CN109949239A - A kind of adaptive clarification method suitable for the more scene haze images of more concentration - Google Patents
A kind of adaptive clarification method suitable for the more scene haze images of more concentration Download PDFInfo
- Publication number
- CN109949239A CN109949239A CN201910177950.4A CN201910177950A CN109949239A CN 109949239 A CN109949239 A CN 109949239A CN 201910177950 A CN201910177950 A CN 201910177950A CN 109949239 A CN109949239 A CN 109949239A
- Authority
- CN
- China
- Prior art keywords
- image
- value
- adaptive
- haze
- formula
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Landscapes
- Image Processing (AREA)
- Facsimile Image Signal Circuits (AREA)
Abstract
The invention discloses a kind of adaptive clarification methods suitable for the more scene haze images of more concentration, including the adapting to image clarification method based on haze image degradation model and dark primary prior model；The adapting to image clarification method is by realizing that parameter obtains or dark primary value adaptive acquiring method, atmosphere light intensity adaptive estimation method and the sharpening coefficient self-adaptive computing method of data processing form using adaptive mode；Adaptive clarification method suitable for the more scene haze images of more concentration of the invention, sharpening parameter is obtained according to haze image unique characteristics completely, without artificial setting parameter, and the robustness with higher in terms of more concentration, more scene haze image sharpenings, its clear image obtained contains much information, and contrast is high, image clearly, without halation phenomenon, hence it is evident that restore clarification method better than multiple dimensioned Retinex enhancing clarification method and He dark primary priori.
Description
Technical field
The present invention relates to a kind of adaptive clarification methods suitable for the more scene haze images of more concentration, and it is clear to belong to image
Clearization processing method technical field.
Background technique
With the rapid development of society being constantly progressive with science and technology, computer vision system has been widely applied to city
The numerous areas such as city's traffic, video monitoring, intelligent navigation, remotely sensed image, military surveillance；However, in recent years, the frequency of haze weather
Numerous appearance has seriously affected the image quality of computer vision system, cause obtain picture contrast decline, cross-color,
It is smudgy, extreme influence is caused with life to people's production；Therefore, image sharpening technology is studied, reduces haze to image
The influence of quality has important practical value and realistic meaning for promoting computer vision performance.
According to the difference of image sharpening mechanism, conventional images clarification method is broadly divided into the image based on image procossing
Enhancement Method and image recovery method based on physical model；First kind method directly utilizes image independent of physical model
Processing Algorithm promotes picture contrast, and typical method has: histogram equalization method, Retinex method etc., such method does not consider mist
Haze image deterioration reason, it is difficult to improve haze image quality from mechanism；Second class method generates mould by building haze image
Type, inverting haze image degrade process, finally restore fog free images out, typical method has: partial differential equation method, depth sharpening
Method and image restoration method based on prior information；In comparison, partial differential equation method and picture depth sharpening method usually assume that
Haze obedience is uniformly distributed model, restores haze image using whole unified method, and practical haze is distributed and uneven, is caused
Keep the sharpening treatment effect of image bad, is lost some detailed information；Image restoration method based on prior information is established
On the basis of for statistical analysis to practical haze image, better haze image recovery effect can be obtained.
By having carried out a large amount of statistical experiments to fog free images and foggy image, discovery fog free images contrast is apparently higher than
Foggy image proposes the clarification method for maximizing local contrast on this basis, and this method passes through local contrast most
Bigization processing achievees the purpose that sharpening, but there are big guns and distortion phenomenon for the color of image after handling；In addition, utilizing light
The incoherent priori knowledge of local surfaces reflector segment with scene is propagated, estimates scene reflectivity rate, obtains fog free images, but should
Method is preferable to mist treatment effect premised on obtaining bright-coloured image color, it is difficult to the image restoration for thick fog weather；
For above-mentioned problem, a kind of image clarification method based on dark channel prior theory is proposed in the prior art, using leading
Replace soft pick figure to carry out transmitance optimization to filtering, improve sharpening processing speed, but after handling image bright area color
Coloured silk is unnatural, blocking artifact is obvious；Air light value and transmissivity are estimated using the method that bright, dark combines later, one
Determine the cross-color for solving the problems, such as bright area sharpening in degree, but not yet considers the adaptive clear of various concentration haze image
Clearization problem；In addition, in existing primary colors priori and its improving in algorithm for image clearness, for adjusting the sharpening system of sharpening effect
Number ω mostly uses fixed value, less to do adaptive adjustment according to the actual situation；Therefore, in order to solve existing algorithm be difficult to it is pervasive
The problems such as dark unknown more scene haze image sharpenings different in concentration, bright, the present invention on the basis of dark primary priori theoretical,
It is proposed a kind of adaptive algorithm for image clearness, which obtains according to haze image unique characteristics completely, is not necessarily to people
For setting parameter, and the robustness with higher in terms of more concentration, more scene haze image sharpenings.
