CN102063706A - Rapid defogging method - Google Patents

Rapid defogging method Download PDF

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CN102063706A
CN102063706A CN 201010620645 CN201010620645A CN102063706A CN 102063706 A CN102063706 A CN 102063706A CN 201010620645 CN201010620645 CN 201010620645 CN 201010620645 A CN201010620645 A CN 201010620645A CN 102063706 A CN102063706 A CN 102063706A
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image
max
transmissivity
dark
mist
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CN 201010620645
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CN102063706B (en
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王好贤
毛兴鹏
李方
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哈尔滨工业大学(威海)
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Abstract

The invention relates to a rapid defogging method, comprising the following steps of: solving for the RGB (Red Green Blue) minimum of each pixel point to obtain a grayscale image, carrying out minimum filtering on the grayscale image to obtain a dark primary-color image, estimating atmospheric parameters by using the dark primary-color image, uniformly selecting some points in the image to solve for transmissivity, carrying out binary bilinear regression analysis to obtain the transmissivity of all points of the image, and recovering a fog-free image according to the transmissivity and the atmospheric parameters. The algorithm provided by the invention has the characteristics of good processing effect and high speed, and can be easily applied to systems with higher requirements on real-time property.

Description

A kind of quick defogging method capable
Technical field:
The invention belongs to the digital image processing techniques field, a kind of specifically quick defogging method capable is used for the improvement that computer vision field has mist picture quality.
Background technology:
Under the greasy weather situation, because the low visibility of scene, features such as target contrast and color are attenuated, and cause the life outdoor videos supervisory system can't operate as normal, so need handle the influence that brings with elimination weather to video and image, so the image mist elimination is treated as the emphasis of people's research.
Currently roughly can be divided into two classes for the mist image process method: a class is based on the method for figure image intensifying, and these class methods are not considered the forming process that the mist image is concrete, only chooses that interested part strengthens in the image.Image enchancing method commonly used mainly contains histogram equalization, homomorphic filtering and Retihex algorithm etc., these class methods are not considered the corresponding relation of the Misty Image contrast and the scenery degree of depth, the enhancing effect of scenic focal point object depth degree variation image greatly is undesirable, and the operand of homomorphic filtering and Retinex algorithm is very big, can not satisfy the requirement of real-time processing.
Defogging method capable based on the mist model is the mist image to be carried out once opposite with imaging inverse process recover not have the mist image.Two transmissivities that primary unknowns is atmospheric parameter and image of mist model, the wherein degree of depth exponent function relation of transmissivity and image.Common defogging method capable is earlier atmospheric parameter and transmissivity to be estimated, recovers not have the mist image according to imaging model then.Compare the method for figure image intensifying, these class methods are with strong points, and the image that obtains is more natural, and generally do not have information loss, can obtain good mist elimination effect.But this class methods calculated amount is all very big, handles a sub-picture and need expend a large amount of time, is difficult to requirement of real time.The He Kaiming of Hong Kong Chinese University etc. has proposed a kind of defogging method capable (reference papers " Single Image Haze Removal Using Dark Channel Prior ") based on dark primary priori, the mist elimination effect of this method is pretty good, but need carry out soft stingy figure when obtaining accurate transmissivity handles, the calculated amount of soft stingy figure is very huge, has limited the widespread use of this algorithm in the engineering field.
The present invention is based on the quick mist elimination algorithm of dark primary priori, and mist elimination is effective and speed is fast, can the higher occasion of requirement of real time.
Summary of the invention:
The purpose of this invention is to provide a kind of quick defogging method capable, not only can effectively improve problems such as greasy weather picture contrast that gas obtains decline and cross-color are arranged, and processing speed is fast, can the higher occasion of requirement of real time.
The technical solution used in the present invention is:
The first step: read the original mist image I that has;
Second step: ask R, the G of each pixel among the original image I, the minimum value of three passages of B, and assignment gives current pixel point, the gray level image that obtains is designated as I g, if I is gray level image, then I g=I;
