Study of UAV Image Denoising Based on Adaptive Two-Dimension Unsaturated System.

The unmanned aerial vehicle equipment is inevitably interfered by environmental noise in the process of image acquisition. Suppress noise to enhance images is a hot topic that scholars strive to study. The stochastic resonance theory can transform the noise signal energy of specific intensity into useful signal energy. Therefore, this paper proposes an image denoising algorithm based on adaptive two-dimensional unsaturated stochastic resonance system. By building a dynamic nonlinear system model, the peak signal to noise ratio and structural similarity of the output image are used as the dual evaluation model of the adaptive system, and the optimal parameters of the model are automatically obtained by adjusting the parameters of the dynamic nonlinear system. Compared with median filtering, mean filtering and two-dimensional traditional stochastic resonance methods, the image restoration effect of adaptive two-dimensional unsaturated stochastic resonance method is closer to the original image, and the histogram and peak signal to noise ratio of the output image are also significantly better than the other two methods. The research results show that in image processing, the proposed adaptive two-dimensional unsaturated stochastic resonance system has better denoising effect and better robustness to the change of noise intensity.