Summary of the invention
To solve the above problems, the invention proposes a kind of adapting to image sharpening sides suitable for the more scenes of more concentration
Method, it is not necessary that sharpening parameter is manually set, image clearly, the color of acquisition are naturally, clear in more concentration, more scene haze images
Changing aspect has better robustness.
Adapting to image clarification method suitable for the more scenes of more concentration of the invention, including degenerated based on haze image
The adapting to image clarification method of model and dark primary prior model；The adapting to image clarification method is by using adaptive
Answer mode realize parameter obtain or the dark primary value adaptive acquiring method of data processing, atmosphere light intensity adaptive estimation method and
Sharpening coefficient self-adaptive computing method composition；
The dark primary value adaptive acquiring method is to be carried out certainly using quick OSTU algorithm to the bright dark areas of haze image
Segmentation is adapted to, and subregion obtains the dark primary value of bright dark areas；
The atmosphere light intensity adaptive estimation method is to be carried out according to bright area distribution situation to bright dark areas atmosphere light intensity
ART network；
The sharpening coefficient self-adaptive computing method is to propose to use gray scale by counting haze image histogram feature
Concentration degree method adaptive polo placement sharpening coefficient.
Further, described to be based on haze image degradation model
I (x)=J (x) t (x)+A [1-t (x)], (1)
In formula: I (x) is the haze image intensity that imaging device observes；J (x) is fog free images intensity, i.e. parked figure
Picture；T (x) is transmitance, and reflection light penetrates the ability of haze；A is the atmosphere light intensity of infinite point；J (x) t (x) corresponds to target
Reflected light enters the part of imaging device after atmospheric scattering is decayed, which exponentially decays with scene depth increase；A
Enter the part of imaging device after [1-t (x)] corresponding atmosphere light scattering, which increases as scene depth increases, can make
It is distorted at Scene Blur and color displacement；
Formula (1) is transplanted and arranged, fog free images intensity is expressed as
It is above it is various in, only I (x) is known terms, and the essence of image sharpening is exactly according to above-mentioned model, by saturating
It crosses rate t (x) and atmosphere light intensity A is estimated, restore clear image J (x) out.
Further, the dark primary prior model is the dark original that clarification method statistics is restored according to He dark primary priori
Color priori rule, performance are exactly to have the presence of dark pixel point, these dark pixels in each position of fog free images on the image
Point is exactly dark primary Jdark, these dark primary point gray values tend to 0, meet
In formula: the RGB channel of subscript c expression image；Y ∈ Ω (x) indicates a window centered on pixel x；
Given that it is known that transmitance t (x) is constant in atmosphere light intensity A, window Ω (x), formula (1) is arranged and is done twice most
Small operation can obtain:
Simultaneous formula (3) and (4), the estimated value that can obtain transmitance are
The dim sense of vision of people's observation distant place scenery in consideration real life, introducing sharpening coefficient ω (0 < ω≤
1), then above formula is modified to
In formula: ω value is smaller, and mist ingredient is more in restored image, and sharpening ability is weaker, but is worth too small, and leveling off to 0 has
Disobey the purpose of sharpening；For ω value closer to 1, the ability of sharpening is stronger, but color of image can be made supersaturated, lacks the sense of reality；
The estimation method of atmosphere light intensity A is: first obtain image dark primary in the maximum pixel of 0.1% brightness, then these
Pixel corresponds to estimated value of the maximum value of pixel in original image as A；
Atmosphere light intensity A in the transmitance t (x) of estimation and formula (2) can be obtained into fog free images, actually in calculating,
Image fault is caused in order to avoid transmitance is too small, transmitance lower limit t is usually set0, then fog free images be
Further, the dark primary value adaptive acquiring method itself specific steps are as follows: to image pixel carry out
Category division divides obtained the distance between all kinds of maximums by making, determines optimal segmenting threshold；
The first step, it is assumed that image I has L gray level, and i-stage number of pixels is ni, total pixel number is
The probability that then i-stage pixel occurs is pi=ni/N；
Second step divides the image into two class of A and B for the threshold value k of setting, and wherein A class gray scale interval is [0, k], B class
Gray scale interval is [k+1, L-1], then the class interval whole image gray average μ, A gray average μA(k), the class interval B gray average μB
(k) it is respectively as follows:
The probability distribution of two class gray scale intervals meets:
Define inter-class variance when threshold value k is are as follows:
