## 1 Introduction  
Computed tomography (CT) is a widely used medical imaging modality that precisely identifies anatomical structures and abnormalities [1]. Medical imaging has revolutionized the healthcare sector by assisting medical professionals in several ways, such as in disease diagnosis, treatment, and risk prediction [2]. However, popular medical imaging modalities, such as magnetic resonance imaging (MRI), ultrasound (US) images, positron emission tomography (PET), and computed tomography (CT) images [2], are degraded by various kinds of noise, not limited to Gaussian noise, speckle noise, Poisson noise, and salt and pepper noise, which severely affects fine details of an image, such as edges, lines, and points [3; 4]. For instance, positron emission tomography (PET) is a procedure that involves nuclear imaging to provide information about the operation of different tissues and organs. These images are usually degraded by a low signal-to-noise ratio and blurred edges caused by Poisson and Gaussian noise. Similarly, CT medical images are corrupted by Gaussian and salt and pepper noise, among others. Gaussian blur noise in CT imaging is caused by electronic noise, image post-processing, the reconstruction process, and quantization [5]. Ultrasound is a medical imaging modality that uses high-frequency sound waves to create real-time images of the inside of the body. It is a non-invasive and safe imaging technique widely used for diagnostic and monitoring purposes. When ultrasound waves propagate through a biological medium, the images are contaminated with speckle noise, obfuscating the pertinent details and reducing the contrast of the soft tissues, thereby degrading their overall visual quality [6]. Medical image analysis encompasses various image types characterized by their generation and appearance, and each is affected by distinct noise that deteriorates their image quality. The major challenge in the process of medical imaging is to obtain an image without loss of any meaningful information for decision-making. Noise or artifacts corrupt the images obtained during the acquisition and further processing stages [7]. Unlike natural images, most medical images pose signal-dependent noises; hence, it is hard to remove them by using conventional raw image denoising techniques [8]. Noise refers to the random variations of brightness and color that are not part of the original image, which deteriorates the image quality and even makes them diagnostically unusable [9]. The blurry and corrupt image quality reduces the visibility of structural details and discourages further decision-making, leading to poor diagnosis, analysis, and treatment [10]. Gaussian noise is the type of noise that arises from sensor noise, heat propagation, or circuit noise that affects CT scan images. Gaussian noise introduces random fluctuations in pixel intensity levels across the image, leading to a loss of image clarity, sharpness, and fine details' preservation and restoration [11]. When CT scan images are acquired, factors such as photon statistics, electronic noise, and patient motion can introduce noise into the images [12]. The noise can obscure fine details and reduce the overall image quality, leading to information loss [13]. The main aim of image denoising is to remove the noise while preserving the details of the image and cover aspects such as edge preservation and robustness to any artifacts [14]. In addition, denoising and enhancement of medical images can be helpful in image restoration, feature extraction, and in reducing distortion of images obtained from complex imaging modalities such as MRI, PET, and CT [15; 16]. Several noise reduction approaches have been reported to address this problem during preprocessing and post-processing stages. These approaches include Gaussian filters (GF), mean filters (mean-F), median filters (Median-F), bilateral filters (BF), wiener filters (WF), non-local mean filters (NLM), and denoising convolutional neural networks (DrCNN) [17; 18; 19; 20].