Infrared Dim-Small Target Tracking Based on Guided Image Filtering and Kernelized Correlation Filtering

Abstract The application of correlation filtering in infrared small target tracking has been a mature research field. Traditionalcorrelation filtering is to describe the target features by using a single feature, which can not solve the problem of target occlusion. Because of the fast moving speed and lack of re-detection mechanism, the target tracking will produce offset, which leads to the performance of the tracker to decline. In view of the above problems, a new multi feature re detection framework is proposed for long-term tracking of small targets. The feature selects multi feature weighting function, considers the importance of intensity feature to infrared target and different regions, calculates the gray distribution weighting function of the target, and combines the weighting function into the correlation filter. Before updating the template, to verify the reliability of target detection, the average peak correlation energy is used as the confidence of candidate region. When the target is completely occluded, the prediction result of Kalman filter is used as the optimal estimation of target position in the next frame. A large number of experimental results on different video sequences show that the tracking accuracy of this method is greatly improved compared with the baseline method.

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Efficient local stereo matching algorithm based on fast gradient domain guided image filtering

Signal Processing Image Communication ◽

10.1016/j.image.2021.116280 ◽

2021 ◽

Vol 95 ◽

pp. 116280

Author(s):

Weimin Yuan ◽

Cai Meng ◽

Xiaoyan Tong ◽

Zhaoxi Li

Keyword(s):

Stereo Matching ◽

Image Filtering ◽

Matching Algorithm ◽

Gradient Domain ◽

Fast Gradient ◽

Guided Image Filtering

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Guided image filtering based ℓ0 gradient minimization for limited-angle CT image reconstruction

Journal of Inverse and Ill-Posed Problems ◽

10.1515/jiip-2020-0096 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Tianyi Wang ◽

Chengxiang Wang ◽

Kequan Zhao ◽

Wei Yu ◽

Min Huang

Keyword(s):

Iteration Process ◽

Real Data ◽

Image Filtering ◽

Reconstruction Technique ◽

Ct Reconstruction ◽

Imaging Device ◽

Practical Applications ◽

Guided Image Filtering ◽

Simultaneous Algebraic Reconstruction Technique ◽

Limited Angle

Abstract Limited-angle computed tomography (CT) reconstruction problem arises in some practical applications due to restrictions in the scanning environment or CT imaging device. Some artifacts will be presented in image reconstructed by conventional analytical algorithms. Although some regularization strategies have been proposed to suppress the artifacts, such as total variation (TV) minimization, there is still distortion in some edge portions of image. Guided image filtering (GIF) has the advantage of smoothing the image as well as preserving the edge. To further improve the image quality and protect the edge of image, we propose a coupling method, that combines ℓ 0 {\ell_{0}} gradient minimization and GIF. An intermediate result obtained by ℓ 0 {\ell_{0}} gradient minimization is regarded as a guidance image of GIF, then GIF is used to filter the result reconstructed by simultaneous algebraic reconstruction technique (SART) with nonnegative constraint. It should be stressed that the guidance image is dynamically updated as the iteration process, which can transfer the edge to the filtered image. Some simulation and real data experiments are used to evaluate the proposed method. Experimental results show that our method owns some advantages in suppressing the artifacts of limited angle CT and in preserving the edge of image.

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