基于粒子群算法的柔性形态学滤波器

典型的中值和均值滤波器分别存在去噪不完全和使图像模糊的缺点，为此，提出了一个改进的柔性形态滤波器(ISMF),在保护细节的同时有效去除高斯和椒盐噪声。为定量分析该滤波器中参数和非线性约束条件，提出了一种改进的粒子群优化算法(msPSO),该算法具有更高的收敛速度和精度。实验表明经msPSO优化后的ISMF能够在峰值性噪比和形状误差上取得比较好的效果。

Soft morphological filter based on particle swarm algorithm

Typical median and mean filters have some drawbacks such as incomplete denoising and image blurring. Therefore, a new Improved Soft Morphological Filter (ISMF) was proposed to remove salt-and-pepper and Gaussian noise while preserving the details. In order to quantitatively analyze the parameters and nonlinear constraints in the filter, a modified simple Particle Swarm Optimization (msPSO) algorithm was given with high convergence speed and precision. The experimental results show that ISMF optimized by msPSO performs better on Peak Signal to Noise Ratio (PSNR) and shape error.