CN107085833B - Remote sensing images filtering method based on the equal intermediate value fusion of gradient inverse self-adaptive switch - Google Patents
Remote sensing images filtering method based on the equal intermediate value fusion of gradient inverse self-adaptive switch Download PDFInfo
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Abstract
The invention discloses the remote sensing images filtering methods based on the equal intermediate value fusion of gradient inverse self-adaptive switch, step 1: obtaining unmanned aerial vehicle remote sensing image；Step 2: choosing the template that size is n × n, find out the gradient value in template center's point and template between remaining (n × n-1) pixel and preservation；Step 3: gradient value obtained in step 2 and threshold value being compared, judge the pixel whether as caused by salt-pepper noise or random noise；Step 4: according to step 3 obtain as a result, if the pixel is caused by salt-pepper noise or random noise, be smoothed with Gradient Inverse Weight algorithm；If the pixel is caused by salt-pepper noise or random noise, denoising is filtered with the equal intermediate value blending algorithm of self-adaptive switch；Step 5: the unmanned aerial vehicle remote sensing image after obtaining filtering and noise reduction by step 4.The present invention overcomes the limitations of existing remote sensing images filtering algorithm scope of application when facing multiple types partition noise.
Description
Technical field
The invention belongs to technical field of image processing, and in particular to one kind is melted based on the equal intermediate value of gradient inverse self-adaptive switch
The remote sensing images filtering method of conjunction.
Background technique
Along with the rapid development of modern space flight and unmanned air vehicle technique, remote sensing technology achieves in recent years advances by leaps and bounds
Development.Unmanned plane is because it is small and exquisite with figure, and the flexible advantage of scouting mode is used widely, wherein to unmanned aerial vehicle remote sensing
The analysis and research of image have become one of the main path that people obtain information.
However, remote sensing images during acquisition and transmission, are influenced inevitable by factors such as sensor and atmosphere
Meeting introduce noise, for obtain clearly, the remote sensing images of high quality, being filtered denoising to remote sensing images is very must
It wants.
When being filtered denoising to unmanned aerial vehicle remote sensing image, traditional remote sensing images filtering is both for having determined
Types noise and select suitable filtering and noise reduction algorithm by the degree of noise jamming, these algorithms are in remote sensing images denoising
Although respectively there is feature, and good filter effect can be obtained, its restricted application has certain limitation, no
It is well positioned to meet the denoising requirement of unmanned aerial vehicle remote sensing image.For unmanned aerial vehicle remote sensing image, due to in-flight vulnerable to appearance
Signal such as is disturbed at the influence of reasons in state interference, the intrinsic speciality of sensing equipment, optical aberration, transmission process, therefore general
Will not can often there be the noise of multiple types distribution in image only by single noise pollution, a plurality of types of make an uproar is being distributed with
When sound, traditional remote sensing images filtering algorithm cannot obtain preferably denoising effect.
Summary of the invention
The purpose of the present invention is to provide a kind of remote sensing images filters based on the equal intermediate value fusion of gradient inverse self-adaptive switch
Wave method, to overcome the problems of the above-mentioned prior art, the present invention to a certain extent, overcomes existing remote sensing images filtering
The limitation of algorithm scope of application when facing multiple types partition noise.
In order to achieve the above objectives, the present invention adopts the following technical scheme:
Remote sensing images filtering method based on the equal intermediate value fusion of gradient inverse self-adaptive switch, comprising the following steps:
Step 1: obtaining unmanned aerial vehicle remote sensing image；
Step 2: choose the template that size is n × n, find out in template center's point and template remaining (n × n-1) pixel it
Between gradient value matrix q and save；
Step 3: gradient value obtained in step 2 and threshold value being compared, judge whether the pixel is made an uproar by the spiced salt
Caused by sound or random noise；
Step 4: according to step 3 obtain as a result, if the pixel is caused by salt-pepper noise or random noise, use
Gradient Inverse Weight algorithm is smoothed；If the pixel is caused by salt-pepper noise or random noise, with adaptive
The equal intermediate value blending algorithm of inductive switch is filtered denoising；
Step 5: the unmanned aerial vehicle remote sensing image after obtaining filtering and noise reduction by step 4.
