## 2 Related Work  
Noise in medical images refers to unwanted random variations or distortions superimposed on the underlying image information. It degrades the quality and clarity of images, making it challenging to accurately interpret and analyze them [33]. Noise can arise from various medical imaging sources, including imaging equipment, signal acquisition, patient factors, and image processing [34]. Image denoising aims to obtain the best of an original image from the corrupted image. Noise reduction improves the perception of images and usually results in better performance for different image analysis and computer vision algorithms [35]. In [36], the authors pointed out that during the transmission of medical images, noise becomes a dominant factor that deteriorates and degrades the image's contrast, reducing its quality and appearance and creating problems in the diagnostic phase. Salt and pepper noise (SPN) and Gaussian noise (GN) are common types of noise in medical images that occur in acquisition or data transmission through any network or medium [37; 38; 39]. [PERSON] et al. [40], in their quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography, eliminated Gaussian noise, and maintained sharpness using DnCNN. In [41], Gaussian noise was added with a standard deviation of 0.002 to thoracic CT images. A fast, non-local means (FNLM) denoising algorithm removed blurring in the images. The FNLM was more efficient than conventional denoising filters, such as Gaussian, wiener, and median filters. [PERSON] et al. [42] assessed denoising filters for brain MRI-weighted contrast-enhanced images. The PSNR, SSIM, and MSE are statistical parameters used for analyzing the performance of the filters. The study showed that the wiener filter is considered the most efficient for Gaussian noise. In the case of speckle noise, anisotropic filters work better on low noise density, whereas the Gaussian filter works better for high noise density. [PERSON] et al. [43] used adaptive wavelet transform and CNN for image denoising, and PSNR was calculated as high and MSE as low. In addressing Gaussian and Rician noise issues of data loss due to compression and preservation of edge details, [PERSON] et al. [44] used Bayesian shrinkage-based fused wavelet transform (BSbFWT) and the block-based autoencoder network (BBAuto-Net) to remove noise from MRI. A novel algorithm that combines the bilateral filter and its method of noise thresholding, using dual-tree complex wavelet transform to remove Gaussian noise in the image, was proposed by [PERSON] et al. [45]. The experimental results show that the proposed algorithm is superior to other existing filtering algorithms in terms of visual quality and has very good PSNR, SSIM, and UIQI values. However, the issues around image blurring, contrast reduction, and quantitative inaccuracies of PSNR and SSIM were not adequately addressed. The CNN and bilateral filters were used to remove Gaussian noise from CT images in [46], and the authors of [47] presented a novel window-based method to remove high-density salt and pepper noise for optimal ROI (region of interest) detection of the brain MRI images. The output was used in watermarking and extracting hidden data in this type of image. An impulse noise removal algorithm model was proposed based on logarithmic images before medical images [48]. Experimental results using PSNR and MSE showed that the method was superior in terms of the effectiveness of impulse noise (salt and pepper noise) removal for medical images, CT, or MRI [49]. In [50], they developed a fast method based on Fuzzy Logic for Gaussian impulsive noise reduction in CT medical images. By applying parallel computing strategies, the obtained computing times indicated that the introduced filter reduced Gaussian-impulse-mixed noise on CT medical images in real time. In [51], the authors discussed how Gaussian filters effectively removed random additive noise, such as Gaussian noise that follows a uniform Gaussian distribution.