Rashid Nasr, Berriri Kamel, Albekairi Mohammed, Kaaniche Khaled, Ben Atitallah Ahmed, Khan Muhammad Attique, El-Hamrawy Osama I
Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia.
Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Nasr City, Cairo 11884, Egypt.
Diagnostics (Basel). 2022 Nov 9;12(11):2738. doi: 10.3390/diagnostics12112738.
In this paper, we propose a new Modified Laplacian Vector Median Filter (MLVMF) for real-time denoising complex images corrupted by "salt and pepper" impulsive noise. The method consists of two rounds with three steps each: the first round starts with the identification of pixels that may be contaminated by noise using a Modified Laplacian Filter. Then, corrupted pixels pass a neighborhood-based validation test. Finally, the Vector Median Filter is used to replace noisy pixels. The MLVMF uses a 5 × 5 window to observe the intensity variations around each pixel of the image with a rotation step of π/8 while the classic Laplacian filters often use rotation steps of π/2 or π/4. We see better identification of noise-corrupted pixels thanks to this rotation step refinement. Despite this advantage, a high percentage of the impulsive noise may cause two or more corrupted pixels (with the same intensity) to collide, preventing the identification of noise-corrupted pixels. A second round is then necessary using a second set of filters, still based on the Laplacian operator, but allowing focusing only on the collision phenomenon. To validate our method, MLVMF is firstly tested on standard images, with a noise percentage varying from 3% to 30%. Obtained performances in terms of processing time, as well as image restoration quality through the PSNR (Peak Signal to Noise Ratio) and the NCD (Normalized Color Difference) metrics, are compared to the performances of VMF (Vector Median Filter), VMRHF (Vector Median-Rational Hybrid Filter), and MSMF (Modified Switching Median Filter). A second test is performed on several noisy chest x-ray images used in cardiovascular disease diagnosis as well as COVID-19 diagnosis. The proposed method shows a very good quality of restoration on this type of image, particularly when the percentage of noise is high. The MLVMF provides a high PSNR value of 5.5% and a low NCD value of 18.2%. Finally, an optimized Field-Programmable Gate Array (FPGA) design is proposed to implement the proposed method for real-time processing. The proposed hardware implementation allows an execution time equal to 9 ms per 256 × 256 color image.
在本文中，我们提出了一种新的改进拉普拉斯向量中值滤波器（MLVMF），用于对受“椒盐”脉冲噪声污染的复杂图像进行实时去噪。该方法由两轮组成，每轮包含三个步骤：第一轮首先使用改进拉普拉斯滤波器识别可能被噪声污染的像素。然后，受损像素通过基于邻域的验证测试。最后，使用向量中值滤波器替换噪声像素。MLVMF使用5×5窗口以π/8的旋转步长观察图像中每个像素周围的强度变化，而传统拉普拉斯滤波器通常使用π/2或π/4的旋转步长。由于这种旋转步长的细化，我们能更好地识别受噪声污染的像素。尽管有这个优点，但高比例的脉冲噪声可能会导致两个或更多受损像素（具有相同强度）碰撞，从而无法识别受噪声污染的像素。因此需要使用第二组滤波器进行第二轮处理，该组滤波器仍基于拉普拉斯算子，但仅关注碰撞现象。为了验证我们的方法，首先在标准图像上对MLVMF进行测试，噪声百分比从3%到30%不等。通过处理时间以及通过峰值信噪比（PSNR）和归一化色差（NCD）指标衡量的图像恢复质量方面获得的性能，与向量中值滤波器（VMF）、向量中值 - 有理混合滤波器（VMRHF）和改进开关中值滤波器（MSMF）的性能进行比较。第二项测试是对用于心血管疾病诊断以及新冠肺炎诊断的几幅噪声胸部X光图像进行的。所提出的方法在这类图像上显示出非常好的恢复质量，特别是当噪声百分比很高时。MLVMF提供了5.5%的高PSNR值和18.2%的低NCD值。最后，提出了一种优化的现场可编程门阵列（FPGA）设计，以实现所提出的方法进行实时处理。所提出的硬件实现允许每256×256彩色图像的执行时间等于9毫秒。