Further, template center's point is (x, y) in step 2, and the gray value of template center's point is f (x, y), is found out respectively
Gradient value between f (x, y) and template remaining (n × n-1) a pixel.
Further, given threshold T=f (x, y) × 10% in step 3, if at least there is pixel (x+i, a y+
J), so that | f (x+i, y+j)-f (x, y) |≤T, then judging pixel (x, y) not is caused by salt-pepper noise or random noise；
If all pixels point (x+i, y+j), so that | f (x+i, y+j)-f (x, y) | > T then judges that pixel (x, y) is made an uproar by the spiced salt
Caused by sound or random noise.
Further, in step 4 when the pixel is caused by salt-pepper noise or random noise, Gradient Inverse Weight is used
Algorithm is smoothed, specifically:
For the template of n × n, matrix q that step 2 obtains are as follows:
Weight matrix w is obtained by matrix q:
If f (x+j, y+j)=f (x, y), then gradient value is 0, it is specified that central element w (x, y)=0.5, remaining n*n-1
The sum of weighted elements are 0.5, so that w each element summation is equal to 1, then have:
Wherein, i, j=-1,0 or 1, but i, j are not 0 simultaneously；
Finally, at each pixel by template elements with it corresponding to weight correspondence be multiplied, then sum and be somebody's turn to do
Pixel is using gradient smoothed out output g (x, y) reciprocal.
Further, the pixel is caused by salt-pepper noise or random noise in step 4, then with self-adaptive switch it is equal in
Value blending algorithm is filtered denoising, specifically:
The equal intermediate value blending algorithm of self-adaptive switch, is switched over using dual threshold, is realized in switch mean filter and switch
The fusion treatment of value filtering, is expressed as follows with mathematical formulae:
Wherein, σ (x, y) is that abscissa is x in image, and ordinate is the grayscale shift value of the point of y；μ (x, y) is in image
Abscissa is x, and ordinate is the gray scale difference value of the point of y；F (x, y) is that abscissa is x, and ordinate is the gray value of the point of y；
Median (x, y) is that abscissa is x, and ordinate is the intermediate value of gray value in the neighborhood of a point of y；Mean (x, y) is for abscissa
X, ordinate are the neighborhood of a point average gray of y；G (x, y) is to be by switching the abscissa after equal median filtering denoises
X, ordinate are the gray value of the point of y；In dual threshold, θ is the threshold value for switching intermediate value；λ is the threshold value for switching mean value.
Further, in dual threshold sampling process, the threshold θ for switching intermediate value is the intermediate value of the gradient value in gradient matrix q
The threshold value λ of M, i.e. M=medium (q), self-adaptive switch mean value are taken as 500 × M.
Compared with prior art, the invention has the following beneficial technical effects:
The method of the present invention to a certain extent, overcomes existing remote sensing images filtering algorithm and makes an uproar in face of multiple types distribution
The limitation of scope of application when sound.The present invention combines gradient inverse algorithm and the equal intermediate value blending algorithm of self-adaptive switch, this
Sample just combines the advantage of two kinds of algorithms respectively, not only has the advantages that retain image border and detailed information, but also for not
The partition noise of same type, such as the adaptive intermediate value of selection or the mean value filter that salt-pepper noise and impulsive noise etc. will be adaptive
Wave algorithm, the scope of application obtain a degree of promotion.In addition, traditional intermediate value and Mean Filtering Algorithm use fixed threshold,
Thus each sub-block can be filtered using same standard, will lead to some made an uproar because of the fixation of threshold value
Sound pollution degree is higher and was realized denoising by the too low sub-block of noise pollution degree or denoised the generation of not foot phenomenon.And this
Inventive method is using adaptive threshold, i.e., the threshold value can be adaptive by the height progress of noise pollution degree with the sub-block
The change answered, avoiding problems because using the generation for denoising or denoising not foot phenomenon is crossed caused by fixed threshold.Meanwhile
This method can take into account performance of both noise suppressed and details protection.
Detailed description of the invention
Fig. 1 is the flow diagram of the method for the present invention；
Fig. 2 is that the method for the present invention and other filtering algorithms denoise Contrast on effect to unmanned aerial vehicle remote sensing image filtering, wherein
It (a) is unmanned aerial vehicle remote sensing image, image after (b) being plus make an uproar (c) switchs mean filter, and (d) switching median filter, (e) switch is equal
Intermediate value fused filtering, (f) the equal intermediate value fused filtering of self-adaptive switch, (g) the equal intermediate value of gradient inverse self-adaptive switch is diffusion-weighted
Filtering.
Specific embodiment
Present invention is further described in detail with reference to the accompanying drawing:
Referring to Fig. 1 and Fig. 2, the filtering of traditional remote sensing images select both for the types noise having determined suitable
Filtering and noise reduction algorithm, the scope of application obtains certain limitation.In view of the above deficiencies, in order to realize unmanned aerial vehicle remote sensing image more
Effective denoising, the present invention propose a kind of unmanned aerial vehicle remote sensing image that the equal intermediate value of gradient inverse self-adaptive switch is diffusion-weighted
Filtering algorithm, which not only achieves good filter effect, but also the more traditional filtering algorithm of the scope of application of the algorithm obtains
Promotion to a certain extent.Whether concrete thought is, first by the comparison with threshold value, to judge the point by salt-pepper noise or arteries and veins
Noise is rushed to be polluted.Then, it if judging not to be contaminated, is not carried out to by the point that salt-pepper noise or impulsive noise pollute terraced
Spend smothing filtering reciprocal；If judging to be polluted by salt-pepper noise or impulsive noise, and then again by and it is adaptive in
The threshold value comparison of value selects suitable adaptive intermediate value or mean algorithm.During being somebody's turn to do, Filtering Template is in entire noisy image
It is mobile to realize traversal.Specific step is as follows:
Step 1 obtains unmanned aerial vehicle remote sensing image: unmanned plane image capture device utilized, remote sensing images to be processed are obtained,
Switch to grayscale image after the processing that carries out plus make an uproar, waits and handling in next step.
Step 2 chooses the template that size is n*n, finds out in template center's point and template between remaining (n*n-1) pixel
Gradient value absolute value and be stored in g matrix；
Step 3 passes through and the comparison of threshold value, judge current pixel point whether by salt-pepper noise or impulsive noise dirt
Dye?
By taking n*n template as an example, enabling the gray value of central point (x, y) is f (x, y), find out respectively f (x, y) and template remaining
Gradient value between (n × n-1) a pixel, and given threshold T=f (x, y) × 10%.If at least there is a pixel (x+
I, y+j) so that | f (x+i, y+j)-f (x, y) |≤T, then judging point (x, y) not is caused by salt-pepper noise or random noise；
If all pixels point (x+i, y+j) so that | f (x+i, y+j)-f (x, y) | > T, then judge point (x, y) be exactly salt-pepper noise or
Caused by random noise, according to the gradient value of surrounding pixel point and template center's pixel come judge templet central point whether by noise
Cause；It has been detected by after we realize traversal to entire image with template, namely all noise spots.
Step 4.1, according to step 3 obtain as a result, if it is determined that be not that salt-pepper noise or random noise cause, use gradient
Inverse weight algorithm is smoothed；Detailed process is as follows:
Firstly, the matrix q obtained in step 2 is as follows by taking n*n template as an example:
Then, our available weight matrix w by q matrix,
If f (x+j, y+j)=f (x, y), then gradient is 0, it is specified that central element w (x, y)=0.5, remaining eight weighting members
The sum of element is 0.5, and w each element summation is made to be equal to 1.Then have:(i, j=-1,
0,1, but i, j be not simultaneously for 0).
Finally, at each pixel by template elements with it corresponding to weight correspondence be multiplied, then summing is exactly the picture
Using gradient smoothed out output g (x, y) reciprocal, i.e., n*n template pixel is multiplied vegetarian refreshments with weight matrix w correspondence, then sums, and ties
Output valve of the fruit as template center's pixel.
