二维卡尔曼滤波的多源信息集中式融合去噪方法

Fusion Denoising Method of Multi-source Information of Two-dimensional Kalman Filtering

目前对于单独的某种算法或单传感器的图像信息已不能满足高精度的图像识别任务的需要,通过多传感器可获得比单一传感器质量更高的信息。基于此,文章首先在图像上建立多传感器二维线性离散系统的状态空间模型,然后利用二维卡尔曼滤波算法对图像进行滤波去噪,最后利用集中式融合去噪。仿真结果表明:文章算法在提高峰值信噪比和提高图像清晰度两个方面比其他算法更加有效。

At present, a single algorithm or single sensor＇s image information can not meet the needs of high-precision image recognition task, the multi-sensor can obtain higher quality information than a single sensor. Based on this, the state space model of the multisensor linear discrete system is established on the image, then the image is filtered and denoised using the two-dimensional Kalman filtering algorithm, and finally the centralized fusion is used to denoise the image. The simulation results show that the pro - posed algorithm is more effective than other algorithms in improving the peak signal-to-noise ratio （PSNR） and image clarity.