Method for overcoming image influenced by atmospheric turbulence in time domain
The influence of atmospheric turbulence on the image in the time domain increases the difficulty of extracting targets from the image. Most of the current object extraction algorithms are not applicable [3], and the use of the most adaptive category variance automatic threshold method (Otsu) in the field of image segmentation also cannot achieve good results. This is because the brightness and darkness of each part of the image affected by turbulence cannot be effectively extracted by the unified threshold of the whole picture. A threshold that can well separate the target and the background of the relatively bright area in the picture is used in The relatively dark area in the picture may judge some or all of the targets in the area as the background. This serious misjudgment makes the target extracted from the image have a large deviation from the real target, which seriously affects the accuracy of the final monitoring result.
In order to overcome this effect of atmospheric turbulence, this paper draws on the reference of local thresholding proposed by the literature [4] for text images with uneven illumination and the adaptive threshold determination method proposed by the literature [5]. A new algorithm.
Considering that the images generally used for remote monitoring purposes have a standard prior value determined by experiment, the image can be divided into several areas according to the prior value when target extraction, each area contains some targets and background , And then perform threshold division for each area separately. Since the interior of a small area can be viewed as uniform brightness, and the threshold of each area is determined by the characteristics of the area, it can ensure that the segmentation results within each area are basically accurate. Finally, the targets segmented from each area are put together to obtain the segmentation result of the whole image. The method for determining the threshold within each area is discussed below.
Suppose the image has a total of L gray levels, the pixels with gray level i have ni, the image has N pixels, the normalized histogram, the ratio of the number of pixels with gray level i to the total number of pixels is
From the point of view of mathematical statistics, if the threshold t is selected properly, the two types of C0 and C1 obtained by the segmentation should have the smallest intra-class variance and the largest inter-class variance. Take t from 0 to L−1 and calculate the class respectively The inter-variance Db. The t corresponding to the maximum value among these Db is the optimal threshold required for image segmentation. BDBD
In order to verify the effect of the above algorithm, this paper takes the images obtained by shooting four lasers at a long distance as samples. As shown in Figure 1, the image segmentation process is performed by the Otsu method and the algorithm designed in this paper. Figures 2 and 3 give The effect picture after processing is shown. It can be seen from Figure 2 that the Otsu method is very unsatisfactory for segmentation of the image affected by atmospheric turbulence, and it is difficult to meet the needs of subsequent image processing. The results in Figure 3 show that the algorithm designed in this paper can more effectively overcome the impact of atmospheric turbulence on the image, and can accurately extract the target from the low-quality image after being affected by atmospheric turbulence, laying a good foundation for subsequent image processing. basis.
Fig.1 Original image of the targetFig.2 Binaryization of Fig.1 by Otsu methodFig.3 Binaryization of Fig.1 by the method of this paper The impact is feasible and has considerable application value.
How to overcome the influence of atmospheric turbulence on the image in the airspace The influence of the airspace is mainly manifested as the random drift of the beam caused by the turbulence, and the beam drift will cause the random jitter of the target image point on the image. This effect is extremely harmful to the long-distance target deformation and micro-displacement monitoring system. Because the random drift of the image points will be mistaken for the movement of the real target, causing false alarms. This kind of error can be overcome by numerical averaging.
Correction of image distortion caused by atmospheric turbulence
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