CN106960421B - Night image defogging method based on statistical characteristics and brightness estimation - Google Patents

Night image defogging method based on statistical characteristics and brightness estimation Download PDF

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CN106960421B
CN106960421B CN201710152244.5A CN201710152244A CN106960421B CN 106960421 B CN106960421 B CN 106960421B CN 201710152244 A CN201710152244 A CN 201710152244A CN 106960421 B CN106960421 B CN 106960421B
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杨爱萍
杨苏辉
王南
赵美琪
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Abstract

The invention discloses a night image defogging method based on statistical characteristics and brightness estimation, which comprises the following steps of: inverting the foggy image at night to obtain an inverted image; calculating the atmospheric light with local color cast of the reverse image, and optimizing through guiding filtering; calculating the initial transmissivity of the three channels and the roughly estimated transmissivity of the three channels, correcting the roughly estimated transmissivity of the three channels by utilizing a bright channel of a reverse image, and obtaining optimized transmissivity through guiding filtering: solving the restored image by utilizing the atmosphere light with color cast locally and the optimized transmissivity, and then reversing again to obtain a defogged image with color cast at night; and carrying out color correction by adopting a local grey word to finally obtain a defogged image at night. The defogged image of the night image obtained by the invention not only can effectively recover the brightness and the contrast of the image, but also can effectively correct the color cast of the night image, obviously improve the visual effect, simultaneously reserve more image detail information and greatly reduce the operation complexity.