σ2(k)=pA[μ-μA(k)]2+pB[μ-μB(k)]2, (10)
According to maximum between-cluster variance criterion, from 0 to L-1 threshold value k is changed, finding out makes the maximum k value of variance in above formula, as
Optimal segmenting threshold T；Change a subthreshold k due to every, it is above-mentioned it is various will all be recalculated, to reduce operation time, first root
According to haze image histogram low ebb gray feature, several threshold points are filtered out, then substitution is above various, realizes quick OSTU threshold
Value segmentation；
Third step, according to gray level i and its pixel number niL relationship is established to [i, ni], it is assumed that curve [i, ni] on it is a certain
Gray level is T at positionj, as its pixel number njWhen meeting following relationship simultaneously, by gray level TjScreening is threshold point:
In formula: Δ is screening window size, takes Δ=3pixels；α is that minimum value limits ratio, takes α=0.1%；
4th step carries out Threshold segmentation to haze image using improved quick OSTU threshold method, is sieved according to its threshold point
It can be seen that threshold point only remains 49 after screening, participating in the threshold value calculated points reduces for choosing and bright dark areas segmentation result
80% or more；Meanwhile bright dark areas by effectively it is separated, facilitate subregion sharpening processing；
5th step, according to formula (3), the RGB triple channel minimum value of each pixel is dark primary value, it may be assumed that
If the haze image of various concentration is all made of above formula and solves dark primary value, the mistake of bright area color may result in
Very；This is because the minimum gradation value of bright area is higher with respect to dark areas, if directlying adopt above formula minimum value method seeks dark primary value,
Transmitance can then calculated inaccurate, and triple channel gray average is selected then more to reflect practical feelings as the dark primary value of bright area
Condition；For this purpose, correcting to dark primary minimum value, dark primary value is solved using following formula:
In formula: TcFor c channel segmentation threshold value, c ∈ { R, G, B }.
Further, the atmosphere light intensity adaptive estimation method itself specific steps are as follows: using bright dark subregion
Averaging method carries out atmosphere light intensity ART network, as bright area pixel number accounting Pb cWhen < 10%, illustrate that bright area is less, this
When first take 0.1% bright spot before dark primary image, then find out the gray average conduct of these bright spots corresponding pixel points in original image
Atmosphere light intensity estimated value；As bright area pixel number accounting Pb cWhen >=10%, illustrate that bright area is more, at this time with all bright area pictures
For the gray average of vegetarian refreshments as atmosphere light intensity estimated value, calculation formula is as follows:
In formula: NbFor bright area pixel number.
Further, the sharpening coefficient self-adaptive computing method itself specific steps are as follows: by different dense
It spends haze image and carries out statistics with histogram, discovery haze concentration is bigger, and histogram distribution is more concentrated, and most gray values are concentrated
It is [m in sizec, Mc] gray scale interval in, using the property, propose that a kind of sharpening coefficient based on gray scale concentration degree calculates
Method meets:
In formula: ωcFor minimum value regulation coefficient, value ωc=0.15；Bound mcAnd McMeet:
In formula: α is bound regulation coefficient, value α=1%；
By the above various corresponding substitution transmitance formula (6), the transmitance of the bright dark areas of haze image can be obtained；It ties again
It closes formula (7), it will be able to the preliminary image J obtained after restoring0。
Compared with prior art, of the invention is suitable for the adaptive clear of the more scene haze images of more concentration to the present invention
Change method is split the bright dark areas of haze image by quick OSTU method, and subregion obtains bright dark areas dark primary value, and
ART network is carried out to different zones atmosphere light intensity value；According to haze image histogram feature, propose to use gray scale concentration degree
Method calculates sharpening coefficient；Adapting to image clarification method sharpening parameter of the invention is completely according to haze image itself
Feature obtains, and is not necessarily to artificial setting parameter, and the robust with higher in terms of more concentration, more scene haze image sharpenings
Property, the clear image obtained contains much information, and contrast is high, and image clearly, color is naturally, without halation phenomenon, hence it is evident that is better than
Multiple dimensioned Retinex enhancing clarification method and He dark primary priori restore clarification method.
Detailed description of the invention
Fig. 1 is the atmospherical scattering model structural schematic diagram that McCartney is proposed.
Fig. 2 is the flow chart of adapting to image clarification method of the invention.
