Semi-supervised learning based edge-preserving background estimation for small target detection

2015 ◽  
Vol 72 ◽  
pp. 29-36 ◽  
Author(s):  
Kun Bai ◽  
Yuehuan Wang ◽  
Qiong Song ◽  
Dang Liu
Keyword(s):  
Supervised Learning ◽  
Target Detection ◽  
Small Target ◽  
Edge Preserving ◽  
Small Target Detection ◽  
Background Estimation

scholarly journals Infrared Small Target Detection Method Combined with Bilateral Filter and Local Entropy

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhonghua Wang ◽  
Siwei Duan ◽  
Chengli Sun
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Bilateral Filter ◽  
Background Suppression ◽  
Small Target ◽  
Local Entropy ◽  
Edge Preserving ◽  
Small Target Detection ◽  
Infrared Small Target

According to the larger false alarm rate resulted from fluctuant clutter, a novel method combined with bilateral filter and local entropy is proposed for infrared small target detection in this paper. Firstly, the original image is respectively processed by bilateral filter and local entropy, and then the two processed images are fused by point product to generate the background suppression map. Secondly, the guided filter is used to further suppress the background and enhance the small target in the map. Thirdly, the small target is detected by the adaptive threshold in the filtered map. The theoretical analyses and experimental results show that the proposed method not only effectively suppresses the clutter background, depending on the edge preserving and denoising characteristics of bilateral filtering, but also effectively highlights the small target, relying on the sensitivity of local entropy to the abrupt gray region. Compared with other methods, it is demonstrated that the proposed method owns lower false alarm rate and higher detection rate.

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