Description

Night image defogging method based on statistical characteristics and brightness estimation
Technical Field
The invention relates to a computer image processing method, in particular to a method for defogging an image at night.
Background
When the image is shot in a foggy environment at night, the whole gray value and the contrast of the image are reduced, a large amount of detail information is lost, an interested area is difficult to identify, and great difficulty is brought to video monitoring, outdoor target identification and tracking, remote sensing imaging and the like. Therefore, the problem of image defogging at night needs to be solved in the fields of computer vision application and digital image processing.
The existing method for defogging images at night is few, and mainly has Pei[1]Proposed night image defogging algorithm based on dark channel prior and color conversion, Zhang[2]Proposed defogging algorithm based on new model and Li[3]A hierarchical decomposition defogging algorithm based on relatively smooth constraint, and the like. The defogging main frame of the algorithms is still based on dark channel prior, but the defogging main frame is special due to the foggy image at nightThe imaging environment of (2) is not applicable to dark channel prior in the night environment, so the images restored by the algorithms are dark as a whole, have color distortion of different degrees, have obvious halo effect at an image light source, are not completely defogged, and are complex in calculation.
[ reference documents ]
[1]Pei S C,Lee T Y.Nighttime haze removal using color transfer pre-processing and dark channel prior[A].Proceedings of the IEEE InternationalConference on Image Processing[C].Orlando:IEEE Computer Society Press,2012,957-960。
[2]Zhang J,Cao Y,Wang Z.Nighttime haze removal based on a new imagingmodel[A].Proceedings of the IEEE International Conference on Image Processing[C].Paris:IEEE Computer Society Press,2014.4557-4561。
[3]Li Y,Tan R T,Brown S Michael.Nighttime haze remov-al with glow andmultiple light colors[C].Proceedings of IEEE International Conference onComputer Vi-sion.Santiago:IEEE Computer Society Press,2015:226-234。
[4]He K,Sun J,Tang X.Single image haze removal using dark channelprior[J].Pattern Analysis and Machine Intelligence,IEEE Transactions on,2011,33(12):2341-2353。
[5]G.Buchsbaum.A spatial processor model for object colourperception.Journal of the Franklin Institute,1980,310(80):1–26。
[6]Meng Gaofeng,WANG Ying,DUAN Jiangyong,et al.Efficient imagedehazing with boundary constraint and contextual regularization[C].IEEEInternational Con-ference on Computer Vision(ICCV),Sydney,Australia,2013:617-624。
[7]X.Dong,J.T.Wen,W.X.Li,An efficient and integrated algorithm forvideo enhancement in challenging lighting conditions,in Proceedings ofInstitute of Electrical and Electronic Engineers International Conference onComputer Vision and Pattern Recognition,pp.1241-1249,2011。
Disclosure of Invention
To the aboveThe invention provides a night image defogging method based on statistical characteristics and brightness estimation. Firstly, establishing a new night foggy image model with a color cast factor according to a special imaging environment of the night foggy image; then converting the defogging problem of the image with fog at night into the enhancement problem of the image with low illumination intensity by counting the histogram distribution of the bright channels of the reversed image of the image with fog at night and the image with low illumination intensity and utilizing improved He[4]The method estimates local atmospheric light with color cast, corrects the transmissivity through a bright channel of a reverse image of a foggy image at night to reserve more edge detail information of a defogged image, and finally passes through local grey-world[5]And carrying out color correction on the defogged image. The method for defogging the image at night can effectively restore the brightness and the contrast of the image, can effectively correct the color cast of the image at night, obviously improves the visual effect, simultaneously retains more image detail information and greatly reduces the operation complexity.
In order to solve the technical problem, the invention provides a night image defogging method based on statistical characteristics and brightness estimation, which comprises the following steps:
step 1, the input night fog image is an image I (x), and the image I (x) is inverted to obtain an inverted image
Figure BDA0001247191750000021
Figure BDA0001247191750000022
In the formula (1), c belongs to { r, g, b };
step 2, calculating a reverse image
Figure BDA0001247191750000023
Atmosphere light r with local color castL(x)AL(x) And optimized by guiding filtering:
Figure BDA0001247191750000024
in the formula (2), Ω (x) is a local neighborhood of the pixel x, Ω (y) is a local neighborhood of the neighborhood y, and GF represents guiding filtering;
step 3, calculating a reverse image
Figure BDA0001247191750000025
Initial transmittance t of three channels ofaL(x) And roughly estimated transmittance t of three channelsbL(x)：
Figure BDA0001247191750000026
Figure BDA0001247191750000027
Step 4, utilizing the reverse image
Figure BDA0001247191750000028
For the bright channel versus the three channelsbL(x) Correction is performed and optimization is performed by guided filtering:
Figure BDA0001247191750000029
tL(x)＝GF(eA_lighttbL(x)) (6)
in formulae (5) and (6): a _ light is a reverse image
Figure BDA00012471917500000210
Bright channel of, tL(x) For rough estimation of the transmission t for three channelsbL(x) The corrected optimized transmittance;
step 5, utilizing the atmosphere light r with partial color cast obtained in the step 2L(x)AL(x) And the optimized transmittance t obtained in step 4L(x) Solving the restored image rL(x)JL(x)：
Figure BDA0001247191750000031
In formula (7): epsilon is a fixed constant, epsilon is 0.1;
step 6, restoring the image rL(x)JL(x) Then the image is reversed again to obtain a defogged image J with color cast at nightp(x)：
Jp(x)＝255-r(x)J(x) (8)
Step 7, adopting local grey word to carry out defogging image J with color cast at nightp(x) Color correction is carried out to obtain a final night defogging image J (x),
Figure BDA0001247191750000032
in formula (9): ω represents the entire visible light range, λ is the wavelength of light, e (λ) represents the distribution of light of a certain wavelength band, s (x, λ) represents the reflectance of a certain point in space to a certain wavelength, p (λ) represents the light sensing characteristic of the camera to a certain light, and m is a constant between [0,1 ].
Compared with the prior art, the invention has the beneficial effects that:
the method comprises the steps of defogging the night image based on statistical characteristics, treating the night foggy image as a low-illumination image, solving local atmospheric light with color cast by using an improved He atmospheric light solving method, correcting the roughly estimated transmissivity by using a bright channel of a reverse image so as to further keep the detail and brightness information of the image, and finally performing color correction on the restored image through a local grey world so as to improve the robustness of the algorithm on the multi-light-source night foggy scene.
Drawings
FIG. 1(a) is a bright channel histogram of a reverse image of a foggy night image;
fig. 1(b) is a bright channel histogram of an inverted image of a low-illuminance image;
FIG. 2(a) is a night fogging image train;
FIG. 2(b) is the result of the defogging algorithm based on the new model in the document [2] after processing the images train;
FIG. 2(c) is the result of document [3] after processing the images train based on the hierarchical decomposition defogging algorithm with relatively smooth constraint;
FIG. 2(d) is the result of processing the images train by the method for defogging images at night according to the present invention;
FIG. 3(a) is a night fog image Street;
FIG. 3(b) is the result of document [2] after the image Street is processed by the defogging algorithm based on the new model;
FIG. 3(c) is the result of document [3] after processing the image Street based on the hierarchical decomposition defogging algorithm with relatively smooth constraint;
fig. 3(d) shows the result of processing the image Street by the night image defogging method according to the present invention.
Detailed Description
The technical solutions of the present invention are further described in detail with reference to the accompanying drawings and specific embodiments, which are only illustrative of the present invention and are not intended to limit the present invention.
The design idea of the night image defogging method is as follows:
1. the algorithm basic principle in the general night image defogging method is as follows: according to document [2], night fog image imaging model:
I(x)＝r(x)(J(x)t(x)+(1-t(x))A(x)) ①
wherein, I (x) is a fog image at night, J (x) is a fog image at night, t (x) is a transmittance, A (x) is a background light intensity, and r (x) is a color shift factor.
According to boundary constraints[6]Initial transmittance t of the available foggy day imagea(x) And roughly estimated transmittance tb(x)：
Figure BDA0001247191750000041
Figure BDA0001247191750000042
And further obtaining a clear image at night:
Figure BDA0001247191750000043
for low illumination image IL(x) Firstly, reversing the process:
RL(x)＝255-IL(x) ⑤
then R isL(x) Substitution of formula ④ yields:
Figure BDA0001247191750000044
to JL(x) And performing inversion again to obtain the final enhanced image.
2. The method comprises the steps of comparing histogram distribution of bright channels of 50 randomly selected night fogging images and reverse images of 50 low-illumination images, as shown in fig. 1(a) and fig. 1(b), finding that the night fogging images and the reverse images of the low-illumination images have great similarity, so that the problem of defogging of the night fogging images can be converted into the problem of enhancement of the low-illumination images, according to the document [7], regarding the reverse images of the low-illumination images as daytime fogging images, and similarly, regarding the reverse images of the night fogging images as daytime fogging images for processing, but because of the existence of color cast of the night environment, the reverse images of the night fogging images still have the properties of uneven illumination, color cast and the like, and the existing defogging algorithm is still not applicable.
The invention provides a night image defogging method based on statistical characteristics and brightness estimation, which comprises the following specific steps:
step 1, inputting a fog image at night as an image I (x), and inverting the image I (x)Obtaining a reverse image
Figure BDA0001247191750000051
Figure BDA0001247191750000052
In the formula (1), c belongs to { r, g, b }, and x is the position of a pixel in an image I (x);
step 2, calculating a reverse image
Figure BDA0001247191750000053
Atmosphere light r with local color castL(x)AL(x) And optimized by guiding filtering:
Figure BDA0001247191750000054
in the formula (2), Ω (x) is a local neighborhood of the pixel x, Ω (y) is a local neighborhood of the field y, and GF represents guiding filtering;
step 3, to make the solution of the transmittance more accurate, the document [6 ]]The method for calculating the transmissivity is improved, and the initial transmissivity t of three channels is calculatedaL(x) And roughly estimated transmittance t of three channelsbL(x)：
Figure BDA0001247191750000055
Figure BDA0001247191750000056
Step 4, using the reversed image to make the transmissivity smooth locally and keep good brightness characteristic
Figure BDA0001247191750000057
For the bright channel versus the three channelsbL(x) Correction is performed and optimization is performed by guided filtering:
Figure BDA0001247191750000058
tL(x)＝GF(eA_lighttbL(x)) (6)
in formulae (5) and (6): a _ light is a reverse image
Figure BDA0001247191750000059
Bright channel of, tL(x) For rough estimation of the transmission t for three channelsbL(x) The corrected optimized transmittance;
step 5, utilizing the atmosphere light r with partial color cast obtained in the step 2L(x)AL(x) And the optimized transmittance t obtained in step 4L(x) Solving the restored image rL(x)JL(x)：
Figure BDA00012471917500000510
In formula (7): ε is a fixed constant, preventing the denominator to be zero, and ε is taken to be 0.1.
Step 6, restoring the image rL(x)JL(x) Then the image is reversed again to obtain a defogged image J with color cast at nightp(x)：
Jp(x)＝255-r(x)J(x) (8)
Step 7, in order to improve the robustness of the algorithm to a scene with multiple light sources at night, a document [5 ] is adopted]The greyword algorithm proposed in the publication and the localized improvement are carried out on the defogged image J with color cast at nightp(x) Color correction is carried out to finally obtain a defogged image J (x) at night,
Figure BDA0001247191750000061
in formula (9): ω represents the entire visible light range, λ is the wavelength of light, e (λ) represents the distribution of light of a certain wavelength band, s (x, λ) represents the reflectance of a certain point in space to a certain wavelength, p (λ) represents the light sensing characteristic of the camera to a certain light, and m is a constant between [0,1 ].
In order to verify the effectiveness of the defogging method for the images at night, the defogging experiment is carried out on the images with the fog at night, and the defogging experiment is compared with a related algorithm. Fig. 2(a) is an image train with fog at night, fig. 2(b) is a defogging effect of the image train after being processed by adopting a defogging algorithm based on a new model proposed by a document [2], fig. 2(c) is a defogging effect of the image train after being processed by adopting a hierarchical decomposition defogging algorithm based on a relative smooth constraint proposed by a document [3], and fig. 2(d) is a defogging effect of the image train after being processed by the defogging method for the image at night. FIG. 3(a) is a night fogging image Street, FIG. 3(b) is a defogging result of the image Street after being processed by the defogging algorithm based on the new model proposed in the document [2], and FIG. 3(c) is a defogging result of the image Street after being processed by the hierarchical decomposition defogging algorithm based on the relative smoothness constraint proposed in the document [3 ]; fig. 3(d) shows the defogging result after the image Street is processed by the night image defogging method according to the invention. Compared with the defogging algorithm based on a new model and the hierarchical decomposition defogging algorithm based on the relative smooth constraint, which are proposed by the document [2] Zhang, the defogging algorithm based on the new model and the hierarchical decomposition defogging algorithm based on the relative smooth constraint, which is proposed by the document [3] Li, the night defogging image processed by the method for defogging the night image can be balanced and improved in brightness and contrast, the halo artifact caused by the light source region can be effectively removed, more image detail information can be recovered, the color cast of the image can be corrected, and the visibility of the visual effect can be better.
In order to objectively evaluate the night image defogging method, the color cast degree and the contrast of the night image defogging are calculated. As shown in table 1.
TABLE 1 Objective index comparison results
Figure BDA0001247191750000062
The data of the color cast degree in the table 1 shows that the method can effectively correct the color cast of the image; as can be seen from the contrast results, the method of the invention can improve the overall contrast of the defogged image.
Experimental results show that the night image defogging method based on the statistical characteristic and the brightness estimation can effectively correct color cast of the night image, retain more image details, improve the overall brightness and contrast of the image in a balanced manner and have better visual performance aiming at the defects of the traditional night image defogging method based on the dark primary color.
While the present invention has been described with reference to the accompanying drawings, the present invention is not limited to the above-described embodiments, which are illustrative only and not restrictive, and various modifications which do not depart from the spirit of the present invention and which are intended to be covered by the claims of the present invention may be made by those skilled in the art.

