Dim small target detection based on stochastic resonance

Dim small target detection, which is characterized by complex background and low Signal-to-Noise Ratio (SNR), is critical for many applications. Traditional detection algorithms assume that noise is not useful for detecting targets and try to remove the noise to improve SNR of images using various filtering techniques. In this paper, we introduce a detection algorithm based on Stochastic Resonance (SR) where stochastic resonance is used to enhance the dim small targets. Our intuition is that SR can achieve the target enhancement in the presence of noise. Adaptive Least Mean Square (ALMS) filtering is first adopted to estimate the background, and the clutter is suppressed by subtracting the estimated background image from the source image. Adaptive SR (ASR) method is then employed to enhance the target and improve the SNR of the image containing the target and noise. ASR tunes and adds the optimal noise intensity to increase the power of the targets and therefore improve the SNR of the image. Several experiments on synthetic and natural images are conducted to evaluate our proposed algorithm. The results demonstrate the effectiveness of our algorithm.