Step 4.2: according to step 3 obtain as a result, if it is determined that be caused by salt-pepper noise or random noise, then with from
It adapts to the equal intermediate value blending algorithm of switch and is filtered denoising, export g (x, y)；Detailed process is as follows:
In self-adaptive switch-mean value fused filtering is expressed as follows with mathematical formulae:
Wherein, σ (x, y) is that abscissa is x in image, and ordinate is the grayscale shift value of the point of y；μ (x, y) is in image
Abscissa is x, and ordinate is the gray scale difference value of the point of y；F (x, y) is that abscissa is x, and ordinate is the gray value of the point of y；
Median (x, y) is that abscissa is x, and ordinate is the intermediate value of gray value in the neighborhood of a point of y；Mean (x, y) is for abscissa
X, ordinate are the neighborhood of a point average gray of y；G (x, y) is to be by switching the abscissa after equal median filtering denoises
X, ordinate are the gray value of the point of y；θ is the threshold value for switching intermediate value；λ is the threshold value for switching mean value；θ is chosen for gradient matrix q
In gradient value intermediate value M, (wherein, M be matrix q intermediate value, i.e. M=medium (q)), the threshold value λ of self-adaptive switch mean value
It is chosen for 500*M.
In the first scenario, it is able to satisfy the pixel of this condition, either switch intermediate value still switchs mean filter
All think that it is not noise, and the gray scale of the pixel is not changed.
In the latter case, meet the pixel of this condition, switch mean filter thinks that it is not noise, but switchs
Median filtering thinks that it is noise, so in switch-mean value fused filtering thinks that it is noise, and with switching median filter pair
It is handled.
In a third case, meet the pixel of this condition, switching median filter thinks that it is not noise, but switchs
Mean value thinks that it is noise, so in switch-mean value fused filtering thinks that it is noise, and with switch mean filter to its into
Row processing.
In the fourth case, meet the pixel of this condition, either switching median filter still switchs mean value filter
Wave all thinks that it is noise.So also thinking that it is noise in the switch-mean value fused filtering, and because meet this item
The pixel of part, the deviation affirmative of gray value is larger in neighborhood territory pixel point, such as can be larger with switch mean filter denoising error,
So being denoised in this case with switching median filter to pixel.
In switching equal median filtering, if threshold value λ and threshold θ get maximum, g (x, y) be just equal to f (x,
Y), i.e., any processing is not carried out to image；If threshold value λ and threshold θ are all taken as zero, g (x, y) just with median (x, y)
It is essentially equal, i.e., median filtering is carried out to original image；If threshold value λ gets maximum, if threshold θ is zero, g (x, y) just with
Median (x, y) is essentially equal to have carried out median filtering to original image；If threshold θ gets maximum, if threshold value λ is zero, g
(x, y) is just essentially equal with mean (i, j), i.e., has carried out mean filter to original image, so the selection of threshold value is algorithm performance
And the determinant of effect, different threshold value selections are possible to the result that can be differed greatly.And used by invention algorithm
Adaptive threshold avoids to a certain extent or alleviates the appearance of this case.
Step 5 obtains the unmanned aerial vehicle remote sensing image g (x, y) after filtering and noise reduction.
The method of the present invention combines Gradient Inverse Weight algorithm and the equal intermediate value blending algorithm of self-adaptive switch, proposes one
The diffusion-weighted unmanned aerial vehicle remote sensing Image filter arithmetic of the kind equal intermediate value of gradient inverse self-adaptive switch, the algorithm not only have ladder
Degree is reciprocal the advantages of denoising simultaneously, retaining image border and detailed information, and has the equal intermediate value fused filtering of switch and calculate
Method to salt-pepper noise, impulsive noise, etc. different types partition noise have the advantages that ideal denoising effect.And this is adaptively opened
It closes using adaptive threshold in equal intermediate value blending algorithm, which can be adaptive by the degree of noise pollution with the sub-block
The suitable threshold value of the selection answered can take into account performance of both noise suppressed and details protection.
As scheming (e) in Fig. 2 and scheming the comparison of (f) it is found that filter effect acquired by adaptive median filtering algorithm is bright
The aobvious equal median filtering of switch that is better than is calculated；As shown in Table 1, every evaluation index of the latter is significantly better than the former, especially average ladder
Degree, PSNR, MSE index become apparent.As shown in Table 1, every evaluation index of the method for the present invention is significantly better than adaptive equal again
Median filtering algorithm, this shows that the method for the present invention achieves better filter effect, not only remains Gradient Inverse Weight algorithm
The advantages of to image border and detailed information is retained, and have the equal intermediate value fused filtering algorithm of switch to salt-pepper noise, arteries and veins
Rushing the different types partition noise such as noise has the advantages that ideal denoising effect.
