## 4 Experimental Validation
### Discussion  
Table 1 reports the MSE and RMSE examination of the OMKHES-MS model under distinct images. The experimental results indicated the improvements of the OMKHES-MS model interms of MSE and RMSE. Figure 6 illustrates the MSE inspection of the OMKHES-MS model with existing models on distinct images. The results indicated that the OMKHES-MS model has offered effectual outcomes with minimal values of MSE over the other WOA-EBS and GWO-EBS models. For instance, with Barbara image, the OMKHES-MS model has obtained lower MSE of 0.2715 whereas the WOA-EBS and GWO-EBS models have offered higher MSE of 0.2840 and 0.5407 respectively. Followed by, with Peppers image, the OMKHES-MS model has gained reduced MSE of 0.1937 whereas the WOA-EBS and GWO-EBS models have provided increased MSE of 0.2262 and 0.3053 respectively. Figure 7 exemplifies the RMSE examination of the OMKHES-MS model with existing models on diverse images. The experimental values reported that the OMKHES-MS model has resulted in improved performance with lower values of RMSE over the other WOA-EBS and GWO-EBS models. For instance, with Barbara image, the OMKHES-MS model has \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Images**} & \multicolumn{2}{c}{**MSE**} & \multicolumn{3}{c}{**RMSE**} \\ \cline{2-7}  & **OMKHES-MS** & **WOA-EBS** & **GWO-EBS** & **OMKHES-MS** & **WOA-EBS** & **GWO-EBS** \\ \hline Barbara & 0.2715 & 0.2840 & 0.5407 & 0.5211 & 0.5329 & 0.7353 \\ \hline Boat & 0.3146 & 0.3447 & 0.8515 & 0.5609 & 0.5871 & 0.9228 \\ \hline Foreman & 0.1703 & 0.1913 & 0.2732 & 0.4127 & 0.4374 & 0.5227 \\ \hline House & 0.0595 & 0.0739 & 0.3037 & 0.2438 & 0.2718 & 0.5511 \\ \hline Isabe & 0.3134 & 0.3478 & 0.6824 & 0.5598 & 0.5897 & 0.8261 \\ \hline Peppers & 0.1937 & 0.2262 & 0.3053 & 0.4401 & 0.4756 & 0.5525 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: MSE and RMSE analysis of OMKHES-MS technique with different images. Figure 6: Comparative MSE analysis of OMKHES-MS technique with existing models. attained decreased RMSE of 0.5211 whereas the WOA-EBS and GWO-EBS models have achieved increased RMSE of 0.5329 and 0.7353 respectively. In line with, Peppers image, the OMKHES-MS model has depicted minimal RMSE of 0.4401 whereas the WOA-EBS and GWO-EBS models have exhibited maximum RMSE of 0.4756 and 0.5525 respectively. A comprehensive SC and PSNR assessment of the OMKHES-MS model is compared with recent methods in Table 2. The experimental values notified that the OMKHES-MS model has gained effectual outcome with maximum values of SC and PSNR. For instance, the OMKHES-MS model has offered higher SC of 0.9986, 0.9991, 0.9991, 0.9986, 0.9986, and 0.9987 on the test Barbara, Boat, Foreman, House, Isabe, and Peppers images respectively. At the same time, the OMKHES-MS model has provided increased PSNR of 59.4556 dB, 58.1759 dB, 63.5057 dB, 72.6470 dB, 58.2083 dB, and 62.3885 dB on the test Barbara, Boat, Foreman, House, Isabe, and Peppers images respectