CN109214993A - A kind of haze weather intelligent vehicular visual Enhancement Method - Google Patents
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Abstract
The invention discloses a kind of haze weather intelligent vehicular visual Enhancement Methods, comprising the following steps: 1) image is transformed into hsv color space from RGB color；2) haze concentration is assessed；3) it calculates atmosphere and covers parameter；4) foreground image I is obtainedvis(x)；5) current haze concentration is obtained as background image using the Steerable filter algorithm of luminance channel, remove picture noise using Gaussian filter, increase influence of the atmosphere covering parameter to foreground image；6) according to step 5), vision enhancement result is exported.The present invention can effectively improve the contrast and clarity of video, while computational efficiency with higher, effectively improve the picture quality of vision signal, sensitivity be promoted, to guarantee that nobody controls safety and the comfort of manipulation.
Description
Technical field
The present invention relates to vision enhancement fields, more particularly to a kind of haze weather intelligent vehicular visual Enhancement Method.
Background technique
Computer vision system on present road is very sensitive to weather condition, wherein haze weather condition is various
To one kind of visual impact most serious in weather condition.Under the conditions of haze weather, visibility is greatly reduced, road environment system
The visual of system is deteriorated, and the image of acquisition receives serious degeneration, not only smudgy, and contrast reduces, but also can go out
Existing serious color displacement and distortion, many features contained in image are all capped, and what this greatly reduced image applies valence
Value, while also causing these vision systems that can not be worked normally.
In recent years, the sharpening processing of haze sky image has become the research of computer vision and field of image processing
Hot spot has attracted large quantities of researchers both domestic and external, and has had considerable method to be suggested.But due to weather condition itself
Complexity and randomness so that some algorithms proposed at present have certain limitation, some researchs obtained at
Fruit and research method wait further to improve still in continuous development.In the prior art, for road vision
System haze sky acquires the degenerate problem of image, it is not yet found that a kind of more effective haze weather image enchancing method, makes
It obtains under haze weather, can effectively realize image enhancement, increase the picture quality that effect improves vision signal, promote sensitivity.
Summary of the invention
In view of the above drawbacks of the prior art, technical problem to be solved by the invention is to provide one kind based on guiding
The intelligent vehicle machine vision of filtering enhances algorithm.This method can effectively improve the picture quality of vision signal, be promoted sensitive
Degree, to guarantee that nobody controls safety and the comfort of manipulation.
To achieve the above object, the present invention provides a kind of haze weather intelligent vehicular visual Enhancement Method, it is characterized in that: packet
Include following steps:
1) image is transformed into hsv color space from RGB color；
2) haze concentration is assessed；
3) it calculates atmosphere and covers parameter；
4) foreground image I is obtainedvis(x)；
5) current haze concentration is obtained as background image using the Steerable filter algorithm of luminance channel, utilize Gauss
Filter removes picture noise, increases influence of the atmosphere covering parameter to foreground image；
6) according to step 5), vision enhancement result is exported.
Further, in the step 2), haze concentration is calculated according to the following steps:
21) evaluation points of haze concentration are defined according to the following formula:
Wherein, DdarkIt is dark primary priori；
U, v are the Fourier transformations of image pixel (x, y)；
vdark(x, y) is that one of at least three RGB color channels of pixel (x, y) have low-pixel value；
22) haze concentration is calculated according to the following formula:
fβ=k × Ddark
Wherein, k is linear scaling factor；
During non-distorted image, fβIt is haze concentration；fβThe limited range of parameter is limited to [2,8].
Further, in the step 3), parameter is covered using Steerable filter algorithm evaluation current atmospheric.
Further, it carries out calculating atmosphere covering parameter according to the following steps:
321) kernel of linear filter is established according to the following formula:
Wherein, wkIt is k-th of Kernel window；(ak,bk) it is the linear transform coefficient in a given cell window；
I is the pixel index in window；qiIt is output image；
322) nuance of the image p and output image q of input are distinguished according to the following formula:
Wherein, piIt is input picture；qiIt is output image；
323) solution of calculation formula (12) according to the following formula；
Wherein, brightness guiding image I is taken to be equal to input picture p；
At this moment, covk(I, p)=vark(I),We further obtain:
Wherein, ε is regularization smoothing factor；covk(I, p) is that the image of guiding figure I and input is covariance matrix；
vark(I) be guiding figure I variance matrix；It is the mean value of input picture；It is the mean value of guiding figure I；ε is smoothing factor.
