Infrared Small-Target Detection Based on Background-Suppression Proximal Gradient and GPU Acceleration

Patch-based methods improve the performance of infrared small target detection, transforming the detection problem into a Low-Rank Sparse Decomposition (LRSD) problem. However, two challenges hinder the success of these methods: (1) The interference from strong edges of the background, and (2) the t...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Vol. 15; no. 22; p. 5424
Main Authors: Hao, Xuying, Liu, Xianyuan, Liu, Yujia, Cui, Yi, Lei, Tao
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.11.2023
Subjects:
Accuracy
Algorithms
approximate partial SVD
Approximation
bioinformatics
Decomposition
Deep learning
Design
False alarms
GPU acceleration
Graphics processing units
Infrared imaging
infrared small target detection
methodology
Methods
proximal gradient
remote sensing
Signal processing
solutions
Sparsity
Target detection
Tracking systems
China
ISSN:2072-4292, 2072-4292
Online Access:Get full text
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Summary:Patch-based methods improve the performance of infrared small target detection, transforming the detection problem into a Low-Rank Sparse Decomposition (LRSD) problem. However, two challenges hinder the success of these methods: (1) The interference from strong edges of the background, and (2) the time-consuming nature of solving the model. To tackle these two challenges, we propose a novel infrared small-target detection method using a Background-Suppression Proximal Gradient (BSPG) and GPU parallelism. We first propose a new continuation strategy to suppress the strong edges. This strategy enables the model to simultaneously consider heterogeneous components while dealing with low-rank backgrounds. Then, the Approximate Partial Singular Value Decomposition (APSVD) is presented to accelerate solution of the LRSD problem and further improve the solution accuracy. Finally, we implement our method on GPU using multi-threaded parallelism, in order to further enhance the computational efficiency of the model. The experimental results demonstrate that our method out-performs existing advanced methods, in terms of detection accuracy and execution time.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs15225424