The 3rd step: the I that utilizes formula (1) that second step was obtained gImage carries out filtering, and (x is that (x, pixel y) are that the size at center is the masterplate zone of N*N, generally get N ∈ (9,21), and N is big more, and the atmospheric parameter of trying to achieve is more little, and the overall brightness that obtains not having the mist image is bright relatively more with coordinate y) to definition Ω;
I g dark ( x , y ) = min ( i , j ) ∈ Ω ( x , y ) ( I g ( i , j ) ) - - - ( 1 )
The 4th step: obtain I g DarkPosition coordinates (the x of the pixel of intensity maximum in the image Max, y Max), if I is coloured image, atmospheric parameter A then R=I R(x Max, y Max), A G=I G(x Max, y Max) and A B=I B(x Max, y Max), I wherein R, I GAnd I BBe respectively R, G and the B passage of coloured image I, if I is a gray level image, its atmospheric parameter A Gray=I (x Max, y Max);
The 5th step: ask for the length h and the width w of image, utilize formula (2) to I g DarkPoint (the x that satisfies condition in the image, y) ask the value of its transmissivity, wherein x=1,1+S, 1+2*S ..., 1+n*S, h, y=1,1+S, 1+2*S ..., 1+m*S, w, general step-length S ∈ (40,100), S is big more, and the processing speed in the 6th step is fast more, but the coefficient of the regression equation of estimating is relatively not too accurate, mist elimination effect relative mistake is a little, if I is a coloured image, then in the formula (2) As if I is gray level image, then A=A in the formula (2) Gray, ω 0 is the adjusting parameter, general ω 0 ∈ (0,1), and the big more mist elimination effect of ω 0 is good more;
t ( x , y ) = 1 - ω 0 ( I g dark ( x , y ) A ) - - - ( 2 )
The 6th step: pixel and the corresponding transmissivity of utilizing the dihydric phenol linear regression analysis that the 5th step was related to are carried out regretional analysis, obtain the coefficient b suc as formula the equation of (3) 0, b 1, b 2, b 3, b 4And b 5
t=b 0+b 1*x+b 2*y+b 3*x 2+b 4*xy+b 5*y 2 (3)
The 7th step: utilize formula (3) to obtain the transmissivity t of all pixels;
The 8th step: utilize formula (4) to recover no mist image, if I is a coloured image, then utilize formula (4) at R, G, three passages of B respectively, wherein A gets A respectively R, A GAnd A B, I gets I respectively R, I GAnd I B, then obtain three passage J of J respectively R, J GAnd J B, if I is gray level image, then A=A Gray
J ( x ) = I ( x ) - A ( 1 - t ( x ) ) t ( x ) - - - ( 4 )
The present invention is a kind of quick defogging method capable, and compared with prior art, advantage is:
1, both can handle coloured image and also can handle gray level image;
2, algorithm process is effective, and by the contrast effect figure of Fig. 2 (a) and Fig. 2 (b) as can be seen, through after this algorithm process, the tree in a distant place, street lamp and billboard have all shown clearly, and do not have distortion.Fig. 3 (b) is the result of histogram equalization, the result that Fig. 3 (c) handles for the method that adopts He Kaiming, Fig. 3 (d) is the result of this algorithm, by the result of Fig. 3 as can be seen, this algorithm is suitable with the result of the method for He Kaiming, and the histogram equalization method has serious distortion at regional area.
3, this algorithm process speed is very fast, can realize that mist elimination is handled fast.The method that table 1 is depicted as this algorithm and He Kaiming is handled the contrast of the time complexity of Fig. 3 (a) equally, and the method that this algorithm adopts binary linear regression to analyze has been saved a large amount of operation time with respect to the soft stingy drawing method of He Kaiming, and processing speed is fast.Testing used is the Pentium 1.8G computing machine of 1G internal memory, the matlab code is not optimized, and shows that this algorithm can be applied in the real-time system.
Table 1 algorithm contrast table working time
Description of drawings
Fig. 1 is a process flow diagram of the present invention;
Fig. 2 (a) is the pending original mist image that has;
The no mist image that Fig. 2 (b) obtains after handling for the present invention;
Fig. 3 (a) is the pending original mist image that has;
Fig. 3 (b) is a histogram equalization method result;
Why triumphant bright method is handled the back result to Fig. 3 (c);
Fig. 3 (d) is a result of the present invention.
Embodiment:
Below in conjunction with instantiation the present invention is elaborated.
Example 1: this example is to carry out the process that mist elimination is handled at gray level image, and detailed process is as follows.
1: original have reading in of misty grey degree image, and gray level image is designated as I.
2: (x y) carries out the minimum value Filtering Processing, obtains I to each pixel I among the I g Dark(x, y), computing formula is as follows:
I g dark ( x , y ) = min ( i , j ) ∈ Ω ( x , y ) ( I ( i , j ) )
Wherein (x is that (x is the small images of the N*N size at center y), gets N=15 in this example, and we claim I with pixel I y) to Ω g DarkBe the dark primary image, carry out the estimation of atmospheric parameter A by the dark primary image.
