A comprehensive achievement investigation of iterative mean filter for outlier extinguish aspiration on ubiquitous FVIN

Under commonwealth of the outlier extinguish inspection, exclusively on the impulsive outlier, the outlier extinguish algorithm is a substantial step, which is early performed prior to further computer vision steps thereupon the iterative mean filter (IMF) is inaugurated for fix value impulsive noise (FVIN) and grown into one of the superior achievement outliers extinguish algorithms. This academic article focuses to investigate the correlative achievement of the outlier extinguish algorithm established on IMF, is inaugurated from mean filter (MF) for carrying out the poor achievement of the aforesaid outlier extinguish algorithms (standard median filter (SMF), MF, and adaptive median filter (AMF)), for FVIN at omnipresent scattering of outlier consistency (5-90%). The analytical experiment comprehensively exploits on bountiful figures (F16, Girl, Lena, and Pepper) that are inspected in order to analyze the correlative achievement of an outlier extinguish algorithm established on IMF. In contrast with the aforesaid outlier extinguish algorithms (SMF, MF, and AMF), the outlier extinguish algorithm established on IMF has superior achievement from the experimental results.在脉冲异常值的异常值消除检测场景中，异常值消除算法是一项重要进展——此类算法需在后续计算机视觉处理步骤前提前执行。针对固定值脉冲噪声（fix value impulsive noise, FVIN），迭代均值滤波器（iterative mean filter, IMF）应运而生，并已发展为性能优异的异常值消除算法之一。本文旨在探究基于IMF的异常值消除算法的相关性能：鉴于传统异常值消除算法（标准中值滤波器（standard median filter, SMF）、均值滤波器（mean filter, MF）与自适应中值滤波器（adaptive median filter, AMF））性能欠佳，本文从均值滤波器（MF）出发构建基于IMF的异常值消除算法，并在异常值密度为5%至90%的全分布场景下处理FVIN问题。分析对比实验全面采用经典测试图像（F16、Girl、Lena与Pepper），以验证基于IMF的异常值消除算法的相关性能。实验结果表明，相较于前述传统异常值消除算法（SMF、MF与AMF），基于IMF的异常值消除算法具备更优异的性能。