A novel ship image enhancement method based on fusion of parallel-series stochastic resonance model and multi-scale spiking neural networks

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Abstract

Ship imaging has an impact on the visual judgment of the steersman and influences ship navigation safety. Traditional image enhancement methods struggle under adverse conditions and fail to account for the physiological mechanisms of human visual perception. Therefore, a novel method was proposed to enhance ship imaging by combining a visual neural signal encoding mechanism (accomplished by multi-scale spiking neural networks) with a multi-scale frequency-domain stochastic resonance model. First, the noisy image is encoded by mapping the neuron pulse count per unit time, and then the encoded signal is directionally filtered by constructing a spatial dot-matrix dual-view perception receptive field model to achieve preliminary low-level noise reduction. Second, a stochastic resonance system is constructed and applied in series to enhance the decomposed multi-scale wavelet coefficients. Finally, considering the brightness characteristics of the visual system, the processed wavelet coefficients of each scale are inversely transformed and reconstructed. The reconstructed image is then fused with the image obtained by directional filtering of the spatial dot-matrix dual-view pathway receptive field model to produce the final enhanced image. According to the experimental results, compared with the original noisy image, the peak signal-to-noise ratio (PSNR) and visual information fidelity (VIF) improve by 27% and 30%, respectively. These findings indicate that the proposed method can effectively enhance ship images in a manner more consistent with the physiological characteristics of human visual imaging and signal processing.

Keywords

pulse coding / stochastic resonance / neural networks / image enhancement

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