Fig. 3 is real scene shooting haze image in campus of the invention.
Fig. 4 is threshold point screening of the invention and bright dark areas segmentation result schematic diagram；
Wherein, figure (a) is threshold point screening schematic diagram of the invention, and figure (b) is bright dark areas segmentation result of the invention
Schematic diagram.
Specific embodiment
Adaptive clarification method suitable for the more scene haze images of more concentration as shown in Figure 2, including it is based on haze
The adapting to image clarification method of image degradation model and dark primary prior model；The adapting to image clarification method by
Realize that parameter obtains or the dark primary value of data processing adaptively obtains (obtain dark channel using adaptive mode
Image adaptively) method, atmosphere light intensity ART network (estimate air light adaptively) method and
Sharpening coefficient adaptive polo placement (calculate dehazing coefficient adaptively) method composition；
The dark primary value adaptive acquiring method is to be carried out certainly using quick OSTU algorithm to the bright dark areas of haze image
Segmentation is adapted to, and subregion obtains the dark primary value of bright dark areas；
The atmosphere light intensity adaptive estimation method is to be carried out according to bright area distribution situation to bright dark areas atmosphere light intensity
ART network；
The sharpening coefficient self-adaptive computing method is to propose to use gray scale by counting haze image histogram feature
Concentration degree method adaptive polo placement sharpening coefficient.
Wherein, haze image degradation model can preferably disclose the degradation mechanism of haze image, according to the model, at
As equipment obtain target image mainly by from target reflecting light attenuation components and ambient diffusing light component two parts constitute,
As shown in Figure 1,
McCartney is simplified, the simplified model for having obtained the degeneration of haze image is
I (x)=J (x) t (x)+A [1-t (x)], (1)
In formula: I (x) is the haze image intensity that imaging device observes；J (x) is fog free images intensity, i.e. parked figure
Picture；T (x) is transmitance, and reflection light penetrates the ability of haze；A is the atmosphere light intensity of infinite point；As shown in Figure 1, J (x) t
(x) target reflecting light in corresponding diagram 1 enters the part of imaging device after atmospheric scattering is decayed, and the part is with scene depth
Increase exponentially decays；In A [1-t (x)] corresponding diagram 1 atmosphere light scattering after enter imaging device part, the part with
Scene depth increases and increases, and will cause Scene Blur and color displacement distortion；
Formula (1) is transplanted and arranged, fog free images intensity is represented by
It is above it is various in, only I (x) is known terms, and the essence of image sharpening is exactly according to above-mentioned model, by saturating
It crosses rate t (x) and atmosphere light intensity A is estimated, restore clear image J (x) out；
The dark primary priori rule of clarification method statistics is restored according to He dark primary priori, shade is everywhere in natural scene
As it can be seen that performance is exactly to have the presence of dark pixel point in each position of fog free images on the image, these dark pixel points are exactly
Dark primary Jdark, these dark primary point gray values tend to 0, meet
In formula: the RGB channel of subscript c expression image；Y ∈ Ω (x) indicates a window centered on pixel x；
Given that it is known that transmitance t (x) is constant in atmosphere light intensity A, window Ω (x), formula (1) is arranged and is done twice most
Small operation can obtain:
Simultaneous formula (3) and (4), the estimated value that can obtain transmitance are
The dim sense of vision of people's observation distant place scenery in consideration real life, introducing sharpening coefficient ω (0 < ω≤
1), then above formula is modified to
In formula: ω value is smaller, and mist ingredient is more in restored image, and sharpening ability is weaker, but value too small (level off to 0) has
Disobey the purpose of sharpening；For ω value closer to 1, the ability of sharpening is stronger, but color of image can be made supersaturated, lacks the sense of reality；
The estimation method of atmosphere light intensity A is: first obtain image dark primary in the maximum pixel of 0.1% brightness, then these
Pixel corresponds to estimated value of the maximum value of pixel in original image as A；
The transmitance t (x) of estimation and atmosphere light intensity A formula (2) can be obtained into fog free images, actually in calculating, in order to
It avoids transmitance is too small from causing image fault, transmitance lower limit t is usually set0, then fog free images be
The dark primary value adaptive acquiring method itself specific steps are as follows: to image pixel carry out category division,
By making to divide obtained the distance between all kinds of maximums, optimal segmenting threshold is determined；
The first step, it is assumed that image I has L gray level, and i-stage number of pixels is ni, total pixel number is
The probability that then i-stage pixel occurs is pi=ni/N；
Second step divides the image into two class of A and B for the threshold value k of setting, and wherein A class gray scale interval is [0, k], B class
