying of grey level stretching with based on the image local of statistical information; Though these two kinds of methods can strengthen image; But, when strengthening entire image, cause losing of detailed information such as image border easily based on the image enchancing method of grey level stretching; And based on the image local Enhancement Method of statistical information when image is strengthened easily the high-frequency information of noise as image, promptly when strengthening image, introduce higher noise easily.

Summary of the invention

In view of this, the invention provides a kind of image enchancing method, can strengthen, improved the intellectual analysis accuracy for processing detailed information such as edge of image.

The present invention also provides a kind of image intensifier device, can strengthen detailed information such as edge of image, has improved the intellectual analysis accuracy for processing.

In order to achieve the above object, the technical scheme of the present invention's proposition is:

A kind of image enchancing method, this method comprises:

Original image is handled, obtained detail pictures;

Calculating in the original image with each pixel is the local reinforcing coefficient in the zone at center;

According to the detail pictures that obtains and each the local reinforcing coefficient that calculates each corresponding pixel of original image is strengthened, obtains details and strengthen the back image,

Wherein, be that the local reinforcing coefficient in the zone at center comprises with each pixel in the said calculating original image: calculate through following formula that (i j) is the local reinforcing coefficient β in the zone at center with each pixel in the original image _Local(i, j),

β _Local(i, j)=β _E(i, j)+β _σ(i, j), wherein,

${\mathrm{\&beta;}}_E(i,j)=\left\{\begin{array}{cc}{\&PartialD;}_E(k_E{E}_{\mathrm{global}}-E_{\mathrm{local}}(i,j)),& E_{\mathrm{local}}(i,j)<k_E{E}_{\mathrm{global}}\\ 0,& E_{\mathrm{local}}(i,j)\&GreaterEqual;k_E{E}_{\mathrm{global}}\end{array}\right.,$

${\mathrm{\&beta;}}_{\mathrm{\&sigma;}}(i,j)=\left\{\begin{array}{cc}{\&PartialD;}_{\mathrm{\&sigma;}}({\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)-k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2),& {\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)>k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2\\ 0,& {\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)\&le;k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2\end{array}\right.,$

Said E _GlobalBe the overall average of original image, said

Be the overall variance of original image, said E _Local(i, j) be the original image pixels point (i, local mean value j), said

For the original image pixels point (i, local variance j), said For image light and shade weight coefficient, said

Be the image detail weight coefficient, said k _EBe the threshold value coefficient of image light and shade, said k _σThreshold value coefficient for image detail information;

The detail pictures that said basis obtains strengthens each corresponding pixel of original image with the local reinforcing coefficient of each that calculates, and obtains details enhancing back image and comprises: calculate details through following formula and strengthen the back image I _Detail,

I _Detail(i，j)＝I _orig(i，j)+β _globalβ _local(i，j)I _high(i，j)，

Wherein, said I _Detail(i j) strengthens the back image I for details _DetailPixel (i, pixel value j), said I _Orig(i j) is original image pixels point (i, pixel value j), said β _GlobalBe overall reinforcing coefficient, its value is selected according to image actual needs enhanced strength, said β _Local(i j) is original image pixels point (i, local reinforcing coefficient j), said I _High(i j) is detail pictures pixel (i, pixel value j).

This method also comprises:

It is the increase conversion coefficient in the zone at center that the computational details strengthens in the image of back with each pixel;

Increase conversion coefficient according to each that calculates each pixel that details strengthens back image correspondence is carried out the enhancing of low noise contrast, finally strengthened the back image.

Said original image is handled, is obtained detail pictures and comprise:

Original image is carried out LPF through low pass filter, obtain low-pass pictures;

Pixel value on original image and the low-pass pictures corresponding pixel points is carried out the high-pass image that additive operation obtains, as detail pictures.

Said original image is handled, is obtained detail pictures and comprise: original image is carried out high-pass filtering through high pass filter, with the high-pass image that obtains as detail pictures.

Be that the local reinforcing coefficient in the zone at center comprises with each pixel in the said calculating original image: calculate through following formula that (i j) is the local reinforcing coefficient β in the zone at center with each pixel in the original image _Local(i, j),

β _Local(i, j)=β _E(i, j)+β _σ(i, j), wherein,

${\mathrm{\&beta;}}_E(i,j)=\left\{\begin{array}{cc}{\&PartialD;}_E(k_E{E}_{\mathrm{global}}-E_{\mathrm{local}}(i,j)),& E_{\mathrm{local}}(i,j)<k_E{E}_{\mathrm{global}}\\ 0,& E_{\mathrm{local}}(i,j)\&GreaterEqual;k_E{E}_{\mathrm{global}}\end{array}\right.,$

${\mathrm{\&beta;}}_{\mathrm{\&sigma;}}(i,j)=\left\{\begin{array}{cc}{\&PartialD;}_{\mathrm{\&sigma;}}({\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)-k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2),& {\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)>k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2\\ 0,& {\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)\&le;k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2\end{array}\right.,$

Said E _GlobalBe the overall average of original image, said

Be the overall variance of original image, said E _Local(i, j) be the original image pixels point (i, local mean value j), said

For the original image pixels point (i, local variance j), said

For image light and shade weight coefficient, said

Be the image detail weight coefficient, said k _EBe the threshold value coefficient of image light and shade, said k _σThreshold value coefficient for image detail information;

The detail pictures that said basis obtains strengthens each corresponding pixel of original image with the local reinforcing coefficient of each that calculates, and obtains details enhancing back image and comprises: calculate details through following formula and strengthen the back image I _Detail,

I _Detail(i，j)＝I _orig(i，j)+β _globalβ _local(i，j)I _high(i，j)，

Wherein, said I _Detail(i j) strengthens the back image I for details _DetailPixel (i, pixel value j), said I _Orig(i j) is original image pixels point (i, pixel value j), said β _GlobalBe overall reinforcing coefficient, its value is selected according to image actual needs enhanced strength, said β _Local(i j) is original image pixels point (i, local reinforcing coefficient j), said I _High(i j) is detail pictures pixel (i, pixel value j);

It is that the increase conversion coefficient in the zone at center comprises that the said computational details strengthens in the image of back with each pixel: calculate details through following formula and strengthen the back image I _DetailIn with each pixel (i, j) be the increase conversion coefficient γ that strengthens of the low noise contrast in the zone at center (i, j),

$\mathrm{\&gamma;}(i,j)=\frac{\underset{k=-M}{\overset{M}{\mathrm{\&Sigma;}}}\underset{m=-N}{\overset{N}{\mathrm{\&Sigma;}}}\frac{f\left(I_{\mathrm{Detail}}(i+k,j+m)\right)}{I_{\mathrm{Detail}}(i+k,j+m)}\&CenterDot;\mathrm{\&delta;}\left(\right|I_{\mathrm{Detail}}(i,j)-I_{\mathrm{Detail}}(i+k,j+m)\left|\right)}{\underset{k=-M}{\overset{M}{\mathrm{\&Sigma;}}}\underset{m=-N}{\overset{N}{\mathrm{\&Sigma;}}}\mathrm{\&delta;}\left(\right|I_{\mathrm{Detail}}(i,j)-I_{\mathrm{Detail}}(i+k,j+m)\left|\right)},$

Wherein, (i is j) apart from its region horizontal boundary distance for M remarked pixel point; N remarked pixel point (i; J) apart from the vertical frontier distance in its region, said f () strengthens function for contrast, and said δ () is the weight counting function; Said k is the side-play amount of image level direction, and said m is the side-play amount of image vertical direction;

Each increase conversion coefficient that said basis calculates strengthens back each corresponding pixel of image to details and carries out the enhancing of low noise contrast, and image comprises after finally being strengthened: calculate final enhancing back image I through following formula _Enhancement,

I _Enhancement(i, j)=γ (i, j) I _Detai(i, j), wherein, said I _Enhancement(i is j) for strengthening back image slices vegetarian refreshments (i, pixel value j).

A kind of image intensifier device, this device comprises: treatment of details unit, first computing unit and first enhancement unit, wherein,

Said treatment of details unit is used for original image is handled, and obtains detail pictures;

Said first computing unit is used for calculating the local reinforcing coefficient that original image is the zone at center with each pixel;

Said first enhancement unit, each local reinforcing coefficient that the detail pictures that is used for obtaining according to the treatment of details unit and first computing unit calculate strengthens each corresponding pixel of original image, and obtain details and strengthen the back image,

Wherein, said first computing unit comprises: overall mean value computation unit, overall variance computing unit, local mean value computing unit, local variance computing unit and first computation subunit, wherein,

Said overall mean value computation unit is used for through formula

Calculate the overall average E of original image _Global, wherein, said r is any pixel value of pixel in the original image, and said p (r) is the probability that the pixel of r occurs in original image for pixel value, and said L and H are respectively minimum pixel value and the max pixel value in the original image;

Said overall variance computing unit is used for calculating through formula

the overall variance

of original image

Said local mean value computing unit is used for through formula $E_{\mathrm{Local}}(i,j)=\frac{1}{(2M+1)\&times;(2N+1)}\underset{k=-M}{\overset{M}{\mathrm{\&Sigma;}}}\underset{m=-N}{\overset{N}{\mathrm{\&Sigma;}}}{I}_{\mathrm{Orig}}(i+k,j+m)$ Calculate original image pixels point (i, local mean value E j) _Local(i, j), wherein, M remarked pixel point (i, j) apart from its region horizontal boundary distance, (i is j) apart from the vertical frontier distance in its region for N remarked pixel point;

Said local variance computing unit is used for through formula ${\mathrm{\&sigma;}}_{\mathrm{Local}}^2(i,j)=\frac{1}{(2M+1)\&times;(2N+1)}\underset{k=-M}{\overset{M}{\mathrm{\&Sigma;}}}\underset{m=-N}{\overset{N}{\mathrm{\&Sigma;}}}{(I_{\mathrm{Orig}}(i+k,j+m)-E_{\mathrm{Local}}(i,j))}^2$ Calculate original image pixels point (i, local variance j)

Said k is the side-play amount of image level direction, and said m is the side-play amount of image vertical direction;

Said first computation subunit is used for the overall average E according to the original image that calculates _Global, overall variance

Pixel (i, local mean value E j) _Local(i, j) and pixel (i, local variance j)

Calculate through following formula that (i j) is the local reinforcing coefficient β in the zone at center with each pixel in the original image _Local(i, j),

β _Local(i, j)=β _E(i, j)+β _σ(i, j), wherein,

${\mathrm{\&beta;}}_E(i,j)=\left\{\begin{array}{cc}{\&PartialD;}_E(k_E{E}_{\mathrm{global}}-E_{\mathrm{local}}(i,j)),& E_{\mathrm{local}}(i,j)<k_E{E}_{\mathrm{global}}\\ 0,& E_{\mathrm{local}}(i,j)\&GreaterEqual;k_E{E}_{\mathrm{global}}\end{array}\right.,$

${\mathrm{\&beta;}}_{\mathrm{\&sigma;}}(i,j)=\left\{\begin{array}{cc}{\&PartialD;}_{\mathrm{\&sigma;}}({\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)-k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2),& {\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)>k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2\\ 0,& {\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)\&le;k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2\end{array}\right.,$

Said

For image light and shade weight coefficient, said

Be the image detail weight coefficient, said k _EBe the threshold value coefficient of image light and shade, said k _σThreshold value coefficient for image detail information;

Said first enhancement unit is used for calculating details through following formula and strengthens the back image I _Detail,

I _Detail(i，j)＝I _orig(i，j)+β _globalβ _local(i，j)I _high(i，j)，

Wherein, said I _Detail(i j) strengthens back image slices vegetarian refreshments (i, pixel value j), said I for details _Orig(i j) is original image pixels point (i, pixel value j), said β _GlobalBe overall reinforcing coefficient, its value is selected according to image actual needs enhanced strength, said β _Local(i j) is original graph image slices vegetarian refreshments (i, local reinforcing coefficient j), said I _High(i j) is detail pictures pixel (i, pixel value j).

This device also comprises: second computing unit and second enhancement unit, wherein,

Said second computing unit, being used for calculating details that said first enhancement unit obtains, to strengthen the back image be the increase conversion coefficient in the zone at center with each pixel;

Said second enhancement unit; Each that is used for calculating according to second computing unit increases conversion coefficient each corresponding pixel of details enhancing back image that said first enhancement unit obtains is carried out the enhancing of low noise contrast, is finally strengthened the back image.

Said treatment of details unit comprises that low pass is handled subelement and high pass is handled subelement, wherein,

Said low pass is handled subelement, is used for original image is carried out LPF through low pass filter, obtains low-pass pictures;

Said high pass is handled subelement, is used for original image and the pixel value handled on the low-pass pictures corresponding pixel points that subelement obtains through low pass are carried out the high-pass image that additive operation obtains, as detail pictures.

Said treatment of details unit comprises that high pass handles subelement, be used for original image is carried out high-pass filtering through high pass filter, with the high-pass image that obtains as detail pictures.

Said first computing unit comprises: overall mean value computation unit, overall variance computing unit, local mean value computing unit, local variance computing unit and first computation subunit, wherein,

Said overall mean value computation unit is used for through formula

Calculate the overall average E of original image _Global, wherein, said r is any pixel value of pixel in the original image, and said p (r) is the probability that the pixel of r occurs in original image for pixel value, and said L and H are respectively minimum pixel value and the max pixel value in the original image;

Said overall variance computing unit is used for calculating through formula

the overall variance

of original image

Said local mean value computing unit is used for through formula $E_{\mathrm{Local}}(i,j)=\frac{1}{(2M+1)\&times;(2N+1)}\underset{k=-M}{\overset{M}{\mathrm{\&Sigma;}}}\underset{m=-N}{\overset{N}{\mathrm{\&Sigma;}}}{I}_{\mathrm{Orig}}(i+k,j+m)$ Calculate original image pixels point (i, local mean value E j) _Local(i, j), wherein, M remarked pixel point (i, j) apart from its region horizontal boundary distance, N remarked pixel point (i, j) apart from the vertical frontier distance in its region, said k is the side-play amount of image level direction, said m is the side-play amount of image vertical direction;

Said local variance computing unit is used for through formula ${\mathrm{\&sigma;}}_{\mathrm{Local}}^2(i,j)=\frac{1}{(2M+1)\&times;(2N+1)}\underset{k=-M}{\overset{M}{\mathrm{\&Sigma;}}}\underset{m=-N}{\overset{N}{\mathrm{\&Sigma;}}}{(I_{\mathrm{Orig}}(i+k,j+m)-E_{\mathrm{Local}}(i,j))}^2$ Calculate original image pixels point (i, local variance j)

Said first computation subunit is used for the overall average E according to the original image that calculates _Global, overall variance

Pixel (i, local mean value E j) _Local(i, j) and pixel (i, local variance j)

Calculate through following formula that (i j) is the local reinforcing coefficient β in the zone at center with each pixel in the original image _Local(i, j),

β _Local(i, j)=β _E(i, j)+β _σ(i, j), wherein,

${\mathrm{\&beta;}}_E(i,j)=\left\{\begin{array}{cc}{\&PartialD;}_E(k_E{E}_{\mathrm{global}}-E_{\mathrm{local}}(i,j)),& E_{\mathrm{local}}(i,j)<k_E{E}_{\mathrm{global}}\\ 0,& E_{\mathrm{local}}(i,j)\&GreaterEqual;k_E{E}_{\mathrm{global}}\end{array}\right.,$

${\mathrm{\&beta;}}_{\mathrm{\&sigma;}}(i,j)=\left\{\begin{array}{cc}{\&PartialD;}_{\mathrm{\&sigma;}}({\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)-k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2),& {\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)>k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2\\ 0,& {\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)\&le;k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2\end{array}\right.,$

Said For image light and shade weight coefficient, said

Be the image detail weight coefficient, said k _EBe the threshold value coefficient of image light and shade, said k _σThreshold value coefficient for image detail information;

Said first enhancement unit is used for calculating details through following formula and strengthens the back image I _Detail,

I _Detail(i，j)＝I _orig(i，j)+β _globalβ _local(i，j)I _high(i，j)，

Wherein, said I _Detail(i j) strengthens back image slices vegetarian refreshments (i, pixel value j), said I for details _Orig(i j) is original image pixels point (i, pixel value j), said β _GlobalBe overall reinforcing coefficient, its value is selected according to image actual needs enhanced strength, said β _Local(i j) is original graph image slices vegetarian refreshments (i, local reinforcing coefficient j), said I _High(i j) is detail pictures pixel (i, pixel value j);

Said second computing unit, be used for through following formula calculate details that said first enhancement unit obtains strengthen the back image with each pixel (i, j) be the center the zone the enhancing of low noise contrast increase conversion coefficient γ (i, j),

$\mathrm{\&gamma;}(i,j)=\frac{\underset{k=-M}{\overset{M}{\mathrm{\&Sigma;}}}\underset{m=-N}{\overset{N}{\mathrm{\&Sigma;}}}\frac{f\left(I_{\mathrm{Detail}}(i+k,j+m)\right)}{I_{\mathrm{Detail}}(i+k,j+m)}\&CenterDot;\mathrm{\&delta;}\left(\right|I_{\mathrm{Detail}}(i,j)-I_{\mathrm{Detail}}(i+k,j+m)\left|\right)}{\underset{k=-M}{\overset{M}{\mathrm{\&Sigma;}}}\underset{m=-N}{\overset{N}{\mathrm{\&Sigma;}}}\mathrm{\&delta;}\left(\right|I_{\mathrm{Detail}}(i,j)-I_{\mathrm{Detail}}(i+k,j+m)\left|\right)},$

Wherein, said f () strengthens function for contrast, and said δ () is the weight counting function;

Said second enhancement unit is used for calculating final enhancing back image I through following formula _Enhancement,

I _Enhancement(i, j)=γ (i, j) I _Detail(i, j), wherein, said I _Enhancement(i is j) for strengthening back image slices vegetarian refreshments (i, pixel value j).

In sum; The image enchancing method that the present invention adopted; Be through original image being handled, obtained detail pictures, and calculate in the original image be the local reinforcing coefficient in zone at center with each pixel after; With each the local reinforcing coefficient that calculates each corresponding pixel of original image being carried out details according to the detail pictures that obtains strengthens; Obtain details and strengthen the back image, also just realized enhancing, improved the intellectual analysis accuracy for processing detailed information such as edge of image.

Description of drawings

Fig. 1 is the structural representation of existing video monitoring system;

Fig. 2 is the workflow diagram of image enchancing method embodiment of the present invention;

Fig. 3 is the structural representation of image intensifier device embodiment of the present invention.

Embodiment

In order to solve the problem that exists in the prior art; The present invention proposes a kind of image enchancing method; Promptly, obtain detail pictures through original image is handled, and calculate in the original image be the local reinforcing coefficient in zone at center with each pixel after; With each the local reinforcing coefficient that calculates each corresponding pixel of original image is carried out details according to the detail pictures that obtains and strengthen, obtain details and strengthen the back image.

Based on above-mentioned introduction, the concrete realization of scheme according to the invention comprises:

Original image is handled, obtained detail pictures; Calculating in the original image with each pixel is the local reinforcing coefficient in the zone at center; According to the detail pictures that obtains and each the local reinforcing coefficient that calculates each corresponding pixel of original image is strengthened, obtain details and strengthen the back image.

For making the object of the invention, technical scheme and advantage clearer, the present invention is done to describe in detail further below in conjunction with accompanying drawing and specific embodiment.

Fig. 2 is the workflow diagram of image enchancing method embodiment of the present invention.As shown in Figure 2, this flow process comprises:

Step 201: original image is handled, obtained detail pictures.

Need to prove, in this step, image is handled that obtaining detail pictures can carry out through following dual mode, is specially:

Mode one: at first, original image is carried out LPF through low pass filter, obtain low-pass pictures; Then, the pixel value on original image and the low-pass pictures corresponding pixel points is carried out the high-pass image that additive operation obtains, as detail pictures.

In this execution mode, suppose I _Orig(i j) is original image I _Orig(i, the pixel value on j) is to original image I at pixel _OrigThrough low pass filter F _{Low_filter}After () carried out LPF, the low-pass pictures I that obtains _LowCan be through following formulae express:

I _low(i，j)＝F _{low_filter}(I _orig(i，j))，

Wherein, I _Low(i j) is low-pass pictures I _LowAt pixel (i, the pixel value on j), the low pass filter F that is adopted _{Low_filter}() can be Laplace filter, also can be other low pass filters, and concrete how selective filter is as the criterion with the realization that does not influence the embodiment of the invention.

Obtaining low-pass pictures I _LowAfter, can calculate high-pass image I through following formula _High, i.e. detail pictures in this step:

I _high(i，j)＝I _orig(i，j)-I _low(i，j)，

Wherein, I _High(i j) is high-pass image I _HighAt pixel (i, the pixel value on j).

Mode two: directly original image is carried out high-pass filtering through high pass filter, with the high-pass image that obtains as detail pictures.

In this execution mode, suppose I _Orig(i j) is original image I _Orig(i, the pixel value on j) is to original image I at pixel _OrigThrough high pass filter F _{High_filter}After () carried out high-pass filtering, the high-pass image I that obtains _HighCan be directly through following formulae express:

I _high(i，j)＝F _{high_filter}(I _orig(i，j))，

Wherein, the high pass filter F that is adopted _{High_filter}() can be selected according to application, is as the criterion with the realization that does not influence the embodiment of the invention.

Step 202: calculating in the original image with each pixel is the local reinforcing coefficient in the zone at center.

In this step, in the original image so that (i j) is the local reinforcing coefficient β in the zone at center more arbitrarily _Local(i j) is overall average E according to original image _Global, overall variance

Pixel (i, local mean value E j) _Local(i, j) and pixel (i, local variance j)

Obtain, its concrete computational process can be carried out through following formula:

β _Local(i, j)=β _E(i, j)+β _σ(i, j), wherein,

${\mathrm{\&beta;}}_E(i,j)=\left\{\begin{array}{cc}{\&PartialD;}_E(k_E{E}_{\mathrm{global}}-E_{\mathrm{local}}(i,j)),& E_{\mathrm{local}}(i,j)<k_E{E}_{\mathrm{global}}\\ 0,& E_{\mathrm{local}}(i,j)\&GreaterEqual;k_E{E}_{\mathrm{global}}\end{array}\right.,$

${\mathrm{\&beta;}}_{\mathrm{\&sigma;}}(i,j)=\left\{\begin{array}{cc}{\&PartialD;}_{\mathrm{\&sigma;}}({\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)-k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2),& {\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)>k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2\\ 0,& {\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)\&le;k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2\end{array}\right.,$

Wherein, local mean value E _Local(i, j) reflection is that (i j) is the bright-dark degree in the zone at center, if E with pixel in the original image _Local(i, j)＜k _EE _Global, represent that then (i is a dark areas for the zone at center j) with pixel; Otherwise, then represent with pixel (i, j) for the zone at center is a bright area, said k _EBe the threshold value coefficient of image light and shade, and 0.01＜k _E≤0.50.Local variance

The reflection be in the original image with pixel (i, j) be the center the zone detailed information what, as if

Then represent with pixel (i is that the regional detailed information at center is more j), otherwise, represent that then (i is that the regional detailed information at center is less j), the threshold value coefficient that said k σ is an image detail information, and 1.0＜k with pixel _σ≤5.0.Said Be image light and shade weight coefficient, and Said Be the image detail weight coefficient, and $0.2\&le;{\&PartialD;}_{\mathrm{\&sigma;}}\&le;0.7.$

Need to prove, in this step, the overall average E of original image _GlobalCan calculate through following formula:

$E_{\mathrm{global}}=\underset{r=L}{\overset{H}{\mathrm{\&Sigma;}}}\mathrm{rp}\left(r\right),$

Wherein, said r is any pixel value of pixel in the original image, and said p (r) is the probability that the pixel of r occurs in original image for pixel value; Said L and H are respectively minimum pixel value and the max pixel value in the original image; Generally speaking, L=0, H=255.

The overall variance of original image

can calculate through following formula

${\mathrm{\&sigma;}}_{\mathrm{global}}^2=\underset{r=L}{\overset{H}{\mathrm{\&Sigma;}}}{(r-E_{\mathrm{global}})}^{2}p\left(r\right).$

Original image pixels point (i, local mean value E j) _Local(i j) can calculate through following formula,

$E_{\mathrm{local}}(i,j)=\frac{1}{(2M+1)\&times;(2N+1)}\underset{k=-M}{\overset{M}{\mathrm{\&Sigma;}}}\underset{m=-N}{\overset{N}{\mathrm{\&Sigma;}}}{I}_{\mathrm{orig}}(i+k,j+m),$

Wherein, M remarked pixel point (i, j) apart from its region horizontal boundary distance, and 0≤M≤flow (W _Pic/ 2); N remarked pixel point (i, j) apart from the vertical frontier distance in its region, and 0≤N≤flow (H _Pic/ 2), W wherein _PicWide for image, H _PicBe the height of image, said k is the side-play amount of image level direction, and said m is the side-play amount of image vertical direction.

Original image pixels point (i; J) local variance can calculate through following formula

${\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)=\frac{1}{(2M+1)\&times;(2N+1)}\underset{k=-M}{\overset{M}{\mathrm{\&Sigma;}}}\underset{m=-N}{\overset{N}{\mathrm{\&Sigma;}}}{(I_{\mathrm{orig}}(i+k,j+m)-E_{\mathrm{local}}(i,j))}^2.$

What also need explain is, above-mentioned steps 201 is the processes that obtain detail pictures, and step 202 is processes of computational details reinforcing coefficient, and they are different processing procedures, not the branch of sequencing.

Step 203: according to the detail pictures that obtains and each the local reinforcing coefficient that calculates each corresponding pixel of original image is strengthened, obtain details and strengthen the back image.

After execution of step 201 and step 202, can be according to the detail pictures I that obtains in the step 201 _HighWith the pixel that obtains in the step 202 (i, local reinforcing coefficient β j) _Local(i, (i j) strengthens j) corresponding to original image pixel, obtains details and strengthens the back image I _DetailPixel (i, pixel value j) specifically can calculate through following formula,

I _Detail(i，j)＝I _orig(i，j)+β _globalβ _local(i，j)I _high(i，j)，

Wherein, β _GlobalBe overall reinforcing coefficient, and 0.01≤β _Global≤1.0, in this step, can select β according to image actual needs enhanced strength _Global, β _GlobalBe worth greatly more, it is strong more that expression strengthens intensity.

Step 204: it is the increase conversion coefficient in the zone at center that the computational details strengthens in the image of back with each pixel.

Calculating details enhancing back image I _DetailAfter, in order further to improve the subjective quality of image, can strengthen the back image to details further and carry out the enhancing of low noise contrast, in this step, the details that is adopted strengthens the back image I _Detail(i, j) in each pixel (i, j) be the increase conversion coefficient γ that strengthens of the low noise contrast in the zone at center (i j) can calculate through following formula,

$\mathrm{\&gamma;}(i,j)=\frac{\underset{k=-M}{\overset{M}{\mathrm{\&Sigma;}}}\underset{m=-N}{\overset{N}{\mathrm{\&Sigma;}}}\frac{f\left(I_{\mathrm{Detail}}(i+k,j+m)\right)}{I_{\mathrm{Detail}}(i+k,j+m)}\&CenterDot;\mathrm{\&delta;}\left(\right|I_{\mathrm{Detail}}(i,j)-I_{\mathrm{Detail}}(i+k,j+m)\left|\right)}{\underset{k=-M}{\overset{M}{\mathrm{\&Sigma;}}}\underset{m=-N}{\overset{N}{\mathrm{\&Sigma;}}}\mathrm{\&delta;}\left(\right|I_{\mathrm{Detail}}(i,j)-I_{\mathrm{Detail}}(i+k,j+m)\left|\right)},$

Wherein, said f () strengthens function for contrast, and said δ () is the weight counting function, and they satisfy following form respectively:

$f\left(x\right)=\left\{\begin{array}{cc}{\mathrm{\&lambda;}}_1\left(x\right)& (L\&le;x\&le;T_L)\\ {\mathrm{\&lambda;}}_2\left(x\right)& (T_L<x<T_H)\\ {\mathrm{\&lambda;}}_3\left(x\right)& (T_H\&le;x\&le;H)\end{array}\right.,\mathrm{\&delta;}\left(x\right)=\left\{\begin{array}{cc}{\mathrm{\&lambda;}}_4\left(x\right)& (0\&le;x\&le;T)\\ 0& (x>T)\end{array}\right.,$

Wherein, T _LAnd T _HBe a given in advance threshold value, its scope is [L, H], λ ₁(x) be [L, T _L] the interior monotonically increasing function of scope, λ ₁(L)=and L, λ ₁(T _L)=R _L, and R _L∈ [L, T _L); λ ₂(x) be [T _L, T _H] the interior monotonically increasing function of scope, λ ₂(T _L)=R _L, λ ₁(T _H)=R _H, and R _H∈ (T _H, H]; λ ₃(x) be [T _H, H] and the interior monotonically increasing function of scope, λ ₃(T _H)=R _H, λ ₃(H)=H; λ ₄(x) be the monotonic decreasing function in [0, T] scope, λ ₄(0)=1, λ ₄(T)=and P, said P is given in advance threshold value, and P ∈ [0, (H-L)].

Step 205: increase conversion coefficient according to each that calculates each pixel that details strengthens back image correspondence is carried out the enhancing of low noise contrast, finally strengthened the back image.

In this step, can calculate final enhancing back image I through following formula _Enhancement,

I _Enhancement(i, j)=γ (i, j) I _Detail(i, j), wherein, said I _Enhancement(i is j) for strengthening back image slices vegetarian refreshments (i, pixel value j).

So far, promptly accomplished the whole workflow of image enchancing method embodiment of the present invention.

Need to prove, in the present embodiment, the only operation of execution in step 201-203, and the image that step 203 is obtained is as the final back image that strengthens; The image that also can step 205 be obtained specifically is as the criterion with practical application as the final back image that strengthens.

Based on said method, Fig. 3 has provided the image intensifier device that the present invention adopted, and is as shown in Figure 3, and this device comprises: treatment of details unit 31, first computing unit 32 and first enhancement unit 33, wherein,

Said treatment of details unit 31 is used for original image is handled, and obtains detail pictures.

Further, said treatment of details unit 31 can comprise that again low pass is handled subelement 311 and high pass is handled subelement 312, wherein,

Said low pass is handled subelement 311, is used for original image is carried out LPF through low pass filter, obtains low-pass pictures;

Said high pass is handled subelement 312, is used for original image and the pixel value handled on the low-pass pictures corresponding pixel points that subelement 311 obtains through low pass are carried out the high-pass image that additive operation obtains, as detail pictures.

Said treatment of details unit 31 also can include only high pass and handle subelement 312, be used for directly original image being carried out high-pass filtering through high pass filter, with the high-pass image that obtains as detail pictures.

Said first computing unit 32 is used for calculating the local reinforcing coefficient that original image is the zone at center with each pixel.

Further, said first computing unit 32 can comprise overall mean value computation unit 321, overall variance computing unit 322, local mean value computing unit 323, local variance computing unit 324 and first computation subunit 325 again, wherein,

Said overall mean value computation unit 321 is used for through formula

Calculate the overall average E of original image _Global, wherein, said r is any pixel value of pixel in the original image, and said p (r) is the probability that the pixel of r occurs in original image for pixel value, and said L and H are respectively minimum pixel value and the max pixel value in the original image;

Said overall variance computing unit 322 is used for calculating through formula

the overall variance of original image

Said local mean value computing unit 323 is used for through formula $E_{\mathrm{Local}}(i,j)=\frac{1}{(2M+1)\&times;(2N+1)}\underset{k=-M}{\overset{M}{\mathrm{\&Sigma;}}}\underset{m=-N}{\overset{N}{\mathrm{\&Sigma;}}}{I}_{\mathrm{Orig}}(i+k,j+m)$ Calculate original image pixels point (i, local mean value E j) _Local(i, j), wherein, M remarked pixel point (i, j) apart from its region horizontal boundary distance, and 0≤M≤flow (W _Pic/ 2); N remarked pixel point (i, j) apart from the vertical frontier distance in its region, and 0≤N≤flow (H _Pic/ 2), W wherein _PicWide for image, H _PicHeight for image;

Said local variance computing unit 324 is used for through formula ${\mathrm{\&sigma;}}_{\mathrm{Local}}^2(i,j)=\frac{1}{(2M+1)\&times;(2N+1)}\underset{k=-M}{\overset{M}{\mathrm{\&Sigma;}}}\underset{m=-N}{\overset{N}{\mathrm{\&Sigma;}}}{(I_{\mathrm{Orig}}(i+k,j+m)-E_{\mathrm{Local}}(i,j))}^2$ Calculate original image pixels point (i, local variance j)

Said first computation subunit 325 is used for the overall average E according to the original image that calculates _Global, overall variance Pixel (i, local mean value E j) _Local(i, j) and pixel (i, local variance j)

Calculate through following formula that (i j) is the local reinforcing coefficient β in the zone at center with each pixel in the original image _Local(i, j),

β _Local(i, j)=β _E(i, j)+β _σ(i, j), wherein,

${\mathrm{\&beta;}}_E(i,j)=\left\{\begin{array}{cc}{\&PartialD;}_E(k_E{E}_{\mathrm{global}}-E_{\mathrm{local}}(i,j)),& E_{\mathrm{local}}(i,j)<k_E{E}_{\mathrm{global}}\\ 0,& E_{\mathrm{local}}(i,j)\&GreaterEqual;k_E{E}_{\mathrm{global}}\end{array}\right.,$

${\mathrm{\&beta;}}_{\mathrm{\&sigma;}}(i,j)=\left\{\begin{array}{cc}{\&PartialD;}_{\mathrm{\&sigma;}}({\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)-k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2),& {\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)>k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2\\ 0,& {\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)\&le;k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2\end{array}\right.,$

Said k _EBe the threshold value coefficient of image light and shade, and 0.01＜k _E≤0.50; Said k _σBe the threshold value coefficient of image detail information, and 1.0＜k _σ≤5.0; Said

Be image light and shade weight coefficient, and

Said

Be the image detail weight coefficient, and

Said first enhancement unit 33, each local reinforcing coefficient that the detail pictures that is used for obtaining according to treatment of details unit 31 and first computing unit 32 calculate strengthens each corresponding pixel of original image, obtains details and strengthens the back image.

Further, said first enhancement unit 33 is used for calculating details through following formula and strengthens the back image I _Detail,

I _Detail(i，j)＝I _orig(i，j)+β _globalβ _local(i，j)I _high(i，j)，

Wherein, said I _Detail(i j) strengthens back image slices vegetarian refreshments (i, pixel value j), said I for details _Orig(i j) is original image pixels point (i, pixel value j), said β _GlobalBe overall reinforcing coefficient, and 0.01≤β _Global≤1.0, select β according to image actual needs enhanced strength _GlobalValue, β _GlobalBe worth greatly more, it is strong more that expression strengthens intensity, said β _Local(i j) is original graph image slices vegetarian refreshments (i, local reinforcing coefficient j), said I _High(i j) is detail pictures pixel (i, pixel value j).

Further, this device also can comprise second computing unit 34 and second enhancement unit 35, wherein,

Said second computing unit 34, being used for calculating details that said first enhancement unit 33 obtains, to strengthen the back image be the increase conversion coefficient in the zone at center with each pixel.

Further; Said second computing unit 34 is used for strengthening the back image (i j) be the increase conversion coefficient γ (i of low noise contrast enhancing in the zone at center with each pixel through the details that obtains that following formula calculates that said first enhancement unit 33 obtains; J)

$\mathrm{\&gamma;}(i,j)=\frac{\underset{k=-M}{\overset{M}{\mathrm{\&Sigma;}}}\underset{m=-N}{\overset{N}{\mathrm{\&Sigma;}}}\frac{f\left(I_{\mathrm{Detail}}(i+k,j+m)\right)}{I_{\mathrm{Detail}}(i+k,j+m)}\&CenterDot;\mathrm{\&delta;}\left(\right|I_{\mathrm{Detail}}(i,j)-I_{\mathrm{Detail}}(i+k,j+m)\left|\right)}{\underset{k=-M}{\overset{M}{\mathrm{\&Sigma;}}}\underset{m=-N}{\overset{N}{\mathrm{\&Sigma;}}}\mathrm{\&delta;}\left(\right|I_{\mathrm{Detail}}(i,j)-I_{\mathrm{Detail}}(i+k,j+m)\left|\right)},$

Wherein, said f () strengthens function for contrast, and said δ () is the weight counting function, and they satisfy following form respectively:

$f\left(x\right)=\left\{\begin{array}{cc}{\mathrm{\&lambda;}}_1\left(x\right)& (L\&le;x\&le;T_L)\\ {\mathrm{\&lambda;}}_2\left(x\right)& (T_L<x<T_H)\\ {\mathrm{\&lambda;}}_3\left(x\right)& (T_H\&le;x\&le;H)\end{array}\right.,\mathrm{\&delta;}\left(x\right)=\left\{\begin{array}{cc}{\mathrm{\&lambda;}}_4\left(x\right)& (0\&le;x\&le;T)\\ 0& (x>T)\end{array}\right.,$

Wherein, T _LAnd T _HBe a given in advance threshold value, its scope is [L, H], λ ₁(x) be [L, T _L] the interior monotonically increasing function of scope, λ ₁(L)=and L, λ ₁(T _L)=R _L, and R _L∈ [L, T _L); λ ₂(x) be [T _L, T _H] the interior monotonically increasing function of scope, λ ₂(T _L)=R _L, λ ₁(T _H)=R _H, and R _H∈ (T _H, H]; λ ₃(x) be [T _H, H] and the interior monotonically increasing function of scope, λ ₃(T _H)=R _H, λ ₃(H)=H; λ ₄(x) be the monotonic decreasing function in [0, T] scope, λ ₄(0)=1, λ ₄(T)=and P, said P is given in advance threshold value, and P ∈ [0, (H-L)].

Said second enhancement unit 35; Each that is used for calculating according to second computing unit 34 increases conversion coefficient each corresponding pixel of details enhancing back image that said first enhancement unit 33 obtains is carried out the enhancing of low noise contrast, is finally strengthened the back image.

Further, said second enhancement unit 35 is used for calculating final enhancing back image I through following formula _Enhancement,

I _Enhancement(i, j)=γ (i, j) I _Detail(i, j), wherein, said I _Enhancement(i is j) for strengthening back image slices vegetarian refreshments (i, pixel value j).

So far, promptly obtained the image intensifier device that the present invention adopted.The concrete realization flow of device shown in Figure 3 can repeat no more referring to Fig. 2 here.

In a word; The image enchancing method that the present invention adopted; Through original image is handled, obtain detail pictures, and calculate in the original image be the local reinforcing coefficient in zone at center with each pixel after; With each the local reinforcing coefficient that calculates each corresponding pixel of original image being carried out details according to the detail pictures that obtains strengthens; Obtain details and strengthen the back image, also just realized enhancing, improved the intellectual analysis accuracy for processing detailed information such as edge of image

In addition; It is that the increase conversion coefficient in the zone at center calculates that the inventive method can also strengthen in the image of back with each pixel details; And then according to the increase conversion coefficient that calculates details is strengthened the back image and further carry out the low noise contrast and strengthen; Also just strengthen the information of image further, improved the subjective quality of image.

The above is merely preferred embodiment of the present invention, and is in order to restriction the present invention, not all within spirit of the present invention and principle, any modification of being made, is equal to replacement, improvement etc., all should be included within the scope that the present invention protects.

Claims (10)

1. an image enchancing method is characterized in that, this method comprises:

Original image is handled, obtained detail pictures;

Calculating in the original image with each pixel is the local reinforcing coefficient in the zone at center;

According to the detail pictures that obtains and each the local reinforcing coefficient that calculates each corresponding pixel of original image is strengthened, obtains details and strengthen the back image,

Wherein, be that the local reinforcing coefficient in the zone at center comprises with each pixel in the said calculating original image: calculate through following formula that (i j) is the local reinforcing coefficient β in the zone at center with each pixel in the original image _Local(i, j),

β _Local(i, j)=β _E(i, j)+β _σ(i, j), wherein,

${\mathrm{\&beta;}}_E(i,j)=\left\{\begin{array}{cc}{\&PartialD;}_E(k_E{E}_{\mathrm{global}}-E_{\mathrm{local}}(i,j)),& E_{\mathrm{local}}(i,j)<k_E{E}_{\mathrm{global}}\\ 0,& E_{\mathrm{local}}(i,j)\&GreaterEqual;k_E{E}_{\mathrm{global}}\end{array}\right.,$

${\mathrm{\&beta;}}_{\mathrm{\&sigma;}}(i,j)=\left\{\begin{array}{cc}{\&PartialD;}_{\mathrm{\&sigma;}}({\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)-k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2),& {\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)>k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2\\ 0,& {\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)\&le;k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2\end{array}\right.,$

Said E _GlobalBe the overall average of original image, said

Be the overall variance of original image, said E _Local(i, j) be the original image pixels point (i, local mean value j), said For the original image pixels point (i, local variance j), said

For image light and shade weight coefficient, said

Be the image detail weight coefficient, said k _EBe the threshold value coefficient of image light and shade, said k _σThreshold value coefficient for image detail information;

The detail pictures that said basis obtains strengthens each corresponding pixel of original image with the local reinforcing coefficient of each that calculates, and obtains details enhancing back image and comprises: calculate details through following formula and strengthen the back image I _Detail,

I _Detail(i，j)＝I _orig(i，j)+β _globalβ _local(i，j)I _high(i，j)，

Wherein, said I _Detail(i j) strengthens the back image I for details _DetailPixel (i, pixel value j), said I _Orig(i j) is original image pixels point (i, pixel value j), said β _GlobalBe overall reinforcing coefficient, its value is selected according to image actual needs enhanced strength, said β _Local(i j) is original image pixels point (i, local reinforcing coefficient j), said I _High(i j) is detail pictures pixel (i, pixel value j).

2. method according to claim 1 is characterized in that, this method also comprises:

It is the increase conversion coefficient in the zone at center that the computational details strengthens in the image of back with each pixel;

Increase conversion coefficient according to each that calculates each pixel that details strengthens back image correspondence is carried out the enhancing of low noise contrast, finally strengthened the back image.

3. method according to claim 1 and 2 is characterized in that, said original image is handled, and obtains detail pictures and comprises:

Original image is carried out LPF through low pass filter, obtain low-pass pictures;

Pixel value on original image and the low-pass pictures corresponding pixel points is carried out the high-pass image that additive operation obtains, as detail pictures.

4. method according to claim 1 and 2 is characterized in that, said original image is handled, and obtains detail pictures and comprises: original image is carried out high-pass filtering through high pass filter, with the high-pass image that obtains as detail pictures.

5. method according to claim 2; It is characterized in that; Be that the local reinforcing coefficient in the zone at center comprises with each pixel in the said calculating original image: calculate through following formula that (i j) is the local reinforcing coefficient β in the zone at center with each pixel in the original image _Local(i, j),

β _Local(i, j)=β _E(i, j)+β _σ(i, j), wherein,

${\mathrm{\&beta;}}_E(i,j)=\left\{\begin{array}{cc}{\&PartialD;}_E(k_E{E}_{\mathrm{global}}-E_{\mathrm{local}}(i,j)),& E_{\mathrm{local}}(i,j)<k_E{E}_{\mathrm{global}}\\ 0,& E_{\mathrm{local}}(i,j)\&GreaterEqual;k_E{E}_{\mathrm{global}}\end{array}\right.,$

${\mathrm{\&beta;}}_{\mathrm{\&sigma;}}(i,j)=\left\{\begin{array}{cc}{\&PartialD;}_{\mathrm{\&sigma;}}({\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)-k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2),& {\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)>k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2\\ 0,& {\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)\&le;k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2\end{array}\right.,$

Said E _GlobalBe the overall average of original image, said Be the overall variance of original image, said E _Local(i, j) be the original image pixels point (i, local mean value j), said

For the original image pixels point (i, local variance j), said

For image light and shade weight coefficient, said

Be the image detail weight coefficient, said k _EBe the threshold value coefficient of image light and shade, said k _σThreshold value coefficient for image detail information;

The detail pictures that said basis obtains strengthens each corresponding pixel of original image with the local reinforcing coefficient of each that calculates, and obtains details enhancing back image and comprises: calculate details through following formula and strengthen the back image I _Detail,

I _Detail(i，j)＝I _orig(i，j)+β _globalβ _local(i，j)I _high(i，j)，

Wherein, said I _Detail(i j) strengthens the back image I for details _DetailPixel (i, pixel value j), said I _Orig(i j) is original image pixels point (i, pixel value j), said β _GlocalBe overall reinforcing coefficient, its value is selected according to image actual needs enhanced strength, said β _Local(i j) is original image pixels point (i, local reinforcing coefficient j), said I _High(i j) is detail pictures pixel (i, pixel value j);

It is that the increase conversion coefficient in the zone at center comprises that the said computational details strengthens in the image of back with each pixel: calculate details through following formula and strengthen the back image I _DetailIn with each pixel (i, j) be the increase conversion coefficient γ that strengthens of the low noise contrast in the zone at center (i, j),

$\mathrm{\&gamma;}(i,j)=\frac{\underset{k=-M}{\overset{M}{\mathrm{\&Sigma;}}}\underset{m=-N}{\overset{N}{\mathrm{\&Sigma;}}}\frac{f\left(I_{\mathrm{Detail}}(i+k,j+m)\right)}{I_{\mathrm{Detail}}(i+k,j+m)}\&CenterDot;\mathrm{\&delta;}\left(\right|I_{\mathrm{Detail}}(i,j)-I_{\mathrm{Detail}}(i+k,j+m)\left|\right)}{\underset{k=-M}{\overset{M}{\mathrm{\&Sigma;}}}\underset{m=-N}{\overset{N}{\mathrm{\&Sigma;}}}\mathrm{\&delta;}\left(\right|I_{\mathrm{Detail}}(i,j)-I_{\mathrm{Detail}}(i+k,j+m)\left|\right)},$

Wherein, (i is j) apart from its region horizontal boundary distance for M remarked pixel point; N remarked pixel point (i; J) apart from the vertical frontier distance in its region, said f () strengthens function for contrast, and said δ () is the weight counting function; Said k is the side-play amount of image level direction, and said m is the side-play amount of image vertical direction;

Each increase conversion coefficient that said basis calculates strengthens back each corresponding pixel of image to details and carries out the enhancing of low noise contrast, and image comprises after finally being strengthened: calculate final enhancing back image I through following formula _Enhancement,

I _Enhancement(i, j)=γ (i, j) I _Detail(i, j), wherein, said I _Enhancement(i is j) for strengthening back image slices vegetarian refreshments (i, pixel value j).

6. an image intensifier device is characterized in that, this device comprises: treatment of details unit, first computing unit and first enhancement unit, wherein,

Said treatment of details unit is used for original image is handled, and obtains detail pictures;

Said first computing unit is used for calculating the local reinforcing coefficient that original image is the zone at center with each pixel;

Said first enhancement unit, each local reinforcing coefficient that the detail pictures that is used for obtaining according to the treatment of details unit and first computing unit calculate strengthens each corresponding pixel of original image, and obtain details and strengthen the back image,

Wherein, said first computing unit comprises: overall mean value computation unit, overall variance computing unit, local mean value computing unit, local variance computing unit and first computation subunit, wherein,

Said overall mean value computation unit is used for through formula

Calculate the overall average E of original image _Global, wherein, said r is any pixel value of pixel in the original image, and said p (r) is the probability that the pixel of r occurs in original image for pixel value, and said L and H are respectively minimum pixel value and the max pixel value in the original image;

Said overall variance computing unit is used for calculating through formula the overall variance

of original image

Said local mean value computing unit is used for through formula $E_{\mathrm{Local}}(i,j)=\frac{1}{(2M+1)\&times;(2N+1)}\underset{k=-M}{\overset{M}{\mathrm{\&Sigma;}}}\underset{m=-N}{\overset{N}{\mathrm{\&Sigma;}}}{I}_{\mathrm{Orig}}(i+k,j+m)$ Calculate original image pixels point (i, local mean value E j) _Local(i, j), wherein, M remarked pixel point (i, j) apart from its region horizontal boundary distance, (i is j) apart from the vertical frontier distance in its region for N remarked pixel point;

Said local variance computing unit is used for through formula ${\mathrm{\&sigma;}}_{\mathrm{Local}}^2(i,j)=\frac{1}{(2M+1)\&times;(2N+1)}\underset{k=-M}{\overset{M}{\mathrm{\&Sigma;}}}\underset{m=-N}{\overset{N}{\mathrm{\&Sigma;}}}{(I_{\mathrm{Orig}}(i+k,j+m)-E_{\mathrm{Local}}(i,j))}^2$ Calculate original image pixels point (i, local variance j)

Said k is the side-play amount of image level direction, and said m is the side-play amount of image vertical direction;

Said first computation subunit is used for the overall average E according to the original image that calculates _Global, overall variance

Pixel (i, local mean value E j) _Local(i, j) and pixel (i, local variance j) Calculate through following formula that (i j) is the local reinforcing coefficient β in the zone at center with each pixel in the original image _Local(i, j),

β _Local(i, j)=β _E(i, j)+β _σ(i, j), wherein,

${\mathrm{\&beta;}}_E(i,j)=\left\{\begin{array}{cc}{\&PartialD;}_E(k_E{E}_{\mathrm{global}}-E_{\mathrm{local}}(i,j)),& E_{\mathrm{local}}(i,j)<k_E{E}_{\mathrm{global}}\\ 0,& E_{\mathrm{local}}(i,j)\&GreaterEqual;k_E{E}_{\mathrm{global}}\end{array}\right.,$

${\mathrm{\&beta;}}_{\mathrm{\&sigma;}}(i,j)=\left\{\begin{array}{cc}{\&PartialD;}_{\mathrm{\&sigma;}}({\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)-k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2),& {\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)>k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2\\ 0,& {\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)\&le;k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2\end{array}\right.,$

Said

For image light and shade weight coefficient, said Be the image detail weight coefficient, said k _EBe the threshold value coefficient of image light and shade, said k _σThreshold value coefficient for image detail information;

Said first enhancement unit is used for calculating details through following formula and strengthens the back image I _Detail,

I _Detail(i，j)＝I _orig(i，j)+β _globalβ _local(i，j)I _high(i，j)，

Wherein, said I _Detail(i j) strengthens back image slices vegetarian refreshments (i, pixel value j), said I for details _Orig(i j) is original image pixels point (i, pixel value j), said β _GlobalBe overall reinforcing coefficient, its value is selected according to image actual needs enhanced strength, said β _Local(, j) be original graph image slices vegetarian refreshments (i, local reinforcing coefficient j), said I _High(i j) is detail pictures pixel (i, pixel value j).

7. device according to claim 6 is characterized in that, this device also comprises: second computing unit and second enhancement unit, wherein,

Said second computing unit, being used for calculating details that said first enhancement unit obtains, to strengthen the back image be the increase conversion coefficient in the zone at center with each pixel;

Said second enhancement unit; Each that is used for calculating according to second computing unit increases conversion coefficient each corresponding pixel of details enhancing back image that said first enhancement unit obtains is carried out the enhancing of low noise contrast, is finally strengthened the back image.

8. according to claim 6 or 7 described devices, it is characterized in that said treatment of details unit comprises that low pass is handled subelement and high pass is handled subelement, wherein,

Said low pass is handled subelement, is used for original image is carried out LPF through low pass filter, obtains low-pass pictures;

Said high pass is handled subelement, is used for original image and the pixel value handled on the low-pass pictures corresponding pixel points that subelement obtains through low pass are carried out the high-pass image that additive operation obtains, as detail pictures.

9. according to claim 6 or 7 described devices, it is characterized in that said treatment of details unit comprises that high pass handles subelement, be used for original image is carried out high-pass filtering through high pass filter, with the high-pass image that obtains as detail pictures.

10. device according to claim 7 is characterized in that, said first computing unit comprises: overall mean value computation unit, overall variance computing unit, local mean value computing unit, local variance computing unit and first computation subunit, wherein,

Said overall mean value computation unit is used for through formula

Calculate the overall average E of original image _Global, wherein, said r is any pixel value of pixel in the original image, and said p (r) is the probability that the pixel of r occurs in original image for pixel value, and said L and H are respectively minimum pixel value and the max pixel value in the original image;

Said overall variance computing unit is used for calculating through formula

the overall variance

of original image

Said local mean value computing unit is used for through formula $E_{\mathrm{Local}}(i,j)=\frac{1}{(2M+1)\&times;(2N+1)}\underset{k=-M}{\overset{M}{\mathrm{\&Sigma;}}}\underset{m=-N}{\overset{N}{\mathrm{\&Sigma;}}}{I}_{\mathrm{Orig}}(i+k,j+m)$ Calculate original image pixels point (i, local mean value E j) _Local(i, j), wherein, M remarked pixel point (i, j) apart from its region horizontal boundary distance, N remarked pixel point (i, j) apart from the vertical frontier distance in its region, said k is the side-play amount of image level direction, said m is the side-play amount of image vertical direction;

Said local variance computing unit is used for through formula ${\mathrm{\&sigma;}}_{\mathrm{Local}}^2(i,j)=\frac{1}{(2M+1)\&times;(2N+1)}\underset{k=-M}{\overset{M}{\mathrm{\&Sigma;}}}\underset{m=-N}{\overset{N}{\mathrm{\&Sigma;}}}{(I_{\mathrm{Orig}}(i+k,j+m)-E_{\mathrm{Local}}(i,j))}^2$ Calculate original image pixels point (i, local variance j)

Said first computation subunit is used for the overall average E according to the original image that calculates _Global, overall variance

Pixel (i, local mean value E j) _Local(i, j) and pixel (i, local variance j)

Calculate through following formula that (i j) is the local reinforcing coefficient β in the zone at center with each pixel in the original image _Local(i, j),

β _Local(i, j)=β _E(i, j)+β _σ(i, j), wherein,

${\mathrm{\&beta;}}_E(i,j)=\left\{\begin{array}{cc}{\&PartialD;}_E(k_E{E}_{\mathrm{global}}-E_{\mathrm{local}}(i,j)),& E_{\mathrm{local}}(i,j)<k_E{E}_{\mathrm{global}}\\ 0,& E_{\mathrm{local}}(i,j)\&GreaterEqual;k_E{E}_{\mathrm{global}}\end{array}\right.,$

${\mathrm{\&beta;}}_{\mathrm{\&sigma;}}(i,j)=\left\{\begin{array}{cc}{\&PartialD;}_{\mathrm{\&sigma;}}({\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)-k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2),& {\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)>k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2\\ 0,& {\mathrm{\&sigma;}}_{\mathrm{local}}^2(i,j)\&le;k_{\mathrm{\&sigma;}}{\mathrm{\&sigma;}}_{\mathrm{global}}^2\end{array}\right.,$

Said

For image light and shade weight coefficient, said

Be the image detail weight coefficient, said k _EBe the threshold value coefficient of image light and shade, said k _σThreshold value coefficient for image detail information;

Said first enhancement unit is used for calculating details through following formula and strengthens the back image I _Detail,

I _Detail(i，j)＝I _orig(i，j)+β _globalβ _local(i，j)I _high(i，j)，

Wherein, said I _Detail(i j) strengthens back image slices vegetarian refreshments (i, pixel value j), said I for details _Orig(i j) is original image pixels point (i, pixel value j), said β _GlobalBe overall reinforcing coefficient, its value is selected according to image actual needs enhanced strength, said β _Local(i j) is original graph image slices vegetarian refreshments (i, local reinforcing coefficient j), said I _High(i j) is detail pictures pixel (i, pixel value j);

Said second computing unit, be used for through following formula calculate details that said first enhancement unit obtains strengthen the back image with each pixel (i, j) be the center the zone the enhancing of low noise contrast increase conversion coefficient γ (i, j),

$\mathrm{\&gamma;}(i,j)=\frac{\underset{k=-M}{\overset{M}{\mathrm{\&Sigma;}}}\underset{m=-N}{\overset{N}{\mathrm{\&Sigma;}}}\frac{f\left(I_{\mathrm{Detail}}(i+k,j+m)\right)}{I_{\mathrm{Detail}}(i+k,j+m)}\&CenterDot;\mathrm{\&delta;}\left(\right|I_{\mathrm{Detail}}(i,j)-I_{\mathrm{Detail}}(i+k,j+m)\left|\right)}{\underset{k=-M}{\overset{M}{\mathrm{\&Sigma;}}}\underset{m=-N}{\overset{N}{\mathrm{\&Sigma;}}}\mathrm{\&delta;}\left(\right|I_{\mathrm{Detail}}(i,j)-I_{\mathrm{Detail}}(i+k,j+m)\left|\right)},$

Wherein, said f () strengthens function for contrast, and said δ () is the weight counting function;

Said second enhancement unit is used for calculating final enhancing back image I through following formula _Enhancement,

I _Enhancement(i, j)=γ (i, j) I _Detail(i, j), wherein, said I _Enhancement(i is j) for strengthening back image slices vegetarian refreshments (i, pixel value j).

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