Infrared and Laser Engineering, Volume. 52, Issue 10, 20230044(2023)
A review of image processing methods in target atmospheric disturbance detection
Figures & Tables(11)
Fig. 3. Diagram of the development of optical flow estimation methods
Fig. 4. Basic flow chart of cross-correlation algorithm based on pattern tracking
Fig. 5. Basic flow chart of motion estimation method for full convolutional networks based on embedded cross-correlation
Table 1. Limitations of various target atmospheric disturbance image processing methods
View tableView in ArticleTable 1. Limitations of various target atmospheric disturbance image processing methods
Image processing method of target atmospheric disturbance Limitation Cross-correlation method The resolution of processing results is reduced, the calculation accuracy is poor, the detection effect is easily affected by the characteristics of flow field and environmental noise, and the application is limited in complex environment Optical flow method The calculation complexity is high, the timeliness is poor, and it is easy to fall into the local optimal value. When the illumination conditions change, there are occlusions, and the target moves locally, the detection effect is poor, and the application is limited Interframe difference method The ability of complete extraction of target information is poor, and the phenomenon of missing detection and "void" is easy to occur. The calculation accuracy is poor, and the detection effect is poor under the moving background or when the background changes dramatically Background subtraction Mixed Gaussian model Prior information is required to build the background model, which has low operational efficiency and limited application when the background changes dramatically ViBe method The background model construction method is relatively simple with large error, resulting in poor calculation accuracy. Also, due to the background model, its application is limited in the scene with drastic changes in the background environment
Table 2. Development of image processing methods for atmospheric disturbance detection of various targets
View tableView in ArticleTable 2. Development of image processing methods for atmospheric disturbance detection of various targets
Method Optimization algorithm itself Fusion with other image processing algorithms Neural network-based Cross-correlation method ●Autonomous velocity measurement and positioning technology based on improved optical flow algorithm[ 18 ]●Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm[ 21 ]●Phase-based disparity measurement[ 20 ]An investigation of smoothness constraints for the estimation of displacement vector fields from image sequences[ 19 ]Optical flow method ●Moving target detection algorithm based on Susan edge detection and frame difference[ 39 ]●Moving object detection based on frame difference and W4[ 41 ]●Object tracking in satellite videos by fusing the kernel correlation filter and the three-frame-difference algorithm[ 40 ]●Moving target detection using inter-frame difference methods combined with texture features and lab color space[ 42 ]Interframe difference method ●A ship target image recognition method based on inter-frame difference algorithm[ 35 ]●Underwater object detection based on bi-dimensional empirical mode decomposition and Gaussian mixture model approach[ 55 ]●Optimal transport for Gaussian mixture models[ 53 ]●Moving target detection based on improved Gaussian mixture model considering camera motion[ 54 ]Background subtraction Mixed Gaussian model ●An improved vibe algorithm of dual background model for quickly suppressing ghost images[ 73 ]●Detection and tracking of a moving object using Canny edge and optical flow techniques[ 74 ]●Deep learning-driven gaussian modeling and improved motion detection algorithm of the three-frame difference method ●Application of pixel drift denoising algorithm in optimizing Gaussian mixture model ViBe method ●A robust single-pixel particle image velocimetry based on fully convolutional networks with cross-correlation embedded [ 76 ]●Deep recurrent optical flow learning for particle image velocimetry data[ 78 ]●Oiflow: Occlusion-inpainting optical flow estimation by unsupervised learning[ 77 ]●An improved ViBe method for motion target detection[ 75 ]
Table 3. Analysis of advantages and disadvantages of three development directions
View tableView in ArticleTable 3. Analysis of advantages and disadvantages of three development directions
Item Optimization algorithm itself Fusion with other image processing algorithms Neural network-based Advantage The algorithm has the advantages of low complexity, fast operation speed and good real-time performance The method can make up for their own technological limitations, can be used to achieve better performance, and better robustness The algorithm performance can be greatly improved, and the algorithm has strong adaptability in multiple scenarios. Disadvantage It is difficult to eliminate the technical limitations of the algorithm, and the robustness of the algorithm is generally low Increase algorithm complexity, reduce operational efficiency, real-time performance is poor Large amount of prior information is required for model construction and the operation speed is low
Table 4. Comparison of various high-precision image processing methods for atmospheric disturbance target detection
View tableView in ArticleTable 4. Comparison of various high-precision image processing methods for atmospheric disturbance target detection
Performance Cross-correlation method Optical flow method Interframe difference method Background subtraction Detection effect The sparse flow field information of target atmospheric disturbance can be obtained, but the detailed description ability is insufficient More detailed information about atmospheric disturbance can be obtained, such as the motion vector in thex and y directions Only the shape and contour information of atmospheric disturbance can be obtained, and the ability to obtain details is poor To obtain the shape and contour information of atmospheric disturbance, more detail information is lost Disturbance inhibition ability Undisturbed background suppression capability Undisturbed background suppression capability Undisturbed background suppression capability Have ability to disturbance background suppression Algorithm complexity With Fourier change, the algorithm has some complexity It needs to use the least square method or other algorithms to obtain the approximate solution, and the algorithm complexity is high The gray difference between two frames is calculated, and the algorithm is simple Need to build background model algorithm complexity Algorithm applicability Best Best Good Poor Real-time performance Best Poor Best Good Advantage Simple calculation and high real-time performance High detection accuracy, complete target, can adapt to the background movement to a certain extent Simple calculation and high real-time performance High detection accuracy, complete target and small amount of calculation Disadvantage Reduced resolution and poor robustness to disturbance The calculation is complex, the calculation error of target contour is large, and the robustness to disturbance is poor Easy to produce "void", poor robustness to disturbance It is not universal to the environment and its robustness to disturbance is poor
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Weihe Ren, Kang Li, Yue Zhang, Guoxian Zheng, Yun Su, Xuemin Zhang, Yi Liu. A review of image processing methods in target atmospheric disturbance detection[J]. Infrared and Laser Engineering, 2023, 52(10): 20230044
Paper Information
Category: Image processing
Received: Feb. 1, 2023
Accepted: --
Published Online: Nov. 21, 2023
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