324) Steerable filter is applied to whole image region on the basis of retaining the hierarchical structure of original image, pressed
Atmosphere, which is calculated, according to following equation covers parameter I∞:
I∞It is defined as
Wherein, | w | it is the pixel quantity in Kernel window；M is the index number of pixel；pmIt is the pixel of input picture；wm
It is and pmThe Kernel window of center pixel；Refer to the processing step of each pixel.
Further, in the step 4), foreground image I is obtained according to the following formulavis(x):
Ivis(x)=E-I∞ (18)
Wherein, E is the atmosphere light of infinite point.
The beneficial effects of the present invention are: the present invention proposes to improve the low visibility and poor contrast of Vehicular video
One haze video enhancement algorithm based on Steerable filter method.Firstly, simplifying atmospheric attenuation model.Then, based on dead color
Priori theoretical assesses haze concentration.Then current haze concentration is obtained as back using the Steerable filter algorithm of luminance channel
Scape image increases influence of the atmosphere covering parameter to foreground image.This method can effectively improve video contrast and
Clarity, while computational efficiency with higher effectively improve the picture quality of vision signal, sensitivity are promoted, thus in mist
Safety and the comfort of manipulation are controlled in guarantee under haze weather.
Detailed description of the invention
Fig. 1 is the step flow diagram of the embodiment of the invention.
Specific embodiment
Present invention will be further explained below with reference to the attached drawings and examples:
As shown in Figure 1, a kind of haze weather intelligent vehicular visual Enhancement Method, comprising the following steps:
1) image is transformed into hsv color space from RGB color；
2) haze concentration is assessed；
3) it calculates atmosphere and covers parameter；
4) foreground image I is obtainedvis(x)；
5) current haze concentration is obtained as background image using the Steerable filter algorithm of luminance channel, utilize Gauss
Filter removes picture noise, increases influence of the atmosphere covering parameter to foreground image.
6) according to step 5), vision enhancement result is exported.
According to the dark priori theoretical of Dr.He show one clearly image in one of at least three RGB color channels
With low-pixel value, can indicate are as follows:
Jdark(x)=min (min (Jc(y)))c∈{r,g,b},y∈Ω(x) (9)
Here, JcIt is the Color Channel of RGB image J；Ω (x) is the eccentric statistical regions of x.
JdarkAlways very little, or even close to zero, in clearly image, in addition to sky area is largely clearly schemed
As statistical analysis.Image under low key tone image and different haze concentration is compared, by comparing low key tone part figure
Picture, it can be found that the mean pixel gray scale of low key tone image can be used for assessing the haze concentration of current environment.
Therefore, particularly, in the step 2), haze concentration is calculated according to the following steps:
21) evaluation points of haze concentration are defined according to the following formula:
Wherein, DdarkIt is dark primary priori；
U, v are the Fourier transformations of image pixel (x, y)；
vdark(x, y) is that one of at least three RGB color channels of pixel (x, y) have low-pixel value；
22) haze concentration is calculated according to the following formula:
fβ=k × Ddark
Wherein, k is linear scaling factor；fβIt is haze concentration,
In order to ensure non-distorted image, the limited range of parameter is limited to [2,8].
Particularly, in the step 3), parameter is covered using Steerable filter algorithm evaluation current atmospheric.
Particularly, it carries out calculating atmosphere covering parameter according to the following steps:
321) kernel of linear filter is established according to the following formula:
Wherein, wkIt is k-th of Kernel window；(ak,bk) it is the linear transform coefficient in a given cell window；
I is the pixel index in window；qiIt is output image；
322) nuance of the image p and output image q of input are distinguished according to the following formula:
Wherein, piIt is input picture；qiIt is output image；
323) solution of calculation formula (12) according to the following formula；
Wherein, brightness guiding image I is taken to be equal to input picture p；
At this moment, covk(I, p)=vark(I),We further obtain:
Wherein, ε is regularization smoothing factor；covk(I, p) is that the image of guiding figure I and input is covariance matrix；
vark(I) be guiding figure I variance matrix；It is the mean value of input picture；It is the mean value of guiding figure I；ε is smoothing factor.
