在信号处理领域,传统的自适应滤波算法采用的固定步长会导致稳态误差和收敛速度无法同时兼顾。针对这个问题,对最小平均p范数(Least Mean p-norm,LMP)算法进行改进,提出了一种基于改进双曲正切(tanh)函数的变步长最小平均p范数算法。该算法利用改进的tanh函数来调节步长,采用移动加权平均法构造变步长函数;同时引入了一个调节函数以进一步提升算法的性能。通过在海洋脉冲噪声干扰下进行仿真,实验表明,与已有的固定步长和变步长算法相比,改进的变步长LMP算法较好地兼顾系统的收敛速度和稳态误差;引入调节函数后的新算法在保证原有算法收敛速度的同时进一步降低了算法的稳态误差,从而兼顾了算法的收敛性和稳定性,具有较好的可行性。 相似文献
Traditional inpainting methods obtain poor performance for finger vein images with blurred texture. In this paper, a finger vein image inpainting method using Neighbor Binary-Wasserstein Generative Adversarial Networks (NB-WGAN) is proposed. Firstly, the proposed algorithm uses texture loss, reconstruction loss, and adversarial loss to constrain the network, which protects the texture in the inpainting process. Secondly, the proposed NB-WGAN is designed with a coarse-to-precise generator network and a discriminator network composed of two Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP). The cascade of a coarse generator network and a precise generator network based on Poisson fusion can obtain richer information and get natural boundary connection. The discriminator consists of a global WGAN-GP and a local WGAN-GP, which enforces consistency between the entire image and the repaired area. Thirdly, a training dataset is designed by analyzing the locations and sizes of the damaged finger vein images in practical applications (i.e., physical oil dirt, physical finger molting, etc). Experimental results show that the performance of the proposed algorithm is better than traditional inpainting methods including Curvature Driven Diffusions algorithm without texture constraints, a traditional inpainting algorithm with Gabor texture constraints, and a WGAN inpainting algorithm based on attention mechanism without texture constraints.