NDLI: Local contrast measure with iterative error for infrared small target detection

Content Provider	IET Digital Library
Author	Yan, Zujing Xin, Yunhong Zhang, Yixuan
Abstract	Local contrast measure (LCM) has been proved to be an effective method for infrared small target detection. However, the detection performance of LCM decreases dramatically when the background contains strong edges and pixel-sized noises with high brightness (PNHB). Based on the analysis of the inherent causes of the poor performance of LCM in extremely complex backgrounds, this study presents an effective LCM with an iterative error. The contribution is as follows: first, the two-dimensional least mean square (TDLMS) filter with an adaptive parameter is applied to suppress the background clutters roughly in each multiscale window. Then, the partial maximum pixel mean is applied to the LCM to optimise the sub-block statistical parameters, which achieves excellent strong edges suppression performance. Finally, the iteration error generated by TDLMS and the sub-block weight matrix is updated alternately to further optimise the statistical parameters of the contrast measure to make it more effective in suppressing PNHB. Experimental results demonstrate that the proposed approach is not only superior to the contrast methods in terms of high detection efficiency and low false alarm rate but also has satisfactory adaptability under extremely complex backgrounds.
Starting Page	3725
Ending Page	3732
Page Count	8
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 15, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/15
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2020.1157
Journal	IET Image Processing
Publisher Date	2020-12-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Filter Adaptive Parameter Background Clutters Computer Vision And Image Processing Technique Contrast Method Detection Performance Edge Detection Edges Suppression Performance Effective LCM Extremely Complex Backgrounds Feature Extraction Filtering Method in Signal Processing High Detection Efficiency Image Filtering Image Recognition Infrared Small Target Detection Inherent Causes Interpolation And Function Approximation Iteration Error Iterative Error Iterative Method Least Mean Squares Method Local Contrast Measure Numerical Analysis Object Detection Partial Maximum Pixel Mean PNHB Statistical Analysis Statistics Sub-block Statistical Parameter Target Tracking TDLMS Two-dimensional Least Mean Square Filter
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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