CN111553862A - Sea-sky background image defogging and binocular stereo vision positioning method - Google Patents
Sea-sky background image defogging and binocular stereo vision positioning method Download PDFInfo
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Abstract
The invention discloses a sea-sky background image defogging and binocular stereo vision positioning method, which belongs to the field of computer vision, and is characterized in that on the basis of a dark channel defogging model, a sky region and a non-sky region in an image shot by a binocular camera are segmented according to the characteristics of a sea-sky background image, a quartering method is utilized to determine a final region of an atmospheric light value estimated value, the condition that a single value is easily influenced by an external random condition is avoided, and the average value of all pixels in the selected final region is used as the atmospheric light value of an optimization model; and then, obtaining areas with similar depth of field and fog concentration by utilizing super-pixel segmentation, constructing cost functions of the contrast and the information loss of the weighing image, calculating the minimum value of the cost function of each area as the transmittance estimated value of the area, and then refining the transmittance estimated value by adopting guide filtering to obtain the transmittance value of the optimization model.
Description
Technical Field
The invention relates to the field of computer vision, in particular to a method for defogging a sea and sky background image and positioning binocular stereoscopic vision.
Background
Image defogging is an indispensable technology in the fields of remote monitoring and computer vision. Under severe weather conditions, such as heavy rain and haze weather, characteristic information attenuation or loss easily occurs in images acquired by a vision system. Compared with the land, the marine environment is complex and changeable, and the sea fog weather is more common weather, so that the vision system of the marine unmanned aircraft is influenced by the sea fog weather when acquiring image information and video images, the phenomena of reduced contrast, color distortion, blurring and the like are generated, the precision of a disparity map obtained by object identification and stereo matching is seriously influenced, and the positioning accuracy is further influenced. When the unmanned aircraft observes at a long distance, the target object happens to be in a sea-sky junction area, and the defogging effect of the image can be influenced due to the unique characteristic of the marine image.
He et al proposed a dark channel theory which is a defogging method based on image restoration, and authors have adopted a lot of experiments to prove that the method is ideal for defogging of a foggy image, and becomes an important research direction of the image restoration defogging method. However, the dark channel defogging algorithm generates a transition region and a color cast phenomenon when acting on the sky region, so that the defogging effect of the sky region is not ideal. Aiming at the problem, improved methods based on He dark channel defogging algorithms emerge, and the improved algorithms mostly achieve the effect of improving the defogging effect of the sky area by weakening the treatment on the sky area, but the method can also weaken the area connected with the sky and lose the detail information of the area.
Disclosure of Invention
According to the problems in the prior art, the invention discloses a method for defogging a sea and sky background image and positioning binocular stereoscopic vision, which comprises the following steps:
s1, acquiring an original foggy image by using a binocular camera;
s2, segmenting a sky region and a non-sky region in the original foggy image, and optimizing the values of atmospheric light values and transmissivity on the basis of a dark channel defogging model to obtain an improved dark channel defogging model;
s3, carrying out defogging treatment on the original foggy image by adopting an improved dark channel defogging model to obtain a defogged image;
s4, carrying out stereo matching on the defogged left image and the defogged right image to obtain a disparity map;
and S5, obtaining the distance between the binocular camera and the target object according to the relation between the parallax map and the depth, and realizing target positioning.
Further, the atmospheric light value taking method comprises the following steps:
s2-1, dividing the sky area of the original foggy image by a quartering method;
s2-2, taking the difference between the average value of the pixels in the area and the standard deviation of the pixels as a measurement standard, and selecting the area with the largest difference in the divided sky area;
s2-3, further dividing the area with the largest difference, selecting the area with the largest difference again until the finally divided area is smaller than a preset threshold value, and taking the area as a final area of the pre-estimation of the atmospheric light value;
and S2-4, selecting the average value of the pixels of the area as the estimated value of the optimal atmosphere light value.
Further, the formula of the estimated value of the optimal atmospheric light value is as follows:
where n is the number of pixels in the region and S (x) is the sum of all pixel values in the region.
Further, the transmittance optimization step is as follows:
s3-1, segmenting the original foggy image into pixel blocks with similar texture, color and brightness by SLIC superpixel segmentation, wherein each pixel block has similar depth of field and fog concentration;
s3-2: and processing the blocking effect by adopting a guided filtering mode to obtain refined transmittance.
Further, the model of the guided filtering is as follows:
wherein, ω iskIs a guide diagram IiA neighborhood centered on pixel k; (a)k,bk) In the neighborhood of ωkIs constant in (q)iTo output an image.
Due to the adoption of the technical scheme, the method for defogging the sea-sky background image and positioning the binocular stereoscopic vision provided by the invention adopts the improved defogging algorithm of the Hoeka bright-dark channel, so that the interference of sea fog weather on a visual system is reduced; the binocular vision system is combined with the defogging model, so that clearer images with richer detail information can be provided in the sea fog weather, and the precision of subsequently obtained parallax images is improved; by adopting a binocular stereoscopic vision positioning technology, the distance between the unmanned ship and the barrier can be prejudged, and the navigation safety is further improved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a general flow diagram of the present invention;
FIG. 2 is a flow chart of the defogging process of the present invention;
FIG. 3(a) is a graph original of a transmittance demo picture according to the present invention;
FIG. 3(b) is a graph of a rough estimate of transmissivity in accordance with the present invention;
FIG. 3(c) is a graph of transmittance optimization for the present invention;
FIG. 4 is a flow chart of disparity map acquisition according to the present invention;
FIG. 5 is a disparity map and depth map according to the present invention;
FIG. 6(a) is an original drawing of picture a;
FIG. 6(b) is a He algorithm effect diagram adopted by the picture a;
FIG. 6(c) is a diagram illustrating the effect of the method adopted by the picture a;
FIG. 6(d) original drawing of Picture b;
FIG. 6(e) is a He algorithm effect diagram adopted by the picture b;
fig. 6(f) is an effect diagram of the method adopted in the picture b.
Detailed Description
In order to make the technical solutions and advantages of the present invention clearer, the following describes the technical solutions in the embodiments of the present invention clearly and completely with reference to the drawings in the embodiments of the present invention:
the sea-sky background image is characterized in that: when the unmanned ship carries out remote observation, the remote sky and the sea area are connected into a line, so that an observed target is positioned at the junction of the sea-sky and the sea-sky.
FIG. 1 is a general flow diagram of the present invention; a method for defogging and binocular stereo vision positioning of a sea and sky background image comprises the following steps:
s1, acquiring an original foggy image by using a binocular camera;
s2, segmenting a sky region and a non-sky region in an original foggy image, and optimizing values of an atmospheric light value and a transmissivity on the basis of a dark channel defogging model provided by Hommin to obtain an improved dark channel model, wherein FIG. 2 is a defogging flow chart of the invention;
(1) segmenting a sky region and a non-sky region in the original foggy image;
according to the priori knowledge, when the unmanned surface ship shoots at a long distance, the sky area exists above the image, the brightness of the sky area is obviously higher than that of the non-sky area, and the difference between the brightness values in the sky area is small, so that the area is relatively smooth. In addition, texture information of ships on the sea or sceneries on the shore is rich, so that a connected region with high brightness and gentle gradient change can be marked as a sky region. The method for segmenting the sky area and the non-sky area in the image comprises the following steps: firstly, converting an RGB color image shot by a binocular camera into a gray image and carrying out noise reduction treatment to improve the image quality; secondly, extracting edge information in the image by adopting a Canny edge detection algorithm; then, carrying out expansion-first and corrosion-second operation on the image with the detected edge information so as to achieve the effect of enhancing the edge information of the image; and finally, carrying out negation operation on the obtained image to obtain a sky region and a non-sky region.
(2) A dark channel defogging model;
in the field of computer vision, a widely used image fogging model is shown as follows:
I(x)＝J(x)t(x)+A(1-t(x)) (1)
wherein x is the spatial coordinate of an image pixel; i (x) is an original foggy image captured by a binocular camera; j (x) is a fog-free image to be restored; a is the global atmospheric illumination intensity; t (x) is the transmission of the medium at image pixel coordinate x.
Transforming equation (1) can result in:
according to the formula (2), the defogging algorithm based on image restoration aims to estimate an atmospheric light value a and a transmittance t (x) from an original foggy image i (x), and an image j (x) after defogging is obtained according to an image fogging model, and a dark channel prior theory considers that in most of non-sky local areas, pixels in a certain area always have a low pixel value of at least one color channel, that is, the minimum value of the light intensity in the area is a small value and even tends to 0, that is:
Jdark(x)→0 (4)
in the formula (3), JcEach channel representing a color channel; Ω (x) denotes a window centered on the pixel x.
The combination of the formulas (3) and (4) can obtain:
since the atmospheric light value a ≠ 0, it can be obtained:
dividing both sides of equation 1 by a, and taking the minimum value of both sides, in combination with equation (6), yields an estimated value of transmittance:
(3) optimizing an atmospheric light value;
according to the physical meaning of the atmospheric light value, the existing dark channel defogging model usually selects the pixel value of the highest brightness point in the image as an approximate estimation value, but the error between the estimation value and the actual value is inevitable, and a single value is selected to be easily influenced by external random conditions, and in the image containing a sky area, the atmospheric light value is selected to be closest to the actual value in the sky area, so the invention adopts a quartering method to divide the sky area of the original fogging image, uses the difference between the average value of the pixels in the area and the standard difference of the pixels as a standard, selects the area with the largest difference, further divides the area with the largest difference, selects the area with the largest difference again, repeats the last operation until the finally divided area is smaller than a preset threshold, uses the area as a final area of the atmospheric light value pre-estimation, selects the pixel average value of the area as a pre-estimation value of the optimal atmospheric light value, namely:
in the formula (8), n is the number of pixels in the region, and s (x) is the sum of all pixel values in the region.
(4) Optimizing the transmittance;
a large number of existing experiments show that the contrast of the fog-free image is higher than that of the fog-free image, on the basis of the characteristics, the cost function is designed by combining the existing transmissivity estimation method, and the minimum value of the cost function is calculated to serve as the candidate value of the optimized transmissivity.
The method firstly utilizes SLIC super-pixel segmentation to segment an original foggy image into pixel blocks with similar texture, color and brightness, and the pixel blocks have similar depth of field and fog concentration, so that depth of field abrupt change does not occur in each small block, but the method assumes that the transmissivity of each small block is unchanged, so that the estimated result has block effect and has certain deviation, so the method adopts a guide filtering mode to obtain finer transmissivity which is used as the transmissivity after optimization, and the guide filtering output and a guide graph are linearly related, namely:
wherein, ω iskIs a guide diagram IiA neighborhood centered on pixel k; (a)k,bk) In the neighborhood of ωkConstant in (b), the guided filtering seeks an optimized coefficient (a) by the difference between the image to be filtered and the output imagek,bk) So as to minimize the mean square error between the output image and the input image and obtain the output image qiFIG. 3(a) is an original graph of a transmittance demonstration picture according to the present invention; FIG. 3(b) is a graph of a rough estimate of transmissivity in accordance with the present invention; FIG. 3(c) is a graph of transmittance optimization according to the present invention.
S3, carrying out defogging treatment on the original foggy image by adopting an improved dark channel defogging model to obtain a defogged image;
s4, carrying out stereo matching on the defogged left image and the defogged right image to obtain a disparity map;
FIG. 4 is a flow chart of disparity map acquisition according to the present invention; the parallax image acquisition process of binocular stereoscopic vision comprises the following steps: the method comprises the following steps of image acquisition, camera calibration, image correction, stereo matching and positioning, wherein the calibration of camera parameters is a very key link in the application of machine vision, whether the calibration result is accurate or not can greatly influence a disparity map obtained by stereo matching, so that the positioning precision of a target object is influenced, and the camera is calibrated, on one hand, the conversion relation of a three-dimensional space point under a world coordinate system and the image coordinate system, namely internal and external parameters, is solved for recovering the three-dimensional coordinate of the target object; the other aspect is to solve the distortion coefficient of the camera in the imaging process for image correction; the model for recovering the three-dimensional information of the target object from the pixel point coordinates of the target object by utilizing the calibrated internal and external parameters of the camera is as follows:
in the formula (10), dxAnd dyIs the physical size of each pixel in the camera chip on the abscissa and ordinate; u. of0And v0Is the abscissa and ordinate of the center of the image plane, f is the focal length between the left and right cameras, R is the camera rotation matrix, which is a matrix of 3 × 3, T is the camera translation matrix, which is a matrix of 3 × 1, where f is the x-axis and y-axis of the center of the image plane, where f is the focal length between the left and right cameras, R is the camera rotation matrix, which is a matrix of 3 ×x＝f/dx、fy＝f/dy。
In stereoscopic vision, due to the difference of positions of the left and right cameras, the left and right images are not coplanar, which increases the difficulty of stereoscopic matching. The correction is to process the left and right images which are not coplanar and are obtained by the binocular camera into images aligned in a coplanar line, wherein the alignment in the coplanar line is as follows: the imaging planes of the two imaging devices of the binocular system are on the same plane, and when any point of the space point is projected to the planes of the two imaging devices, the two projected points are on the same line.
The stereo matching aims at recovering object distance information from a two-dimensional image, and is the most important part of stereo vision, the stereo matching is that certain characteristics of an object are correspondingly matched in left and right images acquired by a camera to obtain the distance-parallax between pixel points of the same object in the left and right images so as to obtain a parallax image, and finally the distance information of a target object is recovered according to an imaging model of the camera and internal and external parameters of the camera.
And S5, obtaining the distance between the binocular camera and the target object according to the relation between the parallax map and the depth, and realizing target positioning.
FIG. 5 is a graph of the relationship between disparity map and depth, O, of the present inventionl、OrThe main points of the imaging planes of the left camera and the right camera are respectively provided with coordinates (c)x,cy) And (c'x,c'y) (ii) a The imaging points of the target point P (X, Y, Z) on the imaging planes of the left camera and the right camera are respectively PlAnd PrThe coordinate values of the two imaging points in the image physical coordinate system are (x)l,yl) And (x)r,yr) (ii) a The parallax d ═ x of the target pointl-xr(ii) a b is the distance between the optical centers of the cameras; f is the focal length of the two imaging planes.
According to the imaging principle of the binocular camera and the similar principle of the triangle, the relationship between the disparity map and the depth can be obtained as shown in the following formula:
the derivation can be found as follows:
in order to illustrate the effectiveness and feasibility of the method, the algorithm of the invention is compared with the He algorithm, and the obtained defogging effect comparison graph is shown in FIG. 6, wherein FIG. 6(a) is an original graph of a picture a; FIG. 6(b) is a He algorithm effect diagram adopted by the picture a; FIG. 6(c) is a diagram illustrating the effect of the method adopted by the picture a; FIG. 6(d) original drawing of Picture b; FIG. 6(e) is a He algorithm effect diagram adopted by the picture b; fig. 6(f) is an effect diagram of the method adopted in the picture b.
The experimental result shows that the He algorithm generates distortion when applied to the sky region, which results in excessively high contrast of the sky region, but the algorithm of the present invention can effectively improve the phenomenon when applied to the sky region, and the image contrast and detail information after defogging are effectively recovered.
As shown in Table 1, the quality of the defogged image is qualitatively analyzed respectively according to evaluation indexes such as information Entropy, structure similarity SSIM, mean square error MSE and the like, wherein the information Entropy represents the average information content of the image, the larger the value of the Entropy is, the larger the information content of the image is, and the richer the details are; the mean square error is a measure reflecting the effective information retention before and after image processing, and the smaller the value of the mean square error is, the stronger the information retention of the processed image is; the structural similarity measures the similarity between the restored image and the original image, and the larger the SSIM value is, the higher the similarity between the two images is.
TABLE 1
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (5)
1. A method for defogging and binocular stereo vision positioning of a sea and sky background image is characterized by comprising the following steps: the method comprises the following steps:
s1, acquiring an original foggy image by using a binocular camera;
s2, segmenting a sky region and a non-sky region in the original foggy image, and optimizing the values of atmospheric light values and transmissivity on the basis of a dark channel defogging model to obtain an improved dark channel defogging model;
s3, carrying out defogging treatment on the original foggy image by adopting an improved dark channel defogging model to obtain a defogged image;
s4, carrying out stereo matching on the defogged left image and the defogged right image to obtain a disparity map;
and S5, obtaining the distance between the binocular camera and the target object according to the relation between the parallax map and the depth, and realizing target positioning.
2. The method for defogging and binocular stereo vision positioning of the marine background image according to claim 1, further comprising: the atmospheric light value taking method comprises the following steps:
s2-1, dividing the sky area of the original foggy image by a quartering method;
s2-2, taking the difference between the average value of the pixels in the area and the standard deviation of the pixels as a measurement standard, and selecting the area with the largest difference in the divided sky area;
s2-3, further dividing the area with the largest difference, selecting the area with the largest difference again until the finally divided area is smaller than a preset threshold value, and taking the area as a final area of the pre-estimation of the atmospheric light value;
and S2-4, selecting the average value of the pixels of the area as the estimated value of the optimal atmosphere light value.
3. The method for defogging and binocular stereo vision positioning of the marine background image according to claim 1, further comprising: the formula of the estimated value of the optimal atmospheric light value is as follows:
where n is the number of pixels in the region and S (x) is the sum of all pixel values in the region.
4. The method for defogging and binocular stereo vision positioning of the marine background image according to claim 1, further comprising: the transmittance optimization steps are as follows:
s3-1, segmenting the original foggy image into pixel blocks with similar texture, color and brightness by SLIC superpixel segmentation, wherein each pixel block has similar depth of field and fog concentration;
s3-2: and processing the blocking effect by adopting a guided filtering mode to obtain refined transmittance.
5. The method for defogging and binocular stereo vision positioning of the marine background image according to claim 1, further comprising: the model of the guided filtering is as follows:
wherein, ω iskIs a guide diagram IiA neighborhood centered on pixel k; (a)k,bk) In the neighborhood of ωkIs constant in (q)iTo output an image.
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