## 6 Conclusions  
Computed tomography (CT) is a commonly used imaging modality that provides diagnostic information for medical images. In this study, 1294 CT medical images were used, whereby 1096 were affected by Gaussian blur noise, while 198 were affected by salt and pepper noise. Gaussian blur noise (GBN) in the images degrades the visual quality and restoration of the image. This research primarily focused on Gaussian blur noise, which dominated 85% of the images. To thoroughly evaluate the denoising capabilities and accommodate possible fluctuations, a systematic methodology was implemented. Gaussian blur noise was added to the images at varying levels, ranging from 5% to 60% in noise intensity. This diverse spectrum of noise intensities facilitated the assessment of the denoising techniques' efficacy across a range of noise levels. This study proposed a  
Figure 9: Graph for the comparison of SSIM values of images R1, R2, R3, R4, R5, and R6 using the proposed approach and standard filtering schemes. wavelet-based anisotropic Gaussian filter (AGF) and denoising CNN to remove noise in CT scan medical images. The Haar wavelet transform and AGF were used as preprocessing operations. The process started by adding Gaussian blur noise to the medical images, and the generated noisy images were then denoised using AGF. Haar transform further decomposed the denoised CT scan image into different frequency bands. The DnCNN was combined with AGF and wavelet Haar transform for post-processing. This study employed six CT scan images: R1, R2, R3, R4, R5, and R6 (grayscale images), for experimental analysis. The standard denoising filters, such as median, mean, wiener, Gaussian, NLM, DWT, and DnCNN, were applied to denoise the images affected by additive Gaussian blur noise, whose performance was compared with the proposed approach, whereby measurements were performed in Python, and the PSNR, SSIM, and MSE values were measured. The results from Table 1 and Figure 4, revealed that the proposed ensemble approach yielded the best results for PSNR at different additive Gaussian blur noise intensities from 5% to 60%. In Table 1, the PSNR values for the proposed approach were higher than other standard denoising filter schemes at 34.7585, 31.6760, 29.2267, 24.9174, 21.4910, and 27.6377 in a range of 5% to 30% noise intensities, respectively. This was closely followed by DnCNN results at 31.9468, 30.7896, 28.5469, 20.9874, and 25.6457, respectively. When the PSNR was high, the MSE between the original and reconstructed images was very low. Therefore, this implies that the image was adequately restored, and the restored image quality was finer. The resultant SSIM index is a decimal value between \(-1\) and \(1\), where \(1\) indicates perfect similarity, \(0\) indicates no similarity, and \(-1\) indicates perfect anti-correlation. Consequently, if the PSNR value is low, the quality of the restored image could be better. The proposed approach aims to reduce all types of Gaussian noise, and the central feature is that the noise-free pixels are left unchanged. The proposed approach performed better than previous experiments, demonstrating its efficiency and suitability for denoising CT scan medical images. The SSIM index is a decimal value that range between \(-1\) and \(1\). A value of \(1\) indicates that the two images are very similar or the same, while a value of \(0\) demonstrates that the two images are very different and no similarity and -1 indicates perfect anti-correlation. These values are often adjusted to the range of [0, 1], where the extremes hold the same meaning. The SSIM values in Table 5 for the proposed approach were 1.000, 1.000, 1.0000, 0.9967, 0.9796, and 0.9986, respectively. Since the SSIM values range between \(0\) and \(1\), \(1\) perfectly matches the reconstructed image with the original image. It is observed that the proposed approach had higher SSIM values in comparison to other denoising methods used in this study.