Gray scale interval is [k+1, L-1], then the class interval whole image gray average μ, A gray average μA(k), the class interval B gray average μB
(k) it is respectively as follows:
The probability distribution of two class gray scale intervals meets:
Define inter-class variance when threshold value k is are as follows:
σ2(k)=pA[μ-μA(k)]2+pB[μ-μB(k)]2, (10)
According to maximum between-cluster variance criterion, from 0 to L-1 threshold value k is changed, finding out makes the maximum k value of variance in above formula, as
Optimal segmenting threshold T；Change a subthreshold k due to every, it is above-mentioned it is various will all be recalculated, to reduce operation time, first root
According to haze image histogram low ebb gray feature, several threshold points are filtered out, then substitution is above various, realizes quick OSTU threshold
Value segmentation；
Third step, according to gray level i and its pixel number niL relationship is established to [i, ni], it is assumed that curve [i, ni] on it is a certain
Gray level is T at positionj, as its pixel number njWhen meeting following relationship simultaneously, by gray level TjScreening is threshold point:
In formula: Δ is screening window size, takes Δ=3pixels；α is that minimum value limits ratio, takes α=0.1%；
4th step carries out Threshold segmentation to haze image as shown in Figure 3 using improved quick OSTU threshold method, according to
The screening of its threshold point and bright dark areas segmentation result are as shown in Figure 4, it will thus be seen that threshold point only remains 49 after screening, participates in meter
The threshold value points of calculation reduce 80% or more；Meanwhile bright dark areas by effectively it is separated, facilitate at the sharpening of subregion
Reason；
5th step, according to formula (3), the RGB triple channel minimum value of each pixel is dark primary value, it may be assumed that
If the haze image of various concentration is all made of above formula and solves dark primary value, the mistake of bright area color may result in
Very；This is because the minimum gradation value of bright area is higher with respect to dark areas, if directlying adopt above formula minimum value method seeks dark primary value,
Transmitance can then calculated inaccurate, and triple channel gray average is selected then more to reflect practical feelings as the dark primary value of bright area
Condition；For this purpose, correcting to dark primary minimum value, dark primary value is solved using following formula:
In formula: TcFor c channel segmentation threshold value, c ∈ { R, G, B }.
The atmosphere light intensity adaptive estimation method itself specific steps are as follows: using bright dark subregion averaging method come into
Row atmosphere light intensity ART network, as bright area pixel number accounting Pb cWhen < 10%, illustrate that bright area is less, first takes dark original at this time
0.1% bright spot before chromatic graph picture, the gray average for then finding out these bright spots corresponding pixel points in original image are estimated as atmosphere light intensity
Evaluation；As bright area pixel number accounting Pb cWhen >=10%, illustrate that bright area is more, at this time with the gray scale of all bright area pixels
For mean value as atmosphere light intensity estimated value, calculation formula is as follows:
In formula: NbFor bright area pixel number.
The sharpening coefficient self-adaptive computing method itself specific steps are as follows: by various concentration haze image
Statistics with histogram is carried out, discovery haze concentration is bigger, and histogram distribution is more concentrated, and most gray values concentrate on size and are
[mc, Mc] gray scale interval in, using the property, propose a kind of sharpening coefficient calculation method based on gray scale concentration degree, it is full
Foot:
In formula: ωcFor minimum value regulation coefficient, value ωc=0.15；Bound mcAnd McMeet:
In formula: α is bound regulation coefficient, value α=1%；
By the above various corresponding substitution transmitance formula (6), the transmitance of the bright dark areas of haze image can be obtained；It ties again
It closes formula (7), it will be able to the preliminary image J obtained after restoring0。
Adaptive clarification method suitable for the more scene haze images of more concentration of the invention, passes through quick OSTU method pair
The bright dark areas of haze image is split, and subregion obtains bright dark areas dark primary value, and to different zones atmosphere light intensity value into
Row ART network；According to haze image histogram feature, proposition calculates sharpening coefficient using gray scale concentration degree method；Host and guest
See evaluation result to show: the clear image that adapting to image clarification method of the invention obtains contains much information, and contrast is high,
Image clearly, color is naturally, without halation phenomenon, hence it is evident that first better than multiple dimensioned Retinex enhancing clarification method and He dark primary
Test recovery clarification method.
Above-described embodiment is only better embodiment of the invention, therefore all according to structure described in present patent application range
It makes, the equivalent change or modification that feature and principle are done, is included in the scope of the patent application of the present invention.
