Spatial-Temporal Stochastic Resonance Model for Dim-Small Target Detection

Stochastic resonance (SR) is usually used to enhance the signal with the help of noise. Inspired by this, we find that the SR can also handle the problem of dim-small target detection under the low local signal-to-noise ratio (LSNR) situation. In this letter, we propose a novel spatial-temporal SR (...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2022, Vol.19, p.1-5
Main Authors: Dan, Bingbing, Li, Meihui, Tang, Tao, Qi, Xiaoping, Zhu, Zijian, Ouyang, Yimin
Format: Article
Language:English
Subjects:
Detection
Dim-small target detection
False alarms
Image enhancement
Image sequences
Infrared imagery
low local signal-to-noise ratio (LSNR)
Modelling
Object detection
Optimization
Photonics
Photons
Poisson distribution
Regularization
Resonance
Signal to noise ratio
Spatial smoothing
spatial–temporal
Stochastic resonance
stochastic resonance (SR)
Stochasticity
Target detection
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Summary:Stochastic resonance (SR) is usually used to enhance the signal with the help of noise. Inspired by this, we find that the SR can also handle the problem of dim-small target detection under the low local signal-to-noise ratio (LSNR) situation. In this letter, we propose a novel spatial-temporal SR (STSR) model for dim-small target detection. First, we select the SR as the core model to enhance the salience of the target by the inherent strong noise. With the help of the Poisson distribution prior, we use the multiple adjacent frames as the input of the SR model, improving the LSNR of the resonance state through the temporal accumulation of photons. Then, we introduce the total variation (TV) regularization in the variational framework to remove the false alarm points by spatial smoothing, while preserving the role of noise in SR. Finally, we customize an optimization process based on the alternating direction method of multiplier (ADMM) to solve the STSR variational minimization problem. Both the qualitative and quantitative experiments on real visible and infrared image sequences have demonstrated the superiority of the proposed model, especially in the low LSNR situation below 2 dB.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2022.3202533