CN113379619A - Integrated processing method for defogging imaging, visibility extraction and depth of field estimation - Google Patents
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Abstract
The invention relates to the technical field of image processing, in particular to an integrated processing method for defogging imaging, visibility extraction and depth of field estimation, which comprises the following steps: firstly, selecting a classical fogging model; secondly, obtaining the value of the atmospheric background light; thirdly, obtaining an estimated value of the transmissivity according to dark channel prior; fourthly, completing the recovery of the fog-free image; expressing the fogging model in a color space in a vector form, constructing an auxiliary line to solve the transmittance ratio of the same scene in two frames of images, and further solving the real color and the corresponding transmittance of the scene; sixthly, combining the change value of the distance between the scenery and the unmanned aerial vehicle to solve an atmospheric extinction coefficient, and calculating the visibility by using an empirical formula of the extinction coefficient and the visibility; and seventhly, solving the depth of the scene. The invention is beneficial to improving the aerial photography effect in the weather of haze and the like, and can also be used for visibility detection and reconnaissance ranging in traffic and environmental protection.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to an integrated processing method for defogging imaging, visibility extraction and depth of field estimation.
Background
Along with the development of unmanned aerial technology, unmanned aerial vehicles increasingly replace traditional manned spacecrafts by virtue of the advantages of safety, maneuverability and the like, and are applied to various scenes such as patrol, investigation and rescue, survey and the like. Unmanned aerial vehicle image quality of taking photo by plane easily receives the influence of haze weather, leads to the image quality of taking photo by plane to descend. The unmanned aerial vehicle aerial photography task effect is influenced, the working scene and time of the unmanned aerial vehicle are limited, and therefore important practical requirements are brought to the research of the image defogging algorithm. Subsequent scholars successively put forward methods for enhancing image contrast, mean defogging, median defogging and the like, wherein the methods have a certain defogging effect, but the results are often serious in color distortion. In 2009, a dark channel prior is proposed by doctor of hokeming and a defogging algorithm based on the dark channel prior is designed, and the algorithm still is a mainstream and classical defogging method by virtue of excellent effect and stability.
The dark channel defogging algorithm explains the principle that imaging colors are synthesized by scene real colors and atmospheric background light by utilizing atmospheric transmittance based on a physical model of atmospheric fogging, so that reduction of the fog real colors only requires solving two unknown parameters of the transmittance of an image and the atmospheric background light, wherein the transmittance is obtained by knowing the atmospheric background light. The algorithm considers the sky area in the image to be the bright area of the dark channel image, so the dark channel defogging algorithm considers the first 1% of the brightest pixels of the dark channel image to be the atmospheric background light; and estimating the transmittance by using the prior statistical rule of the dark channel and the value of the atmospheric background light. And utilizing the estimated value of the atmospheric background light and the estimated transmissivity to finish the defogging of the image. Because the dark channel defogging algorithm determines that the dark channel defogging algorithm depends on the sky area in the image when the atmospheric background light is estimated on the algorithm design, but the aerial image does not contain the sky area, the atmospheric background light in the aerial image is not estimated accurately, the defogging result is dim, the color is distorted, the detail identification degree is low, and a good result cannot be obtained, so that the design of the defogging algorithm suitable for the aerial image has urgent requirements.
Nowadays, unmanned aerial vehicle application demand presents the trend of function diversification, scene diversification, environment complication, and unmanned aerial vehicle technical development needs to move towards all-weather, multi-functional, wide field. In further research, it is found that in addition to trying to improve the defogging imaging effect of the aerial image, it is still rarely studied whether more functions can be mined for the physical process of atmospheric fog penetration imaging. According to the attenuation rule of light in atmospheric transmission, the distance between the atmospheric extinction coefficient and the light transmission directly influences the transmissivity of the scenery, so whether the atmospheric extinction coefficient and the scenery depth can be calculated by utilizing the process is worthy of being researched. Among the parameters of environment detection, the monitoring of visibility (affected by haze, dust and smog, and corresponding relation between atmospheric extinction coefficient and visibility) belongs to important parameters in atmospheric environment. Carry on visibility detection device and must consume the limited load of unmanned aerial vehicle, reduce the efficiency of unmanned aerial vehicle flight. Therefore, if visibility can be estimated from the aerial images, the visibility instrument can be not carried, environment-friendly monitoring can be carried out, unmanned aerial vehicle load is saved, and the flying efficiency of the unmanned aerial vehicle is improved; unmanned aerial vehicle all has the demand to scene depth measurement on applications such as survey and drawing, target tracking. If the depth of field can be estimated from the aerial image, a passive distance detection means can be directly provided without carrying equipment such as a binocular/multi-view camera, a radar and the like. As above-mentioned, be worth excavating visibility and distance information that bring to penetrating fog formation of image physical process, this will make unmanned aerial vehicle more worth in environmental protection monitoring field, provides the helping hand to unmanned aerial vehicle intelligent driving technical development simultaneously.
In summary, typical defogging algorithms in aerial image defogging applications are problematic and need to be improved. Meanwhile, the visibility and the scene depth are calculated by utilizing the atmospheric transmission process of light, so that the method has research and application values.
Disclosure of Invention
It is an object of the present invention to provide an integrated processing method for defogging imaging, visibility extraction and depth of field estimation that overcomes some or all of the deficiencies of the prior art.
The integrated processing method for defogging imaging, visibility extraction and depth of field estimation comprises the following steps:
selecting a classical fogging model I (x) t (x) D (x) plus (1-t (x)) A, wherein x is pixel coordinate, I (x) is observed foggy image, D (x) is natural color of the scene in non-attenuation state, A is atmosphere light background light, t (x) is transmissivity, wherein t (x) e-βd(x)β is the atmospheric extinction coefficient, d (x) is the distance of light transmission;
secondly, searching scenes which commonly appear in the two frames of changed images by using an image matching algorithm, and solving a value A of atmospheric background light by using a geometric relation between the difference of imaging colors of the same scenes in the two frames of images and chromatic aberration;
thirdly, obtaining the estimated value of the transmissivity t (x) according to the dark channel prior
Wherein: Ω is a square window centered at x, c is the color channel of D (x);
and fourthly, a deformation form of the fogging model:
completing the restoration of the fog-free image;
expressing the fogging model in a color space in a vector form, constructing an auxiliary line and solving the transmittance ratio t of the same scene in two frame images1(x)/t2(x) Further solving the real color D (x) and the corresponding transmittance t (x) of the scene;
sixthly, writing the transmittance column as a ratio formula:
where x represents the same scene in both frames, tx1Representing the transmittance of the scene x over the first of the two frame images; combined with the change value d of the scenery relative to the distance of the unmanned aerial vehicle1-d2The atmospheric extinction coefficient beta can be solved, and an empirical formula of the extinction coefficient and the visibility can be utilized
Visibility V can be calculated;
seventhly, the transmittance t (x) and the extinction coefficient β are known, and t (x) is equal to e-βd(x)And obtaining the depth d of the scenery.
Preferably, in step three, in the dark channel prior, the dark channel map is defined as a grayscale image composed of the lowest channel values of all pixels:
wherein D (x)darkIs a dark channel image, consisting of a dark channel prior D (x)dark→ 0, from which the estimated value of t (x) can be obtained
Is given by the following formula.
Preferably, in the fifth step, two frames of images with overlapped areas in the aerial image are selected for estimating the atmospheric background light A, SIFT operators are used for carrying out feature matching, the same points in the two images are found, and the points are respectively counted as t1(x)、t2(x) At this time, the color of the same object on the two images is represented as:
wherein, I isi(x)、Di(x) A is expressed as a space vector in the RGB color space as
pi＝ti(x)qi＝(1-ti(x))，pi+qi1 is ═ 1; wherein
Lie in a spatial plane; i is a reference number,
an unattenuated color vector representing a scene
Vector of ambient light
In the plane formed by the color vectors, all the synthesized different scenes actually image the color vectors caused by different transmittances;
in two adjacent images, an image matching algorithm can be used to obtain a plurality of pairs of point pairs which are in accordance with the description of the formula, and each pair of point pairs generates
In a space plane, there is a perpendicular line for each space plane
Perpendicular to
Then there are:
for several pairs of normal vectors to respective planes
Always have
Then the equation can be obtained:
by utilizing the rule of the above formula, the optimized formula and the fitting can be written
The direction of (a) is as follows:
wherein
For atmospheric background light
A unit vector of (a);
when the color of the scenery is combined with the atmospheric background light, the vector
The vector ends in the vector
On the connecting line of the terminal point, the equation can be written by using the geometrical relationship:
wherein a and b are constants, and a and b can be obtained by solving the above formula, wherein
Get the atmosphere by decompositionBackground light
At this time, t (x) is estimated
The right-hand variables of the expression (2) are known, and an estimated value of the transmittance of each point of the image is obtained
From pi+qiWork up to 1 gives the following formula:
the ratio of two formulas in the above formula is used for obtaining:
make a vector
Parallel line A' I of2Crossing OA with A', known as Δ OI1A1∽△A’I2A2(ii) a Then there are:
solving OA 'I and A' I by sine theorem2L, p is obtained from the relationship in the above formula1/p2A value of (d); further, the self color of the scenery can be solved
In the plane of each pair of matched pairs
Are all known; the corresponding transmittance t (x) at each point pair can be solved.
The invention provides a set of algorithm which can simultaneously realize three functions of unmanned aerial vehicle defogging imaging, visibility extraction and depth of field estimation without adding extra hardware equipment, so that unmanned aerial vehicle imaging can meet more diverse requirements of traffic monitoring, resource surveying, environmental protection monitoring and the like. Aiming at the condition that the traditional defogging algorithm is not suitable for aerial images, the invention provides an aerial video defogging imaging algorithm, and on the basis, the estimation of atmospheric visibility and depth of field is realized by further combining unmanned aerial data and defogging imaging calculation process data. The invention helps to improve aerial photography effect under weather such as haze and the like, and can also be used for traffic and environment-friendly visibility detection and reconnaissance ranging.
Drawings
Fig. 1 is a flowchart of an integrated processing method of defogging imaging, visibility extraction and depth of field estimation in embodiment 1;
FIG. 2 is a schematic plan view of the derived vector and the auxiliary lines in embodiment 1;
FIG. 3 shows each pair of the embodiments 1
Normal vector of the plane formed
And
a schematic vertical view;
FIG. 4 is a diagram illustrating the depth estimation and visibility estimation results in embodiment 1;
FIG. 5 is a graph showing the defogging results in example 1.
Detailed Description
For a further understanding of the invention, reference should be made to the following detailed description taken in conjunction with the accompanying drawings and examples. It should be understood that the examples are illustrative of the invention only and not limiting.
Example 1
As shown in fig. 1, the present embodiment provides an integrated processing method of defogging imaging, visibility extraction and depth of field estimation, which includes:
in computer vision, selecting a classic fogging model:
I(x)＝t(x)·D(x)+(1-t(x))·A (1)
where x is the pixel coordinate, I (x) is the observed foggy image, D (x) is the true color of the scene in the unattenuated state, A is the ambient background light, t (x) is the transmission, where t (x) has:
t(x)＝e-βd(x)，t(x)∈(0,1) (2)
beta is the atmospheric extinction coefficient, d (x) is the distance of light transmission;
as shown in the formula (1), the color I (x) of the scene after being attenuated by atmospheric transmission is a linear superposition of the scene natural color D (x) and the atmospheric background light A about t (x).
And searching scenes which commonly appear in the two frames of changed images by using an image matching algorithm, and solving the value A of the atmospheric background light by using the geometric relationship between the difference of imaging colors of the same scenes in the two frames of images and chromatic aberration.
Dark channels a priori assume that there is a channel with a low or even near zero intensity value in the RGB three color channels for most pixels in a statistically fog-free image. Based on this statistical rule, the dark channel map is defined as the grayscale image composed of the values of the lowest channel of all pixels:
wherein: Ω is a square window centered at x, c is d (x) a defined color channel; d (x)darkIs a dark channel image, consisting of a dark channel prior D (x)dark→ 0, from which an estimated value of t (x) can be obtained
Is represented by the formula:
in the embodiment, two frames of images with overlapped areas in the aerial image are selected for estimating the atmospheric background light A, SIFT operators are used for carrying out feature matching, the same points in the two images are found, the optical path difference from the same object to the airborne camera is different due to the movement of the unmanned aerial vehicle, and the difference of t (x) is known by the formula (2), wherein t is t1(x)、t2(x) At this time, the color of the same object on the two images is represented as:
wherein, I isi(x)、Di(x) A is expressed as a space vector in the RGB color space as
pi＝ti(x)qi＝(1-ti(x))，pi+qi1 is ═ 1; wherein
Lie in a spatial plane; i is a reference number,
an unattenuated color vector representing a scene
Vector of ambient light
In the plane formed by the color vectors, all the synthesized different scenes actually image the color vectors caused by different transmittances; two possible scene imaging color vectors, the scene self color vector and the atmospheric background light vector are selected and respectively represented as figure 2
The vector triangle synthesis process is shown as dashed line type 1 in fig. 2.
In two adjacent images, an image matching algorithm can be used to obtain a plurality of pairs of point pairs according to the description of the formula (5), and each pair of point pairs generates
In one spatial plane, there is a perpendicular line of each spatial plane
Perpendicular to
As shown in fig. 3, there are:
for several pairs of normal vectors to respective planes
Always have
Then the equation can be obtained:
by using the rule of formula (7), the optimized formula can be written and fitted
The direction of (a) is as follows:
wherein
For atmospheric background light
A unit vector of (a);
due to the definition of the relation p1+q21, so that when the color of the scene itself is combined with the atmospheric background light according to equation (5), the vector
The vector ends in the vector
The line of termination is shown above the dashed line 2 in FIG. 2.
The equations can be written in terms of geometric relationships:
wherein a and b are constants, and a and b can be obtained by solving the formula (9), wherein
Get the atmosphere background light
At this time, t (x) is estimated
The right variables of the expression are known, and the estimated value of the transmittance of each point of the image is obtained
Carrying out deformation by using the formula (1) to obtain a fog-free image recovery formula:
and finishing the defogging work of the image.
From pi+qiWork-up of formula (5) to 1 gives the following formula:
the ratio is obtained by using two formulas in formula (11):
make a vector
Parallel line A' I of2Crossing OA at A', as shown by the dotted line 3 in FIG. 1, Δ OI is shown1A1∽△A’I2A2(ii) a Then there are:
solving OA 'I and A' I by sine theorem2L, p is obtained from the relationship in the formula (13)1/p2A value of (d); further, the self color of the scene can be solved by the formula (12)
To this end, each pair of matched pairs is in the plane
Are all known; the corresponding transmittance t (x) at each dot pair can be solved by equation (5).
The formula (2) can be obtained by comparing at different distances:
as can be seen from the formula (14), p is used1/p2In combination with the change d of the scene relative to the range of the drone1-d2The atmospheric extinction coefficient beta can be solved, and an empirical formula of the atmospheric extinction coefficient and visibility is utilized:
the visibility V in the image at that time can be calculated.
The transmittance t (x) and the extinction coefficient β are known, and the depth d of the subject is obtained by the formula (2).
Fig. 4 shows the depth estimation and visibility estimation results, with the depth estimation result on the left and the visibility estimation result on the right. In fig. 5, the original image is on the left, the defogging result (middle) by the classical algorithm is in the middle, and the defogging result on the right is on the right. As can be seen from fig. 4 and 5, the method is helpful for improving aerial photography effects in weather such as haze and the like, and can also be used for visibility detection and reconnaissance ranging in traffic and environmental protection, so as to realize estimation of atmospheric visibility and depth of field.
The embodiment provides an image processing method for an unmanned aerial vehicle, but is not limited to unmanned aerial vehicle scenes.
The present invention and its embodiments have been described above schematically, and the description is not intended to be limiting, and what is shown in the drawings is only one embodiment of the present invention, and the actual structure is not limited thereto. Therefore, if the person skilled in the art receives the teaching, without departing from the spirit of the invention, the person skilled in the art shall not inventively design the similar structural modes and embodiments to the technical solution, but shall fall within the scope of the invention.
Claims (3)
1. The integrated processing method of defogging imaging, visibility extraction and depth of field estimation is characterized by comprising the following steps of: the method comprises the following steps:
selecting a classical fogging model I (x) t (x) D (x) plus (1-t (x) A, wherein x is an imagePixel coordinates, i (x) is the observed foggy image, d (x) is the true color of the scene in the unattenuated state, a is the ambient light background light, t (x) is the transmittance, where t (x) e-βd(x)β is the atmospheric extinction coefficient, d (x) is the distance of light transmission;
secondly, searching scenes which commonly appear in the two frames of changed images by using an image matching algorithm, and solving a value A of atmospheric background light by using a geometric relation between the difference of imaging colors of the same scenes in the two frames of images and chromatic aberration;
thirdly, obtaining the estimated value of the transmissivity t (x) according to the dark channel prior
Wherein: Ω is a square window centered at x, c is the color channel of D (x);
and fourthly, a deformation form of the fogging model:
completing the restoration of the fog-free image;
expressing the fogging model in a color space in a vector form, constructing an auxiliary line and solving the transmittance ratio t of the same scene in two frame images1(x)/t2(x) Further solving the real color D (x) and the corresponding transmittance t (x) of the scene;
sixthly, writing the transmittance column as a ratio formula:
where x represents the same scene in both frames, tx1Representing the transmission of a scene x in the first of two frame imagesRate; combined with the change value d of the scenery relative to the distance of the unmanned aerial vehicle1-d2The atmospheric extinction coefficient beta can be solved, and an empirical formula of the extinction coefficient and the visibility can be utilized
Visibility V can be calculated;
seventhly, the transmittance t (x) and the extinction coefficient β are known, and t (x) is equal to e-βd(x)And obtaining the depth d of the scenery.
2. The integrated processing method of defogging imaging, visibility extraction and depth estimation according to claim 1, wherein: in step three, in the dark channel prior, the dark channel map is defined as a gray image composed of the values of the lowest channel of all pixels:
wherein D (x)darkIs a dark channel image, consisting of a dark channel prior D (x)dark→ 0, from which an estimated value of t (x) can be obtained
Is given by the following formula.
3. The integrated processing method of defogging imaging, visibility extraction and depth estimation according to claim 1, wherein: in the fifth step, two frames of images with overlapped areas in the aerial image are selected for estimating the atmospheric background light A, SIFT operators are used for carrying out feature matching, the same points in the two images are found, and the points are respectively counted as t1(x)、t2(x) At this time, the color of the same object on the two images is represented as:
wherein, I isi(x)、Di(x) A is expressed as a space vector in the RGB color space as
pi＝ti(x)qi＝(1-ti(x))，pi+qi1 is ═ 1; wherein
Lie in a spatial plane; i is a reference number, i is a symbol,
an unattenuated color vector representing a scene
Vector of ambient light
In the formed plane, all the synthesized different scene actual imaging color vectors caused by different transmittances;
in two adjacent images, an image matching algorithm can be used to obtain a plurality of pairs of point pairs which are in accordance with the description of the formula, and each pair of point pairs generates
In a space plane, there is a perpendicular line for each space plane
Perpendicular to
Then there are:
for several pairs of normal vectors to respective planes
Always have
Then the equation can be obtained:
by utilizing the rule of the above formula, the optimized formula and the fitting can be written
The direction of (a) is as follows:
wherein
For atmospheric background light
A unit vector of (a);
when the color of the scenery is combined with the atmospheric background light, the vector
The vector ends in the vector
Above the line connecting the endpoints, the equations can be written as follows using the geometrical relationship:
wherein a and b are constants, and a and b can be obtained by solving the above formula, wherein
Get the atmospheric background light
At this time, t (x) is estimated
The right-hand variables of the expression (2) are known, and an estimated value of the transmittance of each point of the image is obtained
From pi+qiWork up to 1 gives the following formula:
the ratio of two formulas in the above formula is used for obtaining:
make a vector
Parallel line A' I of2Crossing OA with A', known as Δ OI1A1∽△A’I2A2(ii) a Then there are:
solving OA 'I and A' I by sine theorem2L, p is obtained from the relationship in the above formula1/p2A value of (d); further, the self color of the scenery can be solved
In the plane of each pair of matched pairs
Are all known; the corresponding transmittance t (x) at each point pair can be solved.
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| CN116664448A (en) * | 2023-07-24 | 2023-08-29 | 南京邮电大学 | Medium-high visibility calculation method and system based on image defogging |
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