CN107895356A - A kind of near-infrared image Enhancement Method based on steerable pyramid - Google Patents
A kind of near-infrared image Enhancement Method based on steerable pyramid Download PDFInfo
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Abstract
The present invention relates to a kind of near-infrared image Enhancement Method based on steerable pyramid.Original image passes through steerable pyramid multi-resolution decompositions, obtains the high frequency coefficient and low frequency coefficient under multiple yardsticks, then carries out out processing to high frequency coefficient and low frequency coefficient respectively, realizes the noise remove of original image and the raising of contrast.The present invention carries out multi-resolution decomposition and reconstruct to original image, each yardstick in decomposable process obtains the high frequency coefficient and low frequency coefficient of multiple directions, high frequency coefficient removes picture noise using threshold method, low frequency coefficient improves picture contrast using fuzzy set nonlinear transformation, then Image Reconstruction is carried out by steerable pyramid inverse transformations, finally realizes that near-infrared image strengthens.
Description
Technical field
The present invention relates to a kind of near-infrared image Enhancement Method based on steerable pyramid, belong to image procossing
Technical field.
Background technology
Near infrared imaging is mainly the near infrared band environment photoimaging reflected using measured target, compared to visible ray into
As having more preferable air penetration performance and human body skin penetrability, therefore near infrared imaging is in military affairs, medical science and numerous industry
All it is widely used in metaplasia production.But limited by production technology, apparatus and method of the prior art are difficult from hardware
The quality of aspect raising near-infrared image, and requirement more and more higher of the practical application to near-infrared image quality, can not meet existing
There is the application requirement of technology；Image is handled by algorithm for image enhancement, improving picture quality turns into mainstream research direction.
The enhancing of near-infrared image is mainly solved the problems, such as to include, and strengthens picture contrast, prominent image detail, and elimination is made an uproar
Several aspects such as sound.Traditional algorithm for image enhancement is divided into spatial domain enhancing and frequency domain enhancing.Spatial domain enhancing directly handles picture
Plain gray value, main method have gray scale stretching, histogram equalization, unsharp mask etc.；Image is first transformed to frequency by frequency domain enhancing
Rate domain, then handle frequency domain figure picture with frequency domain filter and realize enhancing.Simple spatial domain enhancing or frequency domain enhancing can not meet
Existing system should eliminate noise strengthens the requirement of details again.In recent years, for the engineering demand of reality, there are many scientific research works
Author proposes new Infrared Image Enhancement Algorithm, but this kind of method often relies on application scenarios and realizes that function is targeted,
The handsome proposition combination histogram equalization of such as sea of clouds and the infrared image enhancement of fuzzy set theory (sea of clouds is handsome, Wu Zhiyong, crown army,
Deng combinations histogram equalization and [J] the CADs of the infrared image enhancement of fuzzy set theory and graphics journal,
2015,27(8):1498-1505.) mainly improve picture contrast.
The content of the invention
In view of the shortcomings of the prior art, the present invention provides a kind of near-infrared image based on steerable pyramid and increased
Strong method.
Summary of the invention：
The present invention proposes a kind of near-infrared image Enhancement Method based on steerable pyramid.Original image passes through
Steerable pyramid multi-resolution decompositions, the high frequency coefficient and low frequency coefficient under multiple yardsticks are obtained, then respectively to high frequency system
Number and low frequency coefficient carry out out processing, realize the noise remove of original image and the raising of contrast.
Specifically, main technical content of the invention includes, and carries out multi-resolution decomposition and reconstruct to original image, decomposes
During each yardstick obtain the high frequency coefficient and low frequency coefficient of multiple directions, high frequency coefficient removes image using threshold method and made an uproar
Sound, low frequency coefficient improve picture contrast using fuzzy set nonlinear transformation, then pass through steerable pyramid contravariant
Swap-in row Image Reconstruction, finally realize that near-infrared image strengthens.