324) Steerable filter is applied to whole image region on the basis of retaining the hierarchical structure of original image, pressed
Atmosphere, which is calculated, according to following equation covers parameter I∞:
I∞It is defined as
Wherein, | w | it is the pixel quantity in Kernel window；M is the index number of pixel；pmIt is the pixel of input picture；wm
It is and pmThe Kernel window of center pixel；Refer to the processing step of each pixel.In the present embodiment, smoothing factor ε is arranged
It is 0.3, in other embodiments, smoothing factor can also be configured according to different needs to reach identical technical effect,
The width r of Kernel window is set as 30 pixels.
Particularly, in the step 4), foreground image I is obtained according to the following formulavis(x):
Ivis(x)=E-I∞ (18)
Wherein, E is the atmosphere light of infinite point
About the selection of filter, current most computer vision and computer graphics image filtering are related to
To the content for inhibiting or extracting image.Simply there is a constant filter of the linear translation of core (LTI), such as average, Gauss,
Laplce and Sobel filter are all widely used in image recovery, fuzzy/to sharpen, edge detection and feature extraction etc.
Deng.Selectable, LTI filter can be held explicitly by a Poisson's equation in high dynamic range (HDR) compression is solved
Row image mosaic, image scratch figure and gradient field operation, and filtering core is the transposition by a homogeneous Laplacian Matrix
Clearly it is defined.
LTI filtering core is space invariance and independent with picture material, but usually sometimes needs to consider guiding figure
The additional information of picture.The pioneering work of anisotropy parameter needs the gradient of the image filtered itself to go to instruct diffusion process,
It avoids smoothing to edge.Quadratic sum minimum weight filter needs the image filtered to go to instruct using input, and selects one
Quadratic function, this quadratic function are equivalent to the anisotropy parameter of a non-general stable state.In other applications,
Being oriented to image also can be other image, rather than original input picture.The output of Steerable filter device is the result is that be oriented to image
Local linear conversion.On the one hand, Steerable filter device has a good edge preserving smoothing effect, as two-sided filter, but
It is the influence that it does not have gradient to reverse artifact.On the other hand, Steerable filter can be far from smooth, in the auxiliary of guiding figure
It helps down, it can allow filtering to export more structuring, and smooth unlike input.
And in the technical program, the Steerable filter that we select mainly is accomplished by the following way:
We first define a common linear translation transformed filter program, with guiding image I, a filtering input
Image P and an output image q are related.I and p be according to application it is previously given, they can be identical.At one
Filter result at pixel i is to be expressed as a weighted average:
In formula, i and j are pixel subscripts.Filter kernel Wij is the function of guide image I, and independent with p.This
Filter is and p is linearly related.
The example of one such filter is that cascading filter volume bilateral filtering core Wbf is given by following formula
:
In formula, X is pixel coordinate；Ki is that a normalized parameter guarantees ∑ jWijbf=1；Parameter σsAnd σrIt adjusts separately
The sensitivity of space similarity and colour brightness range similarity.As P and I equal, cascading filter is degraded into initial pair
Side filter.
Explicit weighted average filter optimizes a quadratic function, and solves the linear equation of form below one
Group:
Aq=P (3)
Q and p is column vector N-1, corresponding { qi } and { pi }；A is the only relevant matrix with I of a N-N.Formula (3)
Q=A-1p is solved, has identical form, Wij=(A-1) ij with (1).
We define Steerable filter device now, and crucial hypothesis is this Steerable filter device in guiding image I and filters defeated
It is a Local Linear Model between q out.We assume that q be I center pixel k window Wk linear transformation:
(ak, bk) assumes that the identical linear coefficient of same Wk, the square window for being r with a radius.This local linear
As soon as long as model determination I has an edge, then q has an edge, because
In order to determine linear coefficient (ak, bk), the filtering image P for the input that needs restraint.We define output q as input p
Subtract some undesirable content n, such as noise/texture:
Qi=pi-ni (5)
We, which seek a solution, can minimize difference between q and p, while keep linear model (4).It is special
Other, we minimize the cost function of following window Wk:
Here, ∈ is the regularization parameter of the big ak of a punishment.
Equation (6) is linear ridge regression model, it is solved by formula (7) given below:
Here, μkWithIt is to be oriented to the window Wk average value of image I with variance；| W | it is number of pixels in Wk；
It is average value of the P in Wk.Linear coefficient (ak, bk) is obtained, we can calculate filtering
Export qi root.