Claims (1)

1. A night image defogging method based on statistical characteristics and brightness estimation is characterized by comprising the following steps:
step 1, the input night fog image is an image I (x), and the image I (x) is inverted to obtain an inverted image
Figure FDA0002129585750000011
Figure FDA0002129585750000012
In the formula (1), c belongs to { r, g, b };
step 2, calculating a reverse image
Figure FDA0002129585750000013
Atmosphere light r with local color castL(x)AL(x) And optimized by guiding filtering:
Figure FDA0002129585750000014
in the formula (2), Ω (x) is a local neighborhood of the pixel x, Ω (y) is a local neighborhood of the neighborhood y, and GF represents guiding filtering;
step 3, calculating a reverse image
Figure FDA0002129585750000015
Initial transmittance t of three channels ofaL(x) And roughly estimated transmittance t of three channelsbL(x)：
Figure FDA0002129585750000016
Figure FDA0002129585750000017
Step 4, utilizing the reverse image
Figure FDA0002129585750000018
For the bright channel versus the three channelsbL(x) Correction is performed and optimization is performed by guided filtering:
Figure FDA0002129585750000019
tL(x)＝GF(eA_lighttbL(x)) (6)
in formulae (5) and (6): a _ light is a reverse image
Figure FDA00021295857500000110
Bright channel of, tL(x) For rough estimation of the transmission t for three channelsbL(x) The corrected optimized transmittance;
step 5, utilizing the atmosphere light r with partial color cast obtained in the step 2L(x)AL(x) And the optimized transmittance t obtained in step 4L(x) Solving the restored image rL(x)JL(x)：
Figure FDA00021295857500000111
In formula (7): epsilon is a fixed constant, epsilon is 0.1;
step 6, restoring the image rL(x)JL(x) Reversing again to obtain a color cast at nightDefogged image Jp(x)：
Jp(x)＝255-rL(x)JL(x) (8)
Step 7, adopting local grey word to carry out defogging image J with color cast at nightp(x) Color correction is carried out to obtain a final night defogging image J (x),
Figure FDA00021295857500000112
Figure FDA0002129585750000021
in formula (9): ω represents the entire visible light range, λ is the wavelength of light, e (λ) represents the distribution of light of a certain wavelength band, s (x, λ) represents the reflectance of a certain point in space to a certain wavelength, p (λ) represents the light sensing characteristic of the camera to a certain light, and m is a constant between [0,1 ].
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