1 algorithms of different result of table compares
Claims (5)
1. the remote sensing images filtering method based on the equal intermediate value fusion of gradient inverse self-adaptive switch, which is characterized in that including following
Step:
Step 1: obtaining unmanned aerial vehicle remote sensing image；
Step 2: choosing the template that size is n × n, find out in template center's point and template between remaining (n × n-1) pixel
Gradient value matrix q is simultaneously saved；
Step 3: gradient value obtained in step 2 and threshold value are compared, judge the pixel whether by salt-pepper noise or
Caused by random noise；
Step 4: according to step 3 obtain as a result, if the pixel is caused by salt-pepper noise or random noise, use gradient
Inverse weight algorithm is smoothed；If the pixel is caused by salt-pepper noise or random noise, with adaptively opening
It closes equal intermediate value blending algorithm and is filtered denoising；
The equal intermediate value blending algorithm of self-adaptive switch, is switched over using dual threshold, realizes switch mean filter and switch intermediate value filter
The fusion treatment of wave, is expressed as follows with mathematical formulae:
Wherein, σ (x, y) is that abscissa is x in image, and ordinate is the grayscale shift value of the point of y；μ (x, y) is horizontal seat in image
It is designated as x, ordinate is the gray scale difference value of the point of y；F (x, y) is that abscissa is x, and ordinate is the gray value of the point of y；median
(x, y) is that abscissa is x, and ordinate is the intermediate value of gray value in the neighborhood of a point of y；Mean (x, y) is that abscissa is x, indulges and sits
It is designated as the neighborhood of a point average gray of y；It is x that g (x, y), which is by switching the abscissa after equal median filtering denoises, indulges and sits
It is designated as the gray value of the point of y；In dual threshold, θ is the threshold value for switching intermediate value；λ is the threshold value for switching mean value；
Step 5: the unmanned aerial vehicle remote sensing image after obtaining filtering and noise reduction by step 4.
2. the remote sensing images filtering method according to claim 1 based on the equal intermediate value fusion of gradient inverse self-adaptive switch,
It is characterized in that, template center's point is (x, y) in step 2, the gray value of template center's point is f (x, y), finds out f (x, y) respectively
With the gradient value between remaining (n × n-1) a pixel of template.
3. the remote sensing images filtering method according to claim 2 based on the equal intermediate value fusion of gradient inverse self-adaptive switch,
It is characterized in that, given threshold T=f (x, y) × 10% in step 3, if at least there is a pixel (x+i, y+j), so that |
F (x+i, y+j)-f (x, y) |≤T, then judging pixel (x, y) not is caused by salt-pepper noise or random noise；If all pictures
Vegetarian refreshments (x+i, y+j), so that | f (x+i, y+j)-f (x, y) | > T then judges that pixel (x, y) is by salt-pepper noise or random
Caused by noise.
4. the remote sensing images filtering method according to claim 1 based on the equal intermediate value fusion of gradient inverse self-adaptive switch,
It is characterized in that, when the pixel is caused by salt-pepper noise or random noise in step 4, with Gradient Inverse Weight algorithm into
Row smoothing processing, specifically:
For the template of n × n, matrix q that step 2 obtains are as follows:
Weight matrix w is obtained by matrix q:
If f (x+j, y+j)=f (x, y), then gradient value is 0, it is specified that central element w (x, y)=0.5, remaining n*n-1 weighting
The sum of element is 0.5, so that w each element summation is equal to 1, then has:
Wherein, i, j=-1,0 or 1, but i, j are not 0 simultaneously；
Finally, at each pixel by template elements with it corresponding to weight correspondence be multiplied, then sum and obtain the pixel
Point is using gradient smoothed out output g (x, y) reciprocal.
5. the remote sensing images filtering method according to claim 1 based on the equal intermediate value fusion of gradient inverse self-adaptive switch,
It is characterized in that, the threshold θ for switching intermediate value is the intermediate value M, i.e. M=of the gradient value in gradient matrix q in dual threshold sampling process
The threshold value λ of medium (q), self-adaptive switch mean value are taken as 500 × M.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113379629A (en) * | 2021-06-08 | 2021-09-10 | 深圳思谋信息科技有限公司 | Satellite image denoising method and device, computer equipment and storage medium |
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