用于去除高斯噪声的自适应模糊切换中值滤波器

Jeba Jenitha M, Kani Jesintha D, Mahalakshmi P
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引用次数: 6

摘要

目前,在所有的图像处理系统中,图像恢复占有重要的地位,是图像处理系统的重要组成部分。医学图像,如脑磁共振成像(MRI),肝脏和肾脏的超声图像,视网膜图像和子宫图像图像经常受到各种类型的噪声,如高斯噪声和盐胡椒噪声的影响。所有的图像恢复技术都试图去除各种类型的噪声。本文讨论了去除椒盐噪声的各种滤波器,即均值滤波器、平均滤波器、中值滤波器、自适应中值滤波器、自适应加权中值滤波器、Gabor滤波器和噪声自适应模糊切换中值滤波器(NAFSM)。在所有滤波器中,NAFSM比其他滤波器更好地去除高斯噪声,并使用PSNR(峰值信噪比),MSE(均方误差),NAE(归一化绝对误差),归一化相互关系(NK),平均差(AD),最大差(MD), SC(结构含量)和产生去噪图像所需的时间等指标对所有滤波器的性能进行比较。
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Noise Adaptive Fuzzy Switching Median Filters for Removing Gaussian Noise
Recently, in all image processing systems, image restoration plays a major role and it forms the major part of image processing systems. Medical images such as brain Magnetic Resonance Imaging (MRI), ultrasound images of liver and kidney, retinal images and images of uterus images are often affected by various types of noises such as Gaussian noise and salt and pepper noise. All image restoration techniques attempts to remove various types of noises. This paper deals with various filters namely Mean Filter, Averaging Filter, Median Filter, Adaptive Median Filter, Adaptive Weighted Median Filter, Gabor Filter and Noise Adaptive Fuzzy Switching Median Filter (NAFSM) for removing salt and pepper noise. Among all the filters, NAFSM removes the Gaussian noise better than the other filters and the performance of all the filters are compared using metrics such as PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error), NAE (Normalized Absolute Error), Normalized Cross Correlation (NK), Average Difference (AD), Maximum Difference (MD), SC (Structural Content) and time elapsed to produce the denoised image.
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