## 4 Experimental Results and Analysis
### Qualitative Analysis  
The qualitative analysis visually compared the denoised image with an original noisy image. Noise reduction and preservation of image details were observed, whereby the denoised image appeared smoother and cleaner than the noisy image, with reduced visual artifacts caused by noise. Additionally, the denoised image exhibited a similar structure and pattern as the original image, indicating effective denoising. The intensity profiles wereanalyzed to assess the preservation of fine details. Intensity profiles are one-dimensional plots of pixel intensity values along a specific line or region of interest in the image. By comparing the intensity profiles of the denoised image with the original image, it verified whether important features and edges were well preserved in the denoised result. The qualitative analysis for denoising CT images is demonstrated in Figure 2.  
In Figure 2, the original image in (a) was applied with a grayscale filter in combination with an AGBN with a 30% noise level. The image was degraded after adding Gaussian blur noise, as shown in (b). The denoised image in (c) was generated by applying an anisotropic Gaussian filter, which suppressed the Gaussian noise while preserving edges by using more smoothing in the regions with little variations and less smoothing in the areas with solid intensity gradients. Anisotropic Gaussian filters enhanced the sharpness and overall image structure. Haar transform was then applied to decompose the noisy CT scan images into different frequency bands, including approximation and detail coefficients. Mainly, it analyzed and detected abrupt changes in intensity, such as edges in an image. Removing noise and lower-frequency components while retaining high-frequency information related to the edge helps enhance edge details and reduce blurring. Finally, the DnCNN was trained on a noisy and corresponding clean images dataset to learn the noise patterns and underlying structures. It took a noisy image as input during inference and used its learned knowledge to estimate and remove the noise. By eliminating the noise from the image, the DnCNN effectively reduced blurring caused by noise-related artifacts.  
In Figure 3, the CT scan image was corrupted by adding a 5-30% Gaussian blur noise level. The addition of noise was necessary to obtain the measure of error margins when noise levels deviated. The focus was on reducing the AGBN, which had 1096 images, translating to 85% noisy images contaminated with AGBN. Figure 3 shows the original greyscale CT scan image and the denoised image using the proposed approach. Images R1 to R6 had Gaussian blur noise added at different noise intensities to allow for testing the denoising method's effectiveness under various noise levels, as shown in Figure 3.  
The noisy images were denoised using wiener, mean, median, DWT, Gaussian, non-local means (NLM) filter, and DnCNN for selected CT scan images at various additive Gaussian blur noise (GBN) intensities (5-30% noise intensity) as shown in Figure 3a-f. As more noise is added to the image, blurring and visual quality increases. Image denoising techniques aim to improve the visual quality and interpretability of images by reducing the impact of noise which can otherwise degrade image quality and hinder the ability to extract meaningful information from the image. It was demonstrated that the proposed approach of wavelet-anisotropic Gaussian filter-based denoising CNN remarkably reduced the additive GBN, and at the same time, minimized the blurring effect and improved visual quality, achieving better image and fine details' preservation compared to other schemes used in the experiment.  
Figure 2: (a) Original image [55], (b) noisy image (30%), and (c) denoised image.