Claims (6)
1. a kind of adaptive clarification method suitable for the more scene haze images of more concentration, including it is based on haze image degeneration mould
The adapting to image clarification method of type and dark primary prior model；It is characterized by: the adapting to image clarification method
By dark primary value adaptive acquiring method, the atmosphere light intensity self-adaptive of realizing parameter acquisition or data processing using adaptive mode
Estimation method and sharpening coefficient self-adaptive computing method composition；
The dark primary value adaptive acquiring method is to be carried out adaptively using quick OSTU algorithm to the bright dark areas of haze image
Segmentation, and subregion obtains the dark primary value of bright dark areas；
The atmosphere light intensity adaptive estimation method is to be carried out to bright dark areas atmosphere light intensity adaptive according to bright area distribution situation
It should estimate；
The sharpening coefficient self-adaptive computing method is to propose to concentrate using gray scale by counting haze image histogram feature
Degree method adaptive polo placement sharpening coefficient.
2. the adaptive clarification method according to claim 1 suitable for the more scene haze images of more concentration, feature
It is: described to be based on haze image degradation model
I (x)=J (x) t (x)+A [1-t (x)], (1)
In formula: I (x) is the haze image intensity that imaging device observes；J (x) is fog free images intensity, i.e. parked image；t
It (x) is transmitance, reflection light penetrates the ability of haze；A is the atmosphere light intensity of infinite point；The corresponding target reflection of J (x) t (x)
Light enters the part of imaging device after atmospheric scattering is decayed, which exponentially decays with scene depth increase；A[1-t
(x)] enter the part of imaging device after corresponding atmosphere light scattering, which increases as scene depth increases, and will cause field
Scape is fuzzy and color displacement is distorted；
Formula (1) is transplanted and arranged, fog free images intensity is expressed as
It is above it is various in, only I (x) is known terms, and the essence of image sharpening is exactly according to above-mentioned model, by transmitance
T (x) and atmosphere light intensity A are estimated, clear image J (x) out is restored.
3. the adaptive clarification method according to claim 1 suitable for the more scene haze images of more concentration, feature
Be: the dark primary prior model is the dark primary priori rule that clarification method statistics is restored according to He dark primary priori,
Performance is exactly to have the presence of dark pixel point in each position of fog free images on the image, these dark pixel points are exactly dark primary
Jdark, these dark primary point gray values tend to 0, meet
In formula: the RGB channel of subscript c expression image；Y ∈ Ω (x) indicates a window centered on pixel x；
Given that it is known that transmitance t (x) is constant in atmosphere light intensity A, window Ω (x), formula (1) is arranged and does minimum fortune twice
It calculates, can obtain:
Simultaneous formula (3) and (4), the estimated value that can obtain transmitance are
The dim sense of vision for considering people's observation distant place scenery in real life, introduces sharpening coefficient ω (0 ω≤1 <), then
Above formula is modified to
In formula: ω value is smaller, and mist ingredient is more in restored image, and sharpening ability is weaker, but value is too small, levels off to 0 against clear
The purpose of clearization；For ω value closer to 1, the ability of sharpening is stronger, but color of image can be made supersaturated, lacks the sense of reality；
The estimation method of atmosphere light intensity A is: first obtaining the maximum pixel of 0.1% brightness in image dark primary, then these pixels
Estimated value of the maximum value as A corresponding to pixel in original image；
Atmosphere light intensity A in the transmitance t (x) of estimation and formula (2) can be obtained into fog free images, actually in calculating, in order to
It avoids transmitance is too small from causing image fault, transmitance lower limit t is usually set0, then fog free images be
4. the adaptive clarification method according to claim 1 suitable for the more scene haze images of more concentration, feature
Be: the dark primary value adaptive acquiring method itself specific steps are as follows: to image pixel carry out category division, pass through
Make to divide obtained the distance between all kinds of maximums, determines optimal segmenting threshold；
The first step, it is assumed that image I has L gray level, and i-stage number of pixels is ni, total pixel number isThen i-stage
The probability that pixel occurs is pi=ni/N；
Second step divides the image into two class of A and B for the threshold value k of setting, and wherein A class gray scale interval is [0, k], B class gray scale
Section is [k+1, L-1], then the class interval whole image gray average μ, A gray average μA(k), the class interval B gray average μB(k)
