CN112967305A - Image cloud background detection method under complex sky scene - Google Patents

Image cloud background detection method under complex sky scene Download PDF

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CN112967305A
CN112967305A CN202110312071.5A CN202110312071A CN112967305A CN 112967305 A CN112967305 A CN 112967305A CN 202110312071 A CN202110312071 A CN 202110312071A CN 112967305 A CN112967305 A CN 112967305A
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朱伟
刘羽
董小舒
邱文嘉
辛付豪
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Nanjing Laisi Electronic Equipment Co ltd
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Abstract

The invention provides an image cloud background detection method under a complex sky scene, aiming at an empty application scene of a photoelectric search and tracking system, firstly constructing a neighborhood convolution kernel template, extracting first and second neighborhood envelope characteristics, and performing relevant neighborhood filtering on an original image; secondly, constructing multi-direction morphological gradient direction structural operators of horizontal, vertical, right diagonal and secondary diagonal, extracting contour edges of the image, and adaptively constructing a direction edge weight factor to fuse multi-direction detection results to obtain a contour edge image; and finally, carrying out neighborhood connected domain marking on the edge image to realize the extraction of the cloud background contour.

Description

Image cloud background detection method under complex sky scene
Technical Field
The invention relates to the field of image processing and computer vision, in particular to an image cloud background detection method under a complex sky scene.
Background
With the increasing complexity of modern war environment, the photoelectric searching and tracking system has more extensive requirements in various battlefield defense scenes due to the characteristics of passivity, difficulty in being influenced by the environment and the like. Aiming at the problem that a thick cloud layer shields a target in photoelectric space detection of a small target, the existing solution mainly comprises a neural network, morphological filtering, an interframe difference method and the like, which have good performance when the background changes smoothly, but the detection performance is greatly influenced when the frame frequency is low under the background of a complex cloud layer, and the algorithm has a high false alarm.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to solve the technical problem of the prior art, and provides a method for detecting an image cloud background under a complex sky scene, which comprises the following steps:
step 1, constructing a 5-by-5 neighborhood convolution kernel template S, extracting relevant first and second neighborhood envelope feature median values, and performing neighborhood convolution operation on an original Image1 to obtain a relevant neighborhood filtering Image 2;
step 2, constructing a directional gradient edge operator, and carrying out morphological directional gradient contour edge extraction on the Image 2;
step 3, extracting the detail edge of the outline to obtain a unidirectional edge detection result, and constructing a fusion edge weight factor lambdaiCalculating the edge detection in the horizontal, vertical, major diagonal and minor diagonal directions to obtain an edge Image 3;
and 4, marking the connected domain of the edge Image3, and extracting to obtain the cloud background connected domain outline.
The step 1 comprises the following steps:
step 1-1, defining a 5 x 5 neighborhood convolution kernel template as S, and setting an initial value of an S matrix as 0;
step 1-2, calculating a first neighborhood W of a window of a current image center f (x, y)1Median value m of1I.e. m1＝median(W1) Wherein mean is the median operation of the calculated vectors, x is the horizontal coordinate, y is the vertical coordinate, W1Comprises the following steps:
W1＝[f(x-1,y)，f(x,y+1)，f(x,y)，f(x+1,y)，f(x,y-1)]
step 1-3, calculating a second neighborhood W of a window of a current image center f (x, y)2Median value m of2I.e. m2＝median(W2)，W2Comprises the following steps:
Figure BDA0002990191500000021
step 1-4, setting convolution kernel template center pixel S (2,2) as Max (m)1,m2) And obtaining a neighborhood convolution kernel template S, wherein Max is the maximum operation of the calculation vector.
In step 2, the constructing a directional gradient edge operator includes:
respectively establishing gradient structural operators U in horizontal, vertical, main diagonal and sub diagonal directions1、U2、U3、U4Operator U of direction gradient structureiComprises the following steps:
Figure BDA0002990191500000022
Figure BDA0002990191500000023
wherein i is 1,2,3, 4.
In step 2, the performing morphological direction gradient contour edge extraction on the Image2 includes:
the following formula is adopted for extracting the morphological direction gradient contour edge:
Figure BDA0002990191500000024
ZIi＝f·Ui-fΘUi·L
wherein ZEiFor crude extraction of the results at the edge of the outer contour, ZIiThe results are crudely extracted for the inner contour edge, f is Image2,
Figure BDA0002990191500000025
for morphological dilation operation, Θ is a morphological erosion operation,
Figure BDA0002990191500000026
for a morphological open operation,. for a morphological close operation, the structure operator L is:
Figure BDA0002990191500000031
in step 3, the extracting the contour detail edge to obtain a one-way edge detection result includes:
and extracting the detail edge of the outline by adopting the following formula to obtain a one-way edge detection result:
ZXi＝Max(ZEi,ZIi)-Min(ZEi,ZIi)
Zi＝ZEi+ZIi+ZXi
wherein ZXiFor contour detail edges, ZiFor the one-directional edge detection result, Min represents the minimum value of the calculation vector.
In step 3, the fusion edge weight factor lambda is constructediThe method for detecting and calculating the edges in the horizontal, vertical, major diagonal and minor diagonal directions to obtain an edge Image3 includes:
constructing a fused edge weight factor lambda by the following formulai：
Di＝∑|di-d0|
Figure BDA0002990191500000032
Wherein DiRepresenting gradient gray scale difference in each direction, when i takes values of 1,2,3 and 4 respectively, diRespectively representing the pixel values of horizontal, vertical, major diagonal, and minor diagonal, d0Is the center pixel value;
extracting the multi-direction edge detection result to generate a final edge detection result Z, which is the edge Image 3:
Figure BDA0002990191500000033
step 4 comprises the following steps: traversing the edge Image3, extracting pixel points with the pixel value of 1 in the Image3 as seed pixel points, marking all foreground pixel points with the pixel values of 4 neighborhoods or 8 neighborhoods adjacent to the seed pixel points as L, and generating a connected domain outline Q by marking all pixel composition areas with the same mark as L1Repeating the above steps until the whole image is traversed to obtain the final connected domain vector (Q)1,Q2,...Qi),i∈(1,2,3...)，QiRepresenting the ith connected domain profile.
The invention mainly extracts the cloud layer and background edge characteristics by providing improved morphological gradient edge characteristics, and can effectively detect a cloud layer background area and pre-judge a shielded area of a target by fusing adaptive weighting through morphological gradients in all directions.
Has the advantages that: the invention provides an image cloud background detection method under a complex sky scene, aiming at the scene that the image has cloud interference to an empty detection target. By means of relevant neighborhood filtering, interference of image noise on target background edges is effectively reduced, outline characteristics of a cloud layer background are accurately described by utilizing a multidirectional morphological gradient operator, and generation and interference of false edges are greatly reduced.
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The foregoing and/or other advantages of the invention will become further apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings.
Fig. 1 is a flowchart of image cloud background detection in a complex sky scene according to an embodiment of the present invention.
FIG. 2 is a diagram of a first neighborhood in an embodiment of the present invention.
FIG. 3 is a diagram of a second neighborhood in an embodiment of the present invention.
FIG. 4 is a schematic diagram of the morphological directional gradient in an embodiment of the invention.
Fig. 5 is an input image in an embodiment of the present invention.
Fig. 6 is a result of image edge detection in the embodiment of the present invention.
Fig. 7 shows the contour detection result in the embodiment of the present invention.
Detailed Description
As shown in fig. 1, the present invention provides a method for detecting an image cloud background under a complex sky scene, including:
a) as shown in fig. 5, the infrared image data image1 is acquired, a 5 × 5 neighborhood convolution kernel template S is constructed, all values of the matrix are set to 0, and as shown in fig. 2 and 3, a 5 × 5 pixel matrix fw of the current image center f (x, y) is extracted1：
Figure BDA0002990191500000041
Calculating a first neighborhood W of the current window1Median value m (172,166,178,176,174)1Similarly, a second neighborhood W of the current window is calculated 1742Median value m of2170, the convolution kernel template center pixel S (2,2) is set to Max (m)1,m2) Obtaining S (2,2) 170, wherein Max is the maximum value operation of the calculation vector, and performing neighborhood convolution S operation on traversal of the original Image1 to obtain a related neighborhood filtered Image 2;
b) constructing directional gradient edge operators, and in order to further enhance the extraction of cloud background edges, as shown in fig. 4, respectively establishing horizontal, vertical, main diagonal and sub diagonal directional gradient structure operators U1、U2、U3、U4Operator U of direction gradient structurei(i ═ 1,2,3,4) is:
Figure BDA0002990191500000051
Figure BDA0002990191500000052
the morphological direction gradient contour edge extraction comprises the following steps:
Figure BDA0002990191500000053
ZIi＝f·Ui-fΘUi·L
wherein ZEiFor crude extraction of the results at the edge of the outer contour, ZIiFor the coarse extraction of the inner contour edge, f is Image2, UiI is a directional gradient edge operator and takes the values of 1,2,3 and 4,
Figure BDA0002990191500000054
for morphological dilation operation, Θ is a morphological erosion operation,
Figure BDA0002990191500000055
for a morphological open operation,. for a morphological close operation, the structure operator L is:
Figure BDA0002990191500000056
c) extracting the detail edge of the outline to obtain a one-way edge detection result Zi：
ZXi＝Max(ZEi,ZIi)-Min(ZEi,ZIi)
Zi＝ZEi+ZIi+ZXi
Wherein ZXiFor details of the profileEdge, ZiMax is the maximum value of the calculation vector and Min is the minimum value of the calculation vector for the unidirectional edge detection result. As shown in fig. 6, the multi-direction edge detection result is extracted to generate a final edge detection result Z, which is an edge Image3, and specifically:
Figure BDA0002990191500000061
wherein an edge weight factor lambda is fusediThe method comprises the following steps:
Di＝∑|di-d0|
Figure BDA0002990191500000062
wherein DiRepresenting gradient gray-scale differences in various directions, diRespective pixel values, d, representing horizontal, vertical, major diagonal, minor diagonal0The value of i is 1,2,3 and 4 for the pixel value of the central point.
d) Traversing the edge Image3, extracting pixel points with the pixel value of 1 in the Image3 as seed pixel points, marking all the foreground pixel points with the pixel values of 1 in the adjacent 4 neighborhoods or 8 neighborhoods with L, and marking the same mark as L to form a region by all pixels to generate a connected domain Q1Repeating the above steps until the whole image is traversed to obtain the final connected domain vector (Q)1,Q2,...Qi) I ∈ (1,2, 3.). The final result is shown in fig. 7.
The present invention provides a method for detecting an image cloud background under a complex sky scene, and a number of methods and approaches for implementing the technical solution are provided, the above description is only a preferred embodiment of the present invention, and it should be noted that, for a person skilled in the art, a number of improvements and embellishments may be made without departing from the principle of the present invention, and these improvements and embellishments should also be regarded as the protection scope of the present invention. All the components not specified in the present embodiment can be realized by the prior art.

Claims (7)

1. A method for detecting an image cloud background under a complex sky scene is characterized by comprising the following steps:
step 1, constructing a 5-by-5 neighborhood convolution kernel template S, extracting relevant first and second neighborhood envelope feature median values, and performing neighborhood convolution operation on an original Image1 to obtain a relevant neighborhood filtering Image 2;
step 2, constructing a directional gradient edge operator, and carrying out morphological directional gradient contour edge extraction on the Image 2;
step 3, extracting the detail edge of the outline to obtain a unidirectional edge detection result, and constructing a fusion edge weight factor lambdaiCalculating the edge detection in the horizontal, vertical, major diagonal and minor diagonal directions to obtain an edge Image 3;
and 4, marking the connected domain of the edge Image3, and extracting to obtain the cloud background connected domain outline.
2. The method of claim 1, wherein step 1 comprises:
step 1-1, defining a 5 x 5 neighborhood convolution kernel template as S, and setting an initial value of an S matrix as 0;
step 1-2, calculating a first neighborhood W of a window of a current image center f (x, y)1Median value m of1I.e. m1＝median(W1) Wherein mean is the median operation of the calculated vectors, x is the horizontal coordinate, y is the vertical coordinate, W1Comprises the following steps:
W1＝[f(x-1,y)，f(x,y+1)，f(x,y)，f(x+1,y)，f(x,y-1)]
step 1-3, calculating a second neighborhood W of a window of a current image center f (x, y)2Median value m of2I.e. m2＝median(W2)，W2Comprises the following steps:
Figure FDA0002990191490000011
step 1-4, setting convolution kernel template center pixel S (2,2) as Max (m)1,m2) To obtain a neighborhoodAnd (4) convolution kernel template S, wherein Max is the maximum operation of the calculation vector.
3. The method of claim 2, wherein in step 2, said constructing directional gradient edge operators comprises:
respectively establishing gradient structural operators U in horizontal, vertical, main diagonal and sub diagonal directions1、U2、U3、U4Operator U of direction gradient structureiComprises the following steps:
Figure FDA0002990191490000021
Figure FDA0002990191490000022
wherein i is 1,2,3, 4.
4. The method according to claim 3, wherein in step 2, the performing morphological direction gradient contour edge extraction on the Image2 comprises:
the following formula is adopted for extracting the morphological direction gradient contour edge:
Figure FDA0002990191490000023
ZIi＝f·Ui-fΘUi·L
wherein ZEiFor crude extraction of the results at the edge of the outer contour, ZIiThe results are crudely extracted for the inner contour edge, f is Image2,
Figure FDA0002990191490000024
for morphological dilation operation, Θ is a morphological erosion operation,
Figure FDA0002990191490000025
for a morphological open operation,. for a morphological close operation, the structure operator L is:
Figure FDA0002990191490000026
5. the method according to claim 4, wherein in step 3, the extracting the contour detail edge to obtain a one-directional edge detection result comprises:
and extracting the detail edge of the outline by adopting the following formula to obtain a one-way edge detection result:
ZXi＝Max(ZEi,ZIi)-Min(ZEi,ZIi)
Zi＝ZEi+ZIi+ZXi
wherein ZXiFor contour detail edges, ZiFor the one-directional edge detection result, Min represents the minimum value of the calculation vector.
6. The method according to claim 5, wherein in step 3, the fusion edge weight factor λ is constructediThe method for detecting and calculating the edges in the horizontal, vertical, major diagonal and minor diagonal directions to obtain an edge Image3 includes:
constructing a fused edge weight factor lambda by the following formulai：
Di＝∑|di-d0|
Figure FDA0002990191490000031
Wherein DiRepresenting gradient gray scale difference in each direction, when i takes values of 1,2,3 and 4 respectively, diRespectively representing the pixel values of horizontal, vertical, major diagonal, and minor diagonal, d0Is the center pixel value;
extracting the multi-direction edge detection result to generate a final edge detection result Z, which is the edge Image 3:
Figure FDA0002990191490000032
7. the method of claim 6, wherein step 4 comprises: traversing the edge Image3, extracting pixel points with the pixel value of 1 in the Image3 as seed pixel points, marking all foreground pixel points with the pixel values of 4 neighborhoods or 8 neighborhoods adjacent to the seed pixel points as L, and generating a connected domain outline Q by marking all pixel composition areas with the same mark as L1Repeating the above steps until the whole image is traversed to obtain the final connected domain vector (Q)1,Q2,...Qi),i∈(1,2,3...)，QiRepresenting the ith connected domain profile.
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