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

Stochastic resonance (SR) is usually utilized to enhance the signal with the help of noise. Inspired by this, we find 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 stochastic resonance (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 employ 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 regularization in the variational framework to remove the false alarm points by spatial smoothing, while preserving the role of noise in the 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.