It is respectively as follows:
The probability distribution of two class gray scale intervals meets:
Define inter-class variance when threshold value k is are as follows:
σ2(k)=pA[μ-μA(k)]2+pB[μ-μB(k)]2, (10)
According to maximum between-cluster variance criterion, from 0 to L-1 threshold value k is changed, finding out makes the maximum k value of variance in above formula, as most preferably
Segmentation threshold T；Change a subthreshold k due to every, it is above-mentioned it is various will all be recalculated, to reduce operation time, first according to mist
Haze image histogram low ebb gray feature, filters out several threshold points, and then substitution is above various, realizes quick OSTU threshold value point
It cuts；
Third step, according to gray level i and its pixel number niL relationship is established to [i, ni], it is assumed that curve [i, ni] on a certain position
Place's gray level is Tj, as its pixel number njWhen meeting following relationship simultaneously, by gray level TjScreening is threshold point:
In formula: Δ is screening window size, takes Δ=3pixels；α is that minimum value limits ratio, takes α=0.1%；
4th step, using improved quick OSTU threshold method to haze image carry out Threshold segmentation, according to its threshold point screening and
Bright dark areas segmentation result it can be seen that screening after threshold point only remain 49, participate in calculate threshold value points reduce 80% with
On；Meanwhile bright dark areas by effectively it is separated, facilitate subregion sharpening processing；
5th step, according to formula (3), the RGB triple channel minimum value of each pixel is dark primary value, it may be assumed that
If the haze image of various concentration is all made of above formula and solves dark primary value, bright area color distortion may result in；
This is because the minimum gradation value of bright area is higher with respect to dark areas, if directlying adopt above formula minimum value method seeks dark primary value,
Transmitance can be made to calculate inaccurate, and triple channel gray average is selected then more to reflect practical feelings as the dark primary value of bright area
Condition；For this purpose, correcting to dark primary minimum value, dark primary value is solved using following formula:
In formula: TcFor c channel segmentation threshold value, c ∈ { R, G, B }.
5. the adaptive clarification method according to claim 1 suitable for the more scene haze images of more concentration, feature
Be: the atmosphere light intensity adaptive estimation method itself specific steps are as follows: being carried out using bright dark subregion averaging method
Atmosphere light intensity ART network, as bright area pixel number accounting Pb cWhen < 10%, illustrates that bright area is less, first take dark primary at this time
Then 0.1% bright spot before image finds out the gray average of these bright spots corresponding pixel points in original image as atmosphere radiance estimate
Value；As bright area pixel number accounting Pb cWhen >=10%, illustrate that bright area is more, it is equal with the gray scale of all bright area pixels at this time
Value is used as atmosphere light intensity estimated value, and calculation formula is as follows:
In formula: NbFor bright area pixel number.
6. the adapting to image clarification method according to claim 1 suitable for the more scenes of more concentration, it is characterised in that:
The sharpening coefficient self-adaptive computing method itself specific steps are as follows: by various concentration haze image carry out histogram
Figure statistics, discovery haze concentration is bigger, and histogram distribution is more concentrated, and it is [m that most gray values, which concentrate on size,c, Mc] ash
It spends in section, using the property, proposes a kind of sharpening coefficient calculation method based on gray scale concentration degree, meet:
In formula: ωcFor minimum value regulation coefficient, value ωc=0.15；Bound mcAnd McMeet:
In formula: α is bound regulation coefficient, value α=1%；
By the above various corresponding substitution transmitance formula (6), the transmitance of the bright dark areas of haze image can be obtained；In conjunction with public affairs
Formula (7), it will be able to the preliminary image J obtained after restoring0。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910177950.4A CN109949239B (en) | 2019-03-11 | 2019-03-11 | Self-adaptive sharpening method suitable for multi-concentration multi-scene haze image |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910177950.4A CN109949239B (en) | 2019-03-11 | 2019-03-11 | Self-adaptive sharpening method suitable for multi-concentration multi-scene haze image |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109949239A true CN109949239A (en) | 2019-06-28 |
| CN109949239B CN109949239B (en) | 2023-06-16 |
Family
ID=67009534
