Digital images are affected by various types of noises, which may be incorporated into the image during its acquisition, transmission etc. Removal of these unwanted noises is an essential task, especially for digital images being used for various science, engineering, and biomedical applications. Depending upon the image acquisition process/devices, transmission medium, and other factors, noise present in digital images can be modeled into various types such as Gaussian noise, salt and paper noise etc. Most of the de-noising algorithms such as bilateral filter, Gaussian filter etc., perform well for Gaussian noise. However, these algorithms perform unsatisfactorily for impulsive noise, such as speckle noise. Aim of the proposed (research) work is to develop a de-noising algorithm which can remove different types of noises especially impulsive noises from digital images with high accuracy and at the same time preserving essential or vital image information such as edges, texture information etc. Here, in this paper, we propose an image de-noising algorithm based on an approximated fractional integrator (AFI), which overcomes the above-discussed issues efficiently. It has been employed for both black & white and grayscale images. For grayscale image (because of many intensity levels), we propose a new adaptive method for selection of fractional order (q) that depends on specific features such as gradient, entropy, local roughness, and contrast of the image. A hardware implementation of the proposed algorithm using NEXYS 4 DDR Artix-7, which is a low power FPGA device is done to further validate the performance of the proposed AFI based algorithm in a practical environment. Power consumption and resource utilization of the proposed algorithm is also addressed. Finally, three different quantitative parameters i.e., peak-signal-to-noise-ratio, structural similarity index and cross-correlation has been calculated, and the proposed method is compared with some state-of-the-art techniques, which validates the effectiveness of proposed algorithm especially if impulsive noise is present in digital images.

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基于 FPGA 的使用近似分数积分器的实时图像去噪设计

数字图像会受到各种类型的噪声的影响，这些噪声可能会在其获取、传输等过程中并入图像中。去除这些不需要的噪声是一项必不可少的任务，尤其是对于用于各种科学、工程和生物医学应用的数字图像。根据图像采集过程/设备、传输介质和其他因素，数字图像中存在的噪声可以建模为各种类型，如高斯噪声、盐和纸噪声等。 大多数去噪算法如双边滤波器、高斯滤波器等，对高斯噪声表现良好。然而，这些算法对于脉冲噪声（例如斑点噪声）的表现并不令人满意。拟议（研究）工作的目的是开发一种去噪算法，该算法可以从数字图像中高精度去除不同类型的噪声，尤其是脉冲噪声，同时保留基本或重要的图像信息，如边缘、纹理信息等在这里，在本文中，我们提出了一种基于近似分数积分器（AFI）的图像去噪算法，该算法有效地克服了上述问题。它已被用于黑白和灰度图像。对于灰度图像（由于许多强度级别），我们提出了一种新的自适应方法来选择分数阶 (q)，该方法取决于图像的梯度、熵、局部粗糙度和对比度等特定特征。所提出算法的硬件实现使用 NEXYS 4 DDR Artix-7，这是一个低功耗 FPGA 设备，用于进一步验证所提出的基于 AFI 的算法在实际环境中的性能。还解决了所提出算法的功耗和资源利用问题。最后，计算了三个不同的量化参数，即峰值信噪比、结构相似指数和互相关，并将所提出的方法与一些最先进的技术进行了比较，验证了其有效性建议的算法，尤其是在数字图像中存在脉冲噪声的情况下。