CN109801241A - A kind of solar flare image based on modified dark priority algorithm removes cloud method - Google Patents
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Abstract
A kind of solar flare image based on modified dark priority algorithm removes cloud method, comprising: describes the concept in channel using mathematic(al) representation；Assuming that cloud atlas piece expression formula: I (x)=J (x) t (x)+A (1-t (x)) seeks global atmosphere light value A.Size from dark channel diagram according to brightness takes preceding 0.1% pixel.In these positions, the value of the corresponding point with maximum brightness is found in original foggy image, as A value.Bilateral filtering is used when solving t (x) to improve.Calculate the local mean value and Local standard deviation of dark image D (x, y): according to known airlight vector A, calculation optimization projects graph expression formula.In view of when the value very little of transmission plot t, the value that will lead to J is bigger than normal, so that it is whole excessive to white field to make image, therefore a state value t generally can be set0, when t value is less than t0When, enable t=t0.Therefore the image expression formula finally restored.A kind of solar flare image based on modified dark priority algorithm of the present invention removes cloud method, by modified dark concept to cloud and mist image restoring, goes cloud effect with good.
Description
Technical Field
The invention belongs to the technical field of image processing, and particularly relates to a solar flare image cloud removing method based on an improved dark channel priority algorithm.
Background
The sun is the fixed star most closely related to human beings, and has an inseparable relationship with the life and production activities of human beings. The sun is filled with magnetic field and stores huge magnetic energy. When the magnetic energy stored in the magnetic field is excessive, energy is released through solar bursts, one of the most intense solar burst forms of solar flare. The flare outbreak will affect the earth's magnetic field and the ionosphere above it, and further affect human activities such as satellite navigation, radio communication, etc. Therefore, the recognition and observation of the solar flare phenomenon are also the real needs for people to realize space exploration and make preventive measures.
The solar flare phenomenon is influenced by the earth atmospheric cloud layer when being observed, so the method is particularly important for the cloud removing processing of flare images. The dark channel prior theory proposed by wo 2009-mingkai can effectively perform cloud removal on an image, but because minimum filtering is used in a dark channel, the obtained transmittance contains a halo effect and a block effect, and in order to solve the problem, a soft-matching and guided filtering optimization algorithm (where the soft-matching algorithm is selected for comparison) is adopted to optimize the transmittance, wherein the soft-matching can well eliminate the halo phenomenon and the block phenomenon, but the time complexity is greatly increased; the time complexity of the guided filtering algorithm is small, but the restored image still has certain degree of cloud in the edge area, so that the soft-matching algorithm and the guided filtering can be optimized by utilizing the bilateral filtering algorithm to optimize the calculation of the transmissivity.
Disclosure of Invention
In order to solve the technical problem of cloud removal of solar flare images, the invention provides a cloud removal method of solar flare images based on an improved dark channel priority algorithm, wherein cloud images are restored through an improved dark channel concept, and the cloud removal method has a good cloud removal effect.
The technical scheme adopted by the invention is as follows:
a solar flare image cloud removing method based on an improved dark channel priority algorithm comprises the following steps:
step 1: describing the concept of the channel by mathematical expressions, for image J, dark channel JdarkCan be expressed as the formula:
in the above formula, JcRepresenting the color channel image of image J, wherein omega (x) represents a field area, the pixel center is x, y is any point in the field area of omega (x), and c represents three channels of r, g and b, then Jc(y) represents the image of the c channel at y in the range of the Ω (x) domain. The meaning of the method is to find the minimum value of the three components of RGB, and then carry out minimum value filtering on the single-channel image. The dark channel prior indicates: j. the design is a squaredark→ 0, corresponding to JdarkApproximately equals 0, so that the real J can be obtained according to the conditionc。
Step 2: assuming that the cloud picture expresses formula (2):
I(x)＝J(x)t(x)+A(1-t(x)) (2)
wherein, i (x) represents the graph to be processed, j (x) represents the real graph, t (x) represents the transmittance, which represents the part of light which can reach the computer system and is not scattered, and a represents the global atmospheric light value.
And step 3: and (5) solving a global atmospheric light value A. The first 0.1% of the pixels are taken from the dark channel map in terms of the magnitude of the luminance. In these positions, the value of the corresponding point with the highest brightness is found in the original foggy image as the a value.
Bilateral filtering improvements are employed in solving for t (x).
And 4, step 4: calculating the local mean and the local standard deviation of the dark image D (x, y), and estimating the atmosphere light curtain according to the difference between the two:
wherein,representing an atmospheric light curtain, D (x, y) representing a dark image, B1(x, y) denotes the local mean of the dark image D (x, y), B2(x, y) denotes the local standard deviation, F, of the dark image D (x, y)B(x, y) represents an algorithmic function that employs bilateral filtering.
Due to the fact thatIs the difference between the local mean and the local standard deviation of D (x, y),then:
and 5: calculating an optimized projection diagram expression (6) according to the known global atmospheric light value A
In the above formula, t (x, y) represents a transmittance matrix,an atmospheric light curtain is shown.
Step 6: considering that when the value of the transmission map t is small, it will result in a large value of J, thus making the image overall transition to white, a GUO value t can be generally set0When the value of t is less than t0When t is equal to t0. The final restored image expression is therefore:
wherein J (x, y) represents the processed image, I (x) represents the original image, t (x, y) represents the transmittance matrix, A represents the global atmospheric light value, t (x, y) represents the transmittance matrix, and0the threshold value of the selected transmission map t is expressed.
The invention relates to a solar flare image cloud removing method based on an improved dark channel priority algorithm, which has the following technical effects:
1: the method is used for carrying out cloud removing processing on the solar flare image and carrying out simulation. And evaluating the cloud removing effect according to the simulation result. The result shows that the improved dark channel priority algorithm has a remarkable effect on removing cloud due to solar flare.
2: the improved algorithm provided by the invention greatly shortens the calculation time and reduces the computer complexity. Compared with the effect after image processing, the improved algorithm obtains a clearer image and improves the block phenomenon in the estimated transmittance graph; the algorithm has the effect of smoothing the image edge while thinning the transmissivity.
3: compared with the original algorithm, the improved algorithm provided by the invention has better in-situ performance in average gray scale of the image, display of detail information, image information amount and relative definition degree, and can observe the position of flare and the image more easily.
Drawings
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
FIG. 1 is a flowchart of a solar flare image cloud removing method based on an improved dark channel priority algorithm according to an embodiment of the present invention
Fig. 2 is a solar flare image before clouding.
Fig. 3(1) is a solar flare image processed using the original dark channel priority algorithm.
Fig. 3(2) is a solar flare image processed using a modified dark channel priority algorithm.
Detailed Description
The dark channel prior algorithm is firstly applied to defogging processing of the image, and because the cloud imaging model is similar to the fog imaging model and is the sum of energy of attenuation of target radiation information and energy of attenuation of atmospheric light, the image can be deblurred by using the dark channel prior knowledge.
Based on an improved dark channel preoperative algorithm. Because the original algorithm applies the soft-matching algorithm when solving the transmissivity t (x, y), the calculation complexity and the calculation time are greatly increased, and in order to reduce the calculation burden, the invention applies the bilateral filtering algorithm to replace the soft-matching process, thereby achieving the aim and ensuring the quality of image processing.
A solar flare image cloud removing method based on an improved dark channel priority algorithm comprises the following steps:
step 1: describing the concept of the channel by mathematical expressions, for image J, dark channel JdarkCan be expressed as the formula:
in the above formula, JcRepresenting the color channel image of image J, wherein omega (x) represents a field area, the pixel center is x, y is any point in the field area of omega (x), and c represents three channels of r, g and b, then Jc(y) represents the image of the c channel at y in the range of the Ω (x) domain. The meaning of the method is to find the minimum value of the three components of RGB, and then carry out minimum value filtering on the single-channel image. The dark channel prior indicates: j. the design is a squaredark→ 0, corresponding to JdarkApproximately equals 0, so that the real J can be obtained according to the conditionc。
Step 2: assuming that the cloud picture expresses formula (2):
I(x)＝J(x)t(x)+A(1-t(x)) (2)
wherein, i (x) represents the graph to be processed, j (x) represents the real graph, t (x) represents the transmittance, which represents the part of light which can reach the computer system and is not scattered, and a represents the global atmospheric light value.
And step 3: and (5) solving a global atmospheric light value A. The first 0.1% of the pixels are taken from the dark channel map in terms of the magnitude of the luminance. In these positions, the value of the corresponding point with the highest brightness is found in the original foggy image as the a value.
Bilateral filtering improvements are employed in solving for t (x).
And 4, step 4: calculating the local mean and the local standard deviation of the dark image D (x, y), and estimating the atmosphere light curtain according to the difference between the two:
wherein,representing an atmospheric light curtain, D (x, y) representing a dark image, B1(x, y) denotes the local mean of the dark image D (x, y), B2(x, y) denotes the local standard deviation, F, of the dark image D (x, y)B(x, y) represents an algorithmic function that employs bilateral filtering.
Due to the fact thatIs the difference between the local mean and the local standard deviation of D (x, y),then:
and 5: calculating an optimized projection diagram expression (6) according to the known global atmospheric light value A
In the above formula, t (x, y) represents a transmittance matrix,an atmospheric light curtain is shown.
Step 6: considering that when the value of the transmission map t is small, it will result in a large value of J, thus making the image overall transition to white, a GUO value t can be generally set0When the value of t is less than t0When t is equal to t0. The final restored image expression is therefore:
wherein J (x, y) represents the result after treatmentIs represented by I (x) represents the original image, t (x, y) represents the transmittance matrix, A represents the global atmospheric light value, t0The threshold value of the selected transmission map t is expressed.
FIG. 1 is a flow chart of a solar flare image cloud removal method based on an improved dark channel priority algorithm; the method is characterized in that an original solar flare image is processed, and a soft-matching algorithm and a guide filter are replaced by a bilateral filter algorithm when the transmissivity is solved, so that the calculation of the transmissivity is optimized.
At present, randomly selecting a solar flare image, as shown in figure 2, carrying out cloud removing treatment on the solar flare image, applying MATLAB programming, and carrying out an algorithm implementation process as shown in the flow of figure 1, firstly inputting an original image, and solving a dark channel J of the original imagedark(x) (ii) a The global atmospheric light values are then obtained according to the algorithm described herein, and the atmospheric light curtain is then estimated by calculating the local mean and local standard deviation of the dark image D (x, y). Then, according to the known air light vector, calculating a projection map to obtain the optimized transmittance t (x). Finally, a distribution function of the cloud-removed image is obtained according to the formula (7), and the cloud-removed image is restored to obtain a final cloud-removed image, as shown in fig. 3 (2). Meanwhile, in order to compare with the algorithm before improvement, the selected solar flare image is processed by using the original dark channel priority algorithm to obtain a cloud-removed image, as shown in fig. 3 (1).
In combination with the gray value characteristic, the gray value at the flare is higher, so the display is brighter, and the cloud gray value is higher and is in irregular bright color. Therefore, it can be seen from the comparison between fig. 2 and fig. 3(1) and fig. 3 (2): no matter before and after the improvement, the cloud removing effect of the dark channel priority algorithm is obvious, but as can be seen from the comparison of fig. 3(1) and 3(2), the image definition is higher and the solar flare is more obvious after the improved algorithm is adopted, so that the superiority of the improved algorithm is reflected.
Claims (1)
1. A solar flare image cloud removing method based on an improved dark channel priority algorithm is characterized by comprising the following steps:
step 1: describing the concept of the channel by mathematical expressions, for image J, dark channel JdarkCan be expressed as the formula:
in the above formula, JcA color channel image representing the image J,omega (x) represents a domain range, the pixel center is x, y is any point in the omega (x) domain range, c represents three channels of r, g and b, and J representsc(y) represents the image of the c channel at y in the range of Ω (x) domain; the significance of the method is to calculate the minimum value of three components of RGB, and then carry out minimum value filtering on the single-channel image; the dark channel prior indicates: j. the design is a squaredark→ 0, corresponding to JdarkApproximately equals 0, so that the real J can be obtained according to the conditionc；
Step 2: assuming that the cloud picture expresses formula (2):
I(x)＝J(x)t(x)+A(1-t(x)) (2)
wherein, i (x) represents a graph to be processed, j (x) represents a real graph, t (x) represents a transmittance, represents a part of light which can reach a computer system and is not scattered, and a represents a global atmospheric light value;
and step 3: obtaining a global atmospheric light value A; taking the first 0.1% of pixels according to the brightness from the dark channel map; in these positions, the value of the corresponding point with the highest brightness is found in the original foggy image as the a value;
bilateral filtering improvement is adopted when solving t (x);
and 4, step 4: calculating the local mean and the local standard deviation of the dark image D (x, y), and estimating the atmosphere light curtain according to the difference between the two:
wherein,representing an atmospheric light curtain, D (x, y) representing a dark image, B1(x, y) denotes the local mean of the dark image D (x, y), B2(x, y) denotes the local standard deviation, F, of the dark image D (x, y)B(x, y) represents an algorithm function that applies bilateral filtering;
due to the fact thatIs the difference between the local mean and the local standard deviation of D (x, y),then:
and 5: calculating an optimized projection diagram expression (6) according to the known global atmospheric light value A
In the above formula, t (x, y) represents a transmittance matrix,representing an atmospheric light curtain;
step 6: considering that when the value of the transmission map t is small, it will result in a large value of J, thus making the image overall transition to white, a GUO value t can be generally set0When the value of t is less than t0When t is equal to t0(ii) a The final restored image expression is therefore:
wherein J (x, y) represents the processed image, I (x) represents the original image, t (x, y) represents the transmittance matrix, A represents the global atmospheric light value, t (x, y) represents the transmittance matrix, and0the threshold value of the selected transmission map t is expressed.
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