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910177950.4A Active CN109949239B (en) | 2019-03-11 | 2019-03-11 | Self-adaptive sharpening method suitable for multi-concentration multi-scene haze image |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109949239B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110544009A (en) * | 2019-07-26 | 2019-12-06 | 中国人民解放军海军航空大学青岛校区 | Aviation organic coating aging damage quantitative evaluation method based on digital image processing |
| CN112330558A (en) * | 2020-11-05 | 2021-02-05 | 山东交通学院 | Road image recovery early warning system and method based on foggy weather environment perception |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106327439A (en) * | 2016-08-16 | 2017-01-11 | 华侨大学 | Rapid fog and haze image sharpening method |
| CN106940882A (en) * | 2017-02-15 | 2017-07-11 | 国网江苏省电力公司常州供电公司 | A kind of transformer substation video image clarification method for meeting human-eye visual characteristic |
| JP2018117211A (en) * | 2017-01-17 | 2018-07-26 | キヤノン株式会社 | Image processing apparatus and image processing method, imaging apparatus, program |
| CN109087254A (en) * | 2018-04-26 | 2018-12-25 | 长安大学 | Unmanned plane image haze sky and white area adaptive processing method |
-
2019
- 2019-03-11 CN CN201910177950.4A patent/CN109949239B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106327439A (en) * | 2016-08-16 | 2017-01-11 | 华侨大学 | Rapid fog and haze image sharpening method |
| JP2018117211A (en) * | 2017-01-17 | 2018-07-26 | キヤノン株式会社 | Image processing apparatus and image processing method, imaging apparatus, program |
| CN106940882A (en) * | 2017-02-15 | 2017-07-11 | 国网江苏省电力公司常州供电公司 | A kind of transformer substation video image clarification method for meeting human-eye visual characteristic |
| CN109087254A (en) * | 2018-04-26 | 2018-12-25 | 长安大学 | Unmanned plane image haze sky and white area adaptive processing method |
Non-Patent Citations (3)
| Title |
|---|
| 李云峰: "基于高亮区域自适应处理的监控图像去雾算法", 《计算机应用与软件》 * |
| 王泽胜: "基于改进暗通道先验的交通图像去雾新方法", 《控制与决策》 * |
| 邓莉: "针对明亮区域的自适应全局暗原色先验去雾", 《光学精密工程》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110544009A (en) * | 2019-07-26 | 2019-12-06 | 中国人民解放军海军航空大学青岛校区 | Aviation organic coating aging damage quantitative evaluation method based on digital image processing |
| CN110544009B (en) * | 2019-07-26 | 2022-12-09 | 中国人民解放军海军航空大学青岛校区 | Aviation organic coating aging damage quantitative evaluation method based on digital image processing |
| CN112330558A (en) * | 2020-11-05 | 2021-02-05 | 山东交通学院 | Road image recovery early warning system and method based on foggy weather environment perception |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109949239B (en) | 2023-06-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110599433B (en) | Double-exposure image fusion method based on dynamic scene | |
| CN106846263B (en) | Based on the image defogging method for merging channel and sky being immunized | |
| CN108765342A (en) | A kind of underwater image restoration method based on improvement dark | |
| CN108389175B (en) | Image defogging method integrating variation function and color attenuation prior | |
| CN108596853A (en) | Underwater picture Enhancement Method based on bias light statistical model and transmission map optimization | |
| CN109785262A (en) | Image defogging method based on dark channel prior and adaptive histogram equalization | |
| CN104867121B (en) | Image Quick demisting method based on dark primary priori and Retinex theories | |
| CN110288550B (en) | Single-image defogging method for generating countermeasure network based on priori knowledge guiding condition | |
| CN107103591A (en) | A kind of single image to the fog method based on image haze concentration sealing | |
| CN113822826B (en) | Low-illumination image brightness enhancement method | |
| CN115223004A (en) | Method for generating confrontation network image enhancement based on improved multi-scale fusion | |
| CN104766284A (en) | Underwater color blurred image restoration method | |
| CN111563852A (en) | Dark channel prior defogging method based on low-complexity MF | |
| CN106296618A (en) | A kind of color image defogging method based on Gaussian function weighted histogram regulation | |
| CN109949239A (en) | A kind of adaptive clarification method suitable for the more scene haze images of more concentration | |
| CN105447825A (en) | Image defogging method and system | |
| CN108765323A (en) | A kind of flexible defogging method based on improvement dark and image co-registration | |
| CN107977941B (en) | Image defogging method for color fidelity and contrast enhancement of bright area | |
| CN115482165A (en) | Image defogging method based on dark channel prior | |
| CN110349113A (en) | One kind being based on the improved adapting to image defogging method of dark primary priori | |
| CN109934779B (en) | Defogging method based on guided filtering optimization | |
| CN112070691A (en) | Image defogging method based on U-Net | |
| CN106327439B (en) | A kind of quick haze sky image clarification method | |
| CN110852971B (en) | Video defogging method based on dark channel prior and Retinex and storage medium | |
| CN112330568A (en) | Brightness compensation method for tobacco leaf image with uneven illumination |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |