## 4 Experimental Results and Analysis
### Quantitative Results  
The efficiency and performance of the proposed approach were validated and quantified by comparing the greyscale CT images with other well-known standard denoising filters, such as mean, median, Gaussian, wiener, non-local means (NLMs), and discrete wavelet transform (DWT) for additive Gaussian blur noise (AGBN) reduction using PSNR, SSIM, and MSE. The CT images R1, R2, R3, R4, R5, and R6 were used to compare the proposed approach with the standard filters for 5% to 60% noise intensity. Tables 1-5 in this section shows comparison of the proposed ensemble approach with other standard techniques and their performance in terms of PSNR, SNR and SSIM at various additive GBN noise intensities. Similarly Figures 4-9 reflect the performance of standard techniques and proposed ensemble approach for denoising additive Gaussian blur noise (AGBN). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**SNR Values**} \\ \hline \multirow{2}{*}{**Denoising Scheme**} & \multicolumn{4}{c}{**Gaussian Blur Noise at Different Intensities (\%)**} \\ \cline{2-7}  & **5\%** & **10\%** & **15\%** & **20\%** & **25\%** & **30\%** \\ \hline Non-Local Means [23] & 22.7497 & 19.8691 & 18.0366 & 12.8525 & 9.8674 & 8.0812 \\ \hline Gaussian [24] & 24.7053 & 23.7319 & 20.8656 & 14.7141 & 11.6318 & 9.9032 \\ \hline Median [25] & 25.8455 & 21.9760 & 20.1306 & 15.0174 & 11.8910 & 10.2377 \\ \hline DWT [27] & 22.4563 & 19.6455 & 17.8646 & 12.6789 & 9.4673 & 7.8956 \\ \hline Mean [28] & 26.9982 & 22.9896 & 20.1805 & 18.0253 & 13.9653 & 10.2240 \\ \hline Wiener [42] & 24.8508 & 21.8702 & 19.1477 & 15.9636 & 10.9785 & 9.1923 \\ \hline DnCNN [56] & 20.0456 & 17.8654 & 16.9213 & 11.0486 & 7.9564 & 6.2893 \\ \hline Proposed & 18.6893 & 15.4563 & 15.0895 & 9.7987 & 6.8956 & 5.1124 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: (a) SNR for different denoising schemes at various SPN noise intensities (5–30% noise intensity). (b) SNR for different denoising schemes at various SPN noise intensities (35% to 60%). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**PSNR**} \\ \hline \multirow{2}{*}{**Denoising Scheme**} & \multicolumn{4}{c}{**Gaussian Blur Noise at Different Intensities (\%)**} \\ \cline{2-7}  & **5\%** & **10\%** & **15\%** & **20\%** & **25\%** & **30\%** \\ \hline Non-Local Means [23] & 29.6242 & 27.8872 & 26.1304 & 23.0332 & 19.9996 & 24.2514 \\ \hline Gaussian [24] & 29.6897 & 28.7325 & 24.8656 & 21.7141 & 18.6318 & 22.9032 \\ \hline Median [25] & 31.8468 & 28.9768 & 27.1306 & 22.0174 & 18.9910 & 13.2377 \\ \hline DWT [27] & 30.9422 & 29.7876 & 27.1765 & 23.1353 & 19.9853 & 23.2240 \\ \hline Mean [28] & 27.9920 & 27.9896 & 27.1805 & 19.0253 & 18.9953 & 21.6240 \\ \hline Wiener [42] & 30.7984 & 27.8902 & 26.1477 & 20.9636 & 17.9785 & 22.1923 \\ \hline DnCNN [56] & 31.9468 & 30.7896 & 28.5469 & 24.8696 & 20.9874 & 25.6457 \\ \hline Proposed Approach & 34.7585 & 31.6760 & 29.2267 & 25.9174 & 21.4910 & 27.6377 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: (a) PSNR comparison for the proposed approach with 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). (b) PSNR comparison for the proposed approach with wiener, mean, median, Gaussian, DWT, NLM filter, and DnCNN for selected CT scan images at various AGBN intensities (35–60% noise intensity).