Abstract
The robustness of infrared small-faint target detection methods to noisy situations has been a challenging and meaningful research spot. The targets are usually spatially small due to the far observation distance. Considering the underlying assumption of noise distribution in the existing methods is impractical; a state-of-the-art method has been developed to dig out valuable information in the temporal domain and separate small-faint targets from background noise. However, there are still two drawbacks: (1) The mixture of Gaussians (MoG) model assumes that noise of different frames satisfies independent and identical distribution (i.i.d.); (2) the assumption of Markov random field (MRF) would fail in more complex noise scenarios. In real scenarios, the noise is actually more complicated than the MoG model. To address this problem, a method using the non-i.i.d. mixture of Gaussians (NMoG) with modified flux density (MFD) is proposed in this paper. We firstly construct a novel data structure containing spatial and temporal information with an infrared image sequence. Then, we use an NMoG model to describe the noise, which can be separated with the background via the variational Bayes algorithm. Finally, we can select the component containing true targets through the obvious difference of target and noise in an MFD maple. Extensive experiments demonstrate that the proposed method performs better in complicated noisy scenarios than the competitive approaches.
Highlights
Distant and faint target detection is of great importance to infrared systems, as anti-missile techniques and early-warning systems
The receiver operating characteristic (ROC) curves of IPI and reweighted infrared patch-tensor (RIPT) on Sequences 2 and 5 demonstrate that they are sensitive to salt and pepper noise, and the performance of mixture of Gaussians (MoG) with Markov random field (MRF) method is not satisfying due to the identical noise distribution assumption fails in complex noise case
The results demonstrate the superiority of the proposed approach on target detection, background clutter and noise suppression ability over other competitive methods, because the non-i.i.d. mixture of Gaussians (NMoG) model and modified flux density (MFD) maple improve the robustness of the proposed approach to different kinds of noise
Summary
Distant and faint target detection is of great importance to infrared systems, as anti-missile techniques and early-warning systems. The low-rank and sparse component recovery based approach, as a subdiscipline of the low-rank representation (LRR) [21], has become very popular in recent years In this approach, the background regions are assumed to change gradually, and a special low-rank data structure can be constructed with the original images, such as a 2D matrix and a 3D tensor. The infrared images usually include complex instrumental noise that degrades the performance for target detection. The MRF model does not provide a robust noise estimate in complex scenarios, since its performance is based on the assumption that the noise component does not arise in the neighborhood region of the targets. We propose a small and faint target detection approach based on a non-i.i.d. MoG (NMoG) model [36] and modified flux density (MFD) maple [37].
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