However, a pixel i is related to all covering overlaid windows Wk of i, so the value of qi is not phase in equation (5)
Same working as is calculated in different windows.One simple strategy is the probable value for the qi being averaged out.So calculating institute
There is (ak, bk) value of Wk window in image, we, which calculate, exports result by following filtering:
Pay attention toSince box window is symmetrical, our rewrite equations (8) are as follows:
WithIt is all overlaid windows mean coefficients at i.
The Average Strategy of this overlaid windows is popular in image denoising, and is very successfully.
Equation (6), (7), (8) are the definition of Steerable filter device.
Using this Steerable filter, the technical program simplifies atmospheric attenuation model.And based on dark-coloured priori theoretical assessment
Then haze concentration obtains current haze concentration as background image using the Steerable filter algorithm of luminance channel, increases big
Gas covers influence of the parameter to foreground image.Method provided by the invention can effectively improve the contrast and clarity of video,
Computational efficiency with higher simultaneously.
The preferred embodiment of the present invention has been described in detail above.It should be appreciated that those skilled in the art without
It needs creative work according to the present invention can conceive and makes many modifications and variations.Therefore, all technologies in the art
Personnel are available by logical analysis, reasoning, or a limited experiment on the basis of existing technology under this invention's idea
Technical solution, all should be within the scope of protection determined by the claims.
Claims (5)
1. a kind of haze weather intelligent vehicular visual Enhancement Method, it is characterized in that: the following steps are included:
1) image is transformed into hsv color space from RGB color；
2) haze concentration is assessed；
3) it calculates atmosphere and covers parameter；
4) foreground image I is obtainedvis(x)；
5) current haze concentration is obtained as background image using the Steerable filter algorithm of luminance channel, utilize Gaussian filter
Picture noise is removed, influence of the atmosphere covering parameter to foreground image is increased；
6) according to step 5), vision enhancement result is exported.
2. haze weather intelligent vehicular visual Enhancement Method as described in claim 1, it is characterized in that: in the step 2), according to
The following steps calculate haze concentration:
21) evaluation points of haze concentration are defined according to the following formula:
Wherein, DdarkIt is dark primary priori；
U, v are the Fourier transformations of image pixel (x, y)；
vdark(x, y) is that one of at least three RGB color channels of pixel (x, y) have low-pixel value；
22) haze concentration is calculated according to the following formula:
fβ=k × Ddark
Wherein, k is linear scaling factor；Wherein fβIt is haze concentration；
In order to ensure non-distorted image, fβThe limited range of parameter is limited to [2,8].
3. haze weather intelligent vehicular visual Enhancement Method as described in claim 1, it is characterized in that: being utilized in the step 3)
Steerable filter algorithm evaluation current atmospheric covers parameter.
4. haze weather intelligent vehicular visual Enhancement Method as claimed in claim 3, it is characterized in that:
In the step 3), carry out calculating atmosphere covering parameter according to the following steps:
321) kernel of linear filter is established according to the following formula:
Wherein, wkIt is k-th of Kernel window；(ak,bk) it is the linear transform coefficient in a given cell window；I be
Pixel index in window；qiIt is output image；IiIt is the intensity of observed image；
322) nuance of the image p and output image q of input are distinguished according to the following formula:
Wherein, piIt is input picture；qiIt is output image；
323) solution of calculation formula (12) according to the following formula；
Wherein, brightness guiding image I is taken to be equal to input picture p；
At this moment, covk(I, p)=vark(I),We further obtain:
Wherein, ε is regularization smoothing factor；covk(I, p) is that the image of guiding figure I and input is covariance matrix；vark(I)
It is the variance matrix of guiding figure I；It is the mean value of input picture；It is the mean value of guiding figure I；ε is smoothing factor；
324) Steerable filter is applied to whole image region on the basis of retaining the hierarchical structure of original image, according to following
Formula calculates atmosphere and covers parameter I∞:
I∞It is defined as
Wherein, | w | it is the pixel quantity in Kernel window；M is the index number of pixel；pmIt is the pixel of input picture；wmIt is and pm
The Kernel window of center pixel；Refer to the processing step of each pixel.
5. haze weather intelligent vehicular visual Enhancement Method as described in claim 1, it is characterized in that: in the step 4), according to
Following equation obtains foreground image Ivis(x):
Ivis(x)=E-I∞ (18)
Wherein, E is the atmosphere light of infinite point.
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