3: find I g DarkIn maximum pixel, and write down position coordinates (x in its place image m, y m), being calculated as follows of atmospheric parameter A then:
A=I(x m,y m)
4: obtain the length h and the width w of image, evenly choose the part point in the image and calculate its transmissivity, x and all combinations of y in following two conditions are satisfied in being chosen for of the coordinate of point:
a:x=1、1+S、1+2*S、…、1+n*S、h;
b:y=1、1+S、1+2*S、…、1+m*S、w;
General S ∈ (40,100), S gets 40 in this example.
Point (x, the computing formula of the transmissivity of y) locating is as follows:
t ( x , y ) = 1 - ω 0 ( I g dark ( x , y ) A )
I wherein g Dark(x y) is the pixel of dark primary image, and A is the atmospheric parameter of trying to achieve in the 3rd step, and ω 0 is for regulating parameter, general ω 0 ∈ (0,1), ω 0=0.9 in this example.
5: utilize the dihydric phenol linear regression analysis that point and corresponding transmissivity in 4 are carried out regretional analysis, to obtain coefficient b 0, b 1, b 2, b 3, b 4And b 5, and obtain the transmissivity that other are had a few according to regression function, regression function is as follows:
t=b 0+b 1*x+b 2*y+b 3*x 2+b 4*xy+b 5*y 2
B wherein 0, b 1, b 2, b 3, b 4And b 5Be respectively regression coefficient, transmissivity t is the quadratic function of coordinated indexing x and y.
6: calculate no mist image J (x) according to atmospheric parameter A that obtains above and transmissivity t, computing formula is as follows:
J ( x ) = I ( x ) - A ( 1 - t ( x ) ) t ( x )
Wherein I (x) is the original mist image that has, and the atmospheric parameter that A obtained in the 3rd step, t are the transmissivity of obtaining in the 5th step.
Example 2: this example is to carry out the process that mist elimination is handled at coloured image, and detailed process is as follows.
1: original have reading in of mist coloured image, and image is designated as I.
2: to each pixel among the I, ask the minimum value of its R, G, three passages of B, and assignment gives current pixel point, obtain a gray level image after the processing, be designated as I g, computing formula is as follows:
I g ( x , y ) = min c ∈ { R , G , B } ( I c ( x , y ) )
Wherein R, G and B are three passages of pixel I, I R(x, y), I G(x, y) and I B(x y) is respectively pixel I (x, the value of three passages y).
3: to I gIn each pixel I g(x y) carries out the minimum value Filtering Processing, obtains I g Dark(x, y), computing formula is as follows:
I g dark ( x , y ) = min ( i , j ) ∈ Ω ( x , y ) ( I g ( i , j ) )
Wherein (x is with pixel I y) to Ω g(x y) is the small images of the N*N size at center, gets N=15 in this example, and we claim I g DarkBe the dark primary image, carry out the estimation of atmospheric parameter A by the dark primary image.
4: find I g DarkIn maximum pixel, and write down position coordinates (x in its place image m, y m), being calculated as follows of atmospheric parameter A then:
A R=I R(x max,y max)
A G=I G(x max,y max)
A B=I B(x max,y max)
I wherein R, I GAnd I BBe respectively R, G and three passages of B of original image I, A R, A GAnd A BThree passages for atmospheric parameter A.
5: obtain the length h and the width w of image, evenly choose the part point in the image and calculate its transmissivity.
Being chosen for of coordinate of point satisfied x and all combinations of y in following two conditions:
a:x=1、1+S、1+2*S、…、1+n*S、h;
b:y=1、1+S、1+2*S、…、1+m*S、w;
General S ∈ (40,100), S gets 40 in this example.
Point (x, the computing formula of the transmissivity of y) locating is as follows:
t ( x , y ) = 1 - ω 0 ( I g dark ( x , y ) A )
I wherein g Dark(x y) is the pixel of dark primary image, and A is the atmospheric parameter of trying to achieve in the 3rd step, and ω 0 is for regulating parameter, general ω 0 ∈ (0,1), ω 0=0.9 in this example.
6: utilize the dihydric phenol linear regression analysis that point and corresponding transmissivity in 5 are carried out regretional analysis, to obtain coefficient b 0, b 1, b 2, b 3, b 4And b 5, and obtain the transmissivity that other are had a few according to regression function, regression function is as follows:
t=b 0+b 1*x+b 2*y+b 3*x 2+b 4*xy+b 5*y 2
B wherein 0, b 1, b 2, b 3, b 4And b 5Be respectively regression coefficient, transmissivity t is the quadratic function of coordinated indexing x and y.
7: calculate no mist image J (x) according to atmospheric parameter A that obtains above and transmissivity t, computing formula is as follows:
J R ( x ) = I R ( x ) - A R ( 1 - t ( x ) ) t ( x )
J G ( x ) = I G ( x ) - A G ( 1 - t ( x ) ) t ( x )
J B ( x ) = I B ( x ) - A B ( 1 - t ( x ) ) t ( x )
I wherein R, I GAnd I BBe respectively R, G and three passages of B of original image I, A R, A GAnd A BBe the atmospheric parameter of obtaining in the 4th step, t is the transmissivity of obtaining in the 6th step, the J that obtains R, J GAnd J BThree passages for no mist coloured image.
Though two examples of the present invention only have been described here, and meaning is not to limit the scope of the invention and applicability.On the contrary, the detailed description to example can make those skilled in the art better be implemented.

