# A Novel Guided Box Filter Based on Hybrid Optimization for Medical Image Denoising
## 4 Results and Discussion
### Result Analysis
\begin{table}
\begin{tabular}{l l l} \hline
**GDBF + HCPSO** & **Parameter Values** & **PSNR** \\ \hline \multirow{4}{*}{Filter Order} & 20 & 35.16 \\  & 25 & 35.30 \\  & 30 & 35.72 \\  & 35 & 35.16 \\ \hline \multirow{4}{*}{Population Size} & 5 & 35.75 \\  & 10 & 35.79 \\  & 15 & 35.72 \\  & 20 & 35.63 \\ \hline \multirow{4}{*}{Initial T\({}_{\text{r}}\)} & 0.01 & 35.79 \\  & 0.02 & 35.49 \\ \cline{1-1}  & 0.03 & 35.62 \\ \hline \end{tabular}
\end{table} 
Table 2: Optimal parameters for HCPSO algorithm. Figure 6: Visual representation of denoising using HCPSO. Table 4 shows the performance analysis of bio-inspired optimization algorithms, i.e., PSO, firefly, cuckoo, and the proposed HCPSO model. The number of images taken for testing was 25. Table 4 shows the performance analysis by considering the PSNR, SSIM, and FSIM of the bio-inspired optimization algorithms. The results analysis was performed on an input medical image with a size of 192 \(\times\) 192. The input image is corrupted with noise with variable variance ranging from 0.01 to 0.09. In Table 4, it is observed that the proposed HCPSO has achieved the highest PSNR, SSIM, and FSIM values. Therefore, from this analysis, it is observed that the designed proposed HCPSO model shows fast and best convergence towards the best solution compared to other bio-inspired algorithms. In contrast, the HPSO model also presented better edge preservation compared to others. Table 5 shows the performance analysis of bio-inspired optimization algorithms, i.e., PSO, firefly, cuckoo, and the proposed HCPSO model in terms of the varying low-pass filter orders used. Table 5 shows the performance analysis by considering PSNR, SSIM, and FSIM of the bio-inspired optimization algorithms. The results analysis was performed on an input medical image with a size of 192 \(\times\) 192. The input image is corrupted with noise with a variable variance of 0.01. Here, HCPSO optimization with a filter order of \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Variance** & **PSO** & **FF** & **CS** & **HCPSO** \\ \hline \multirow{8}{*}{PSNR} & 0.01 & 34.529 & 32.931 & 34.571 & 35.704 \\  & 0.03 & 31.966 & 31.997 & 32.763 & 33.981 \\  & 0.05 & 32.010 & 31.743 & 32.687 & 33.229 \\  & 0.07 & 30.407 & 30.699 & 31.326 & 32.967 \\  & 0.09 & 30.866 & 30.435 & 31.274 & 32.664 \\ \hline \multirow{8}{*}{SSIM} & **Variance** & **PSO** & **FF** & **CS** & **HCPSO** \\ \cline{2-6}  & 0.01 & 0.496 & 0.498 & 0.555 & 0.916 \\  & 0.03 & 0.514 & 0.428 & 0.519 & 0.872 \\  & 0.05 & 0.483 & 0.429 & 0.494 & 0.843 \\  & 0.07 & 0.453 & 0.365 & 0.463 & 0.822 \\  & 0.09 & 0.378 & 0.381 & 0.434 & 0.798 \\ \hline \multirow{8}{*}{FSIM} & **Variance** & **PSO** & **FF** & **CS** & **HCPSO** \\ \cline{2-6}  & 0.01 & 0.691 & 0.644 & 0.670 & 0.886 \\  & 0.03 & 0.683 & 0.611 & 0.645 & 0.842 \\ \cline{1-1}  & 0.05 & 0.641 & 0.596 & 0.629 & 0.819 \\ \cline{1-1}  & 0.07 & 0.604 & 0.574 & 0.618 & 0.802 \\ \cline{1-1}  & 0.09 & 0.608 & 0.565 & 0.598 & 0.781 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance analysis of bio-inspired optimization algorithms with respect to noise variance.