Medical images may be corrupted by noise. This noise affects the image quality and can obscure important information required for accurate diagnosis. Effectively apply filtering techniques can facilitate diagnosis or reduce radiation exposure. In this paper, we introduce a parallel method designed to reduce mixed Gaussian-impulse noise from digital images. The method uses fuzzy logic and the fuzzy peer group concept. Implementations of the method on multi-core interface using the open multi-processing (OpenMP) and on graphics processing units (GPUs) using CUDA are presented. Efficiency is measured in terms of execution time and in terms of MAE, PSNR and SSIM over medical images from the mini-MIAS database and over computed radiography (CR) images generated at different exposure levels. These images have been contaminated with impulsive and/or Gaussian noise. Experiments show that the proposed method obtains good performance in terms of the above mentioned objective quality measures. After applying multi-core and GPUs optimization strategies, the observed time shows that the new filter allows to remove mixed Gaussian-impulse noise in real-time.

医学图像可能会被噪音破坏。这种噪声会影响图像质量，并且可能使准确诊断所需的重要信息模糊不清。有效地应用过滤技术可以促进诊断或减少辐射暴露。在本文中，我们介绍了一种并行方法，旨在减少数字图像中的混合高斯脉冲噪声。该方法使用模糊逻辑和模糊对等组概念。提出了该方法在使用开放式多处理（OpenMP）的多核接口上以及在使用CUDA的图形处理单元（GPU）上的实现。效率是根据执行时间，以及根据来自mini-MIAS数据库的医学图像以及不同曝光水平下生成的计算机射线照相（CR）图像的MAE，PSNR和SSIM来衡量的。这些图像已被脉冲和/或高斯噪声污染。实验表明，该方法在上述客观质量指标上取得了良好的性能。在应用多核和GPU优化策略后，观察到的时间表明，新滤波器可实时消除混合的高斯脉冲噪声。