Claims (1)

1. a quick defogging method capable is characterized in that, this method comprises the steps:
The first step: read the original mist image I that has;
Second step: ask R, the G of each pixel among the original image I, the minimum value of three passages of B, and assignment gives current pixel point, the gray level image that obtains is designated as I g, if I is gray level image, then I g=I;
The 3rd step: the I that utilizes formula (1) that second step was obtained gImage carries out filtering, and (x is that (x, pixel y) are that the size at center is the masterplate zone of N*N, generally get N ∈ (9.21), and N is big more, and the atmospheric parameter of trying to achieve is more little, and the overall brightness that obtains not having the mist image is bright relatively more with coordinate y) to definition Ω;
I g dark ( x , y ) = min ( i , j ) ∈ Ω ( x , y ) ( I g ( i , j ) ) - - - ( 1 )
The 4th step: obtain I g DarkPosition coordinates (the x of the pixel of intensity maximum in the image Max, y Max), if I is coloured image, atmospheric parameter A then R=I R(x Max, y Max), A G=I G(x Max, y Max) and A B=I B(x Max, y Max), I wherein R, I GAnd I BBe respectively R, G and the B passage of coloured image I, if I is a gray level image, its atmospheric parameter A Gray=I (x Max, y Max);
The 5th step: ask for the length h and the width w of image, utilize formula (2) to I g DarkPoint (the x that satisfies condition in the image, y) ask the value of its transmissivity, wherein x=1,1+S, 1+2*S ..., 1+n*S, h, y=1,1+S, 1+2*S ..., 1+m*S, w, general step-length S ∈ (40,100), S is big more, and the processing speed in the 6th step is fast more, but the coefficient of the regression equation of estimating is relatively not too accurate, mist elimination effect relative mistake is a little, if I is a coloured image, then in the formula (2) As if I is gray level image, then A=A in the formula (2) Gray, ω 0 is the adjusting parameter, general ω 0 ∈ (0,1), and the big more mist elimination effect of ω 0 is good more;
t ( x , y ) = 1 - ω 0 ( I g dark ( x , y ) A ) - - - ( 2 )
The 6th step: pixel and the corresponding transmissivity of utilizing the dihydric phenol linear regression analysis that the 5th step was related to are carried out regretional analysis, obtain the coefficient b suc as formula the equation of (3) 0, b 1, b 2, b 3, b 4And b 5
t=b 0+b 1*x+b 2*y+b 3*x 2+b 4*xy+b 5*y 2 (3)
The 7th step: utilize formula (3) to obtain the transmissivity t of all pixels;
The 8th step: utilize formula (4) to recover no mist image, if I is a coloured image, then utilize formula (4) at R, G, three passages of B respectively, wherein A gets A respectively R, A GAnd A B, I gets I respectively R, I GAnd I B, then obtain three passage J of J respectively R, J GAnd J B, if I is gray level image, then A=A Gray
J ( x ) = I ( x ) - A ( 1 - t ( x ) ) t ( x ) - - - ( 4 )
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