The technical scheme is that：
A kind of near-infrared image Enhancement Method based on steerable pyramid, including step are as follows：
1) input picture is subjected to steerable pyramid decomposition, obtains the low frequency coefficient under n-th of yardstick and K
The high frequency coefficient in direction；N=1 when first time steerable pyramid is decomposed；
2) threshold method is respectively adopted in the high frequency coefficient in the K direction obtained in step 1) and removes picture noise；I.e. in frequency
Rate domain given threshold, to isolate the noise information of image；Wherein, threshold method result is mainly by threshold size and threshold function table
Two factors determine；
Picture signal coefficient value is generally large in high frequency coefficient, and noise coefficient value is smaller, therefore, by choosing suitable threshold
Value, signal coefficient and noise coefficient can be separated by threshold function table.
3) the down-sampled of the low-frequency image obtained in step 1) progress 2 is obtained into image I1, image I1 resolution ratio reduces
For the 1/4 of original image；N=n+1, repeat step 1), 2) N-1 times；Wherein, image I1 is next layer of steerable
The input picture of pyramid decomposable processes；Set variable m=N；
4) low frequency coefficient of m-th of yardstick is subjected to fuzzy set nonlinear transformation；I.e. in spatial domain to low-frequency image
Pixel value carries out ambiguity function conversion；Low-pass coefficients retain artwork general picture feature, and the present invention is handled each by Fuzzy Set Theory
Yardstick low-pass coefficients, strengthen picture contrast.
5) high frequency coefficient on m-th of yardstick after processing and low frequency coefficient are subjected to steerable pyramid contravariant
Change, reconstruct enhanced image on m-th of yardstick；
6) the enhanced image obtained in step 5) is carried out to 2 up-sampling, obtains the low frequency figure on the m-1 yardstick
Picture；M=m-1；
7) repeat step 4), 5), 6), until obtaining the image with the equal resolution ratio of original image；
8) image obtained in step 7) is subjected to unsharp mask, strengthens image detail, obtain enhanced image.
According to currently preferred, in the step 1), K value includes 2,3 or 4；In the step 3), N value includes
2nd, 3 or 4.
According to currently preferred, the threshold value in the step 2)Wherein, σ is that noise signal is square
Difference, n are steerable pyramid signal length；Noise signal meansquaredeviationσ by the mediant estimation of high frequency coefficient, i.e. σ=
Median (I), wherein, I is image pixel value, and median (I) represents to choose the intermediate value of image pixel value；Selected threshold is minimum
Minimum value in the multiple high frequency coefficient threshold values of yardstick；Because there is the high-frequency information of multiple directions in each yardstick, again by calculating
Obtained threshold value also has multiple, and the present invention chooses that minimum.
The threshold function table that threshold method uses in the step 2) is hard threshold function, and signal value is kept not when being more than threshold value
Become, zero setting during less than threshold value；
According to currently preferred, in the step 4), fuzzy set nonlinear transformation comprises the following steps that：
4.1) image is transformed to by fuzzy field by linear membership function；Linear membership function：
Wherein, xmaxAnd xminRespectively original image pixel maximum and minimum value, xijFor each point pixel value；Linearly it is subordinate to letter
Number normalizes to the pixel value of digital picture between 0 and 1；
4.2) fuzzy contrast is calculated；Fuzzy contrast calculation formula：
WhereinFor μ after the linear membership function conversion of original imageijAverage；
4.3) fuzzy contrast nonlinear transformation；Choose transforming function transformation function ψ (x) stretching gradation of images, Fc'=ψ (Fc)；To letter
Number ψ (x) requirement：ψ (0)=0, ψ (1)=1, ψ (x) >=x x ∈ (0,1)；
4.4) inverse transformation；After the transforming function transformation function processing in step 4.3), fuzzy contrast FcValue by phase strain stretch
And compression；Image pixel value after fuzzy contrast conversion is obtained by following formula inverse transformation；The function of two step inverse transformations is as follows：
xij'=μij'(xmax-xmin)+xmin。
It is further preferred that ψ (x)=4x-6x in the step 4.3)2+4x3-x4。
According to currently preferred, the upsampling process in the step 6) is realized by bilinear interpolation method.
According to currently preferred, in the step 8), unsharp mask algorithm mathematics expression formula：
V=u+ γ (u-w),
Wherein, v is enhanced image, and u is input picture, and w is the result after linear low-pass ripple；γ is weighting system
Number, γ ＞ 0.
It is further preferred that the low pass filter is the L1_2 filters in steerable pyramid under first yardstick
Ripple device.
Beneficial effects of the present invention are：
1. near-infrared image Enhancement Method of the present invention, using the invertibity of steerable pyramid decomposition models,
Original image is decomposed and handled in multiple resolution ratio, while by high and low frequency unpack in each resolution ratio,
The noise and contrast problem of image are handled respectively；Frequency domain Threshold segmentation is used to high frequency imaging in each resolution ratio, it is right
Low-frequency image uses spatial domain fuzzy set nonlinear transformation；The high frequency coefficient of image retains the detailed information and noise of image
Information, low frequency coefficient retain the general picture property feature of original image；By the processing to image high frequency coefficient, image can be filtered out
Noise, the processing to image low frequency coefficient, the contrast of image can be improved；Two kinds of processing are used in combination, effective to realize figure
As denoising and the requirement of raising contrast；
2. near-infrared image Enhancement Method of the present invention, using the polytropism of steerable pyramid decomposition models,
Image texture characteristic is preferably remained, is effectively reduced loss of detail of the image in noise process is removed.
Brief description of the drawings
Fig. 1 is steerable pyramid decomposition and reconstruction block diagrams；
Fig. 2 is low pass filter L1_1 schematic diagrames；
Fig. 3 is high-pass filter H1_1 schematic diagrames；
Fig. 4 is low pass filter L1_2 schematic diagrames；
Fig. 5 is direction bandpass filter B1_1 schematic diagrames；
Fig. 6 is direction bandpass filter B1_2 schematic diagrames；
Fig. 7 is direction bandpass filter B1_3 schematic diagrames；
Fig. 8 is the original image in experiment；
Fig. 9 be it is processed by the invention after image；
Figure 10 is the image after histogram equalization processing；
Figure 11 is the result after gray scale stretching processing；
Figure 12 is the result after bibliography processing in background technology.
Embodiment
With reference to embodiment and Figure of description, the present invention will be further described, but not limited to this.
Embodiment 1
As shown in Figure 1.
A kind of near-infrared image Enhancement Method based on steerable pyramid, at the image shown in Fig. 8
Reason, step are as follows：
1) input picture is subjected to steerable pyramid decomposition, obtains the low frequency coefficient under n-th of yardstick and 3
The high frequency coefficient in direction；N=1 when first time steerable pyramid is decomposed；Steerable pyramid decompose what is used
Wave filter L1_1, H1_1, L1_2, B1_1, B1_2, B1_3 are respectively as shown in Fig. 2-Fig. 7.
2) threshold method is respectively adopted in the high frequency coefficient in obtained in step 1) 3 directions and removes picture noise；I.e. in frequency
Rate domain given threshold, to isolate the noise information of image；
The threshold valueWherein, σ is noise signal mean square deviation, and n is steerable pyramid signal
Length；Noise signal meansquaredeviationσ is by the mediant estimation of high frequency coefficient, i.e. σ=median (I), wherein, I is image pixel value,
Median (I) represents to choose the intermediate value of image pixel value；Selected threshold is the minimum in the multiple high frequency coefficient threshold values of smallest dimension
Value；Because there is the high-frequency information of multiple directions in each yardstick, also there is multiple, present invention selection again by the threshold value being calculated
That minimum.
The threshold function table that the threshold method uses is hard threshold function, and signal value keeps constant when being more than threshold value, less than threshold
Zero setting during value；
Wherein, threshold method result is mainly determined by two factors of threshold size and threshold function table；
Picture signal coefficient value is generally large in high frequency coefficient, and noise coefficient value is smaller, therefore, by choosing suitable threshold
Value, signal coefficient and noise coefficient can be separated by threshold function table.
3) the down-sampled of the low-frequency image obtained in step 1) progress 2 is obtained into image I1, image I1 resolution ratio reduces
For the 1/4 of original image；N=n+1, repeat step 1), 2) 2 times；Wherein, image I1 is next layer of steerable pyramid
The input picture of decomposable process；Set variable m=3；
4) low frequency coefficient of m-th of yardstick is subjected to fuzzy set nonlinear transformation；I.e. in spatial domain to low-frequency image
Pixel value carries out ambiguity function conversion；Low-pass coefficients retain artwork general picture feature, and the present invention is handled each by Fuzzy Set Theory
Yardstick low-pass coefficients, strengthen picture contrast.
Fuzzy set nonlinear transformation comprises the following steps that：
4.1) image is transformed to by fuzzy field by linear membership function；Linear membership function：
Wherein, xmaxAnd xminRespectively original image pixel maximum and minimum value, xijFor each point pixel value；Linearly it is subordinate to letter
Number normalizes to the pixel value of digital picture between 0 and 1；
4.2) fuzzy contrast is calculated；Fuzzy contrast calculation formula：
WhereinFor μ after the linear membership function conversion of original imageijAverage；
4.3) fuzzy contrast nonlinear transformation；Choose transforming function transformation function ψ (x) stretching gradation of images, Fc'=ψ (Fc)；To letter
Number ψ (x) requirement：ψ (0)=0, ψ (1)=1, ψ (x) >=x x ∈ (0,1)；ψ (x)=4x-6x2+4x3-x4。
4.4) inverse transformation；After the transforming function transformation function processing in step 4.3), fuzzy contrast FcValue by phase strain stretch
And compression；Image pixel value after fuzzy contrast conversion is obtained by following formula inverse transformation；The function of two step inverse transformations is as follows：
xij'=μij'(xmax-xmin)+xmin。
5) high frequency coefficient on m-th of yardstick after processing and low frequency coefficient are subjected to steerable pyramid contravariant
Change, reconstruct enhanced image on m-th of yardstick；Inverse transformation is as shown in Fig. 1 right half parts.
6) the enhanced image obtained in step 5) is carried out to 2 up-sampling, obtains the low frequency figure on the m-1 yardstick
Picture；M=m-1；Upsampling process is realized by bilinear interpolation method.
7) repeat step 4), 5), 6), until obtaining the image with the equal resolution ratio of original image；
8) image obtained in step 7) is subjected to unsharp mask, strengthens image detail, obtain enhanced image.
In the step 8), unsharp mask algorithm mathematics expression formula：
V=u+ γ (u-w),
Wherein, v is enhanced image, and u is input picture, and w is the result after linear low-pass ripple；γ is weighting system
Number, γ ＞ 0.γ=1.The low pass filter used in the step is the L1_ in steerable pyramid under first yardstick
2 wave filters.
The image obtained after original image Fig. 8 is processed by the invention is as shown in Figure 9.
Comparative example 1：
Fig. 8 is entered using " with reference to histogram equalization and the infrared image enhancement of fuzzy set theory " method in background technology
Row image enhancement processing；Result is as shown in figure 12.
Comparative example 2：
Image enhancement processing is carried out to Fig. 8 using histogram equalizing method of the prior art；Result such as Figure 10 institutes
Show.
Comparative example 3：
Image enhancement processing is carried out to Fig. 8 using gray scale stretching method of the prior art；Result is as shown in figure 11.
The picture quality that comparison diagram 9- Figure 12 can be seen that after present invention processing is better than other several processing methods.
Several objective evaluation achievement datas of embodiment 1 and comparative example result are as shown in the table：
By the contrast on indices, the image enhaucament in embodiment 1 possess the obvious gain of more high-contrast and
Comentropy, it is advantageous relative to method mean square error in traditional image enchancing method and comparative example 1 and Y-PSNR, say
Image enchancing method positive effect in near-infrared image enhancing processing in bright embodiment 1.
Claims (8)
1. a kind of near-infrared image Enhancement Method based on steerable pyramid, it is characterised in that as follows including step：
1) input picture is subjected to steerable pyramid decomposition, obtains the low frequency coefficient under n-th of yardstick and K direction
High frequency coefficient；N=1 when first time steerable pyramid is decomposed；
2) threshold method is respectively adopted in the high frequency coefficient in the K direction obtained in step 1) and removes picture noise；I.e. in frequency domain
Given threshold, to isolate the noise information of image；
3) the down-sampled of the low-frequency image obtained in step 1) progress 2 is obtained into image I1, image I1 resolution ratio is reduced to original
The 1/4 of image；N=n+1, repeat step 1), 2) N-1 times；Wherein, image I1 is next layer of pyramid points of steerable
The input picture of solution preocess；Set variable m=N；
4) low frequency coefficient of m-th of yardstick is subjected to fuzzy set nonlinear transformation；Pixel i.e. in spatial domain to low-frequency image
Value carries out ambiguity function conversion；
5) high frequency coefficient on m-th of yardstick after processing and low frequency coefficient are subjected to steerable pyramid inverse transformations, weight
Structure goes out enhanced image on m-th of yardstick；
6) the enhanced image obtained in step 5) is carried out to 2 up-sampling, obtains the low-frequency image on the m-1 yardstick；m
=m-1；
7) repeat step 4), 5), 6), until obtaining the image with the equal resolution ratio of original image；
8) image obtained in step 7) is subjected to unsharp mask, strengthens image detail, obtain enhanced image.
2. the near-infrared image Enhancement Method according to claim 1 based on steerable pyramid, its feature exist
In in the step 1), K value includes 2,3 or 4；In the step 3), N value includes 2,3 or 4.
3. the near-infrared image Enhancement Method according to claim 1 based on steerable pyramid, its feature exist
In the threshold value in the step 2)Wherein, σ is noise signal mean square deviation, and n is steerable pyramid
Signal length；Noise signal meansquaredeviationσ is by the mediant estimation of high frequency coefficient, i.e. σ=median (I), wherein, I is image
Pixel value, median (I) represent to choose the intermediate value of image pixel value；
The threshold function table that threshold method uses in the step 2) is hard threshold function, and signal value keeps constant when being more than threshold value, small
Zero setting when threshold value；
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4. the near-infrared image Enhancement Method according to claim 1 based on steerable pyramid, its feature exist
In in the step 4), fuzzy set nonlinear transformation comprises the following steps that：
4.1) image is transformed to by fuzzy field by linear membership function；Linear membership function：
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Contracting；Image pixel value after fuzzy contrast conversion is obtained by following formula inverse transformation；The function of two step inverse transformations is as follows：
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</mtd>
</mtr>
</mtable>
</mfenced>
<mo>,</mo>
</mrow>
xij'=μij′(xmax-xmin)+xmin。
5. the near-infrared image Enhancement Method according to claim 4 based on steerable pyramid, its feature exist
In ψ (x)=4x-6x in the step 4.3)2+4x3-x4。
6. the near-infrared image Enhancement Method according to claim 1 based on steerable pyramid, its feature exist
In the upsampling process in the step 6) is realized by bilinear interpolation method.
7. the near-infrared image Enhancement Method according to claim 1 based on steerable pyramid, its feature exist
In, in the step 8), unsharp mask algorithm mathematics expression formula：
V=u+ γ (u-w),
Wherein, v is enhanced image, and u is input picture, and w is the result after linear low-pass ripple；γ is weight coefficient, γ
＞ 0.
8. the near-infrared image Enhancement Method according to claim 7 based on steerable pyramid, its feature exist
In the low pass filter is the L1_2 wave filters in steerable pyramid under first yardstick.
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