基于灰度密度和四方向的随机脉冲噪声检测

Random-valued Impulse Noise Detection Based on Pixel-valued Density and Four Directions

提高检测正确率的同时降低漏检率和错检率是脉冲噪声检测过程中的难点。提出了两阶段的检测方法,第一阶段,根据窗口中心点的灰度密度小于某阈值检测噪声,分5次迭代,对每次检测到的噪声进行中值滤波,滤波图像作为下一次检测的输入图像;第二阶段,用窗口4个方向检测噪声,并根据MAD值自适应设定阈值。以512×512的Lena和Boat为测试对象,添加10%至50%的随机脉冲噪声进行仿真实验,结果表明,随着噪声密度的增加,错检数都稳定在较低值,漏检数保持在理论上的低值。

Improving correction detection,meanwhile decreasing miss detection and false alarm is a challenge in randomvalued impulse noise detection.A two stages noise detection algorithm was proposed,at first stage,noisy pixels were detected when their pixel-value density was less than certain threshold,this stage included five iterations,noise pixels were removed through median filtering in every iteration,and the filtered images were the input images for next iterations.At second stage,noisy pixels were detected through four directions,and adaptive threshold was based on MAD.The test images were 512×512 Lena and Boat added by random-valued impulse noise from 10% to 50%.Simulations indicate that with the increase of noise density,false alarm remains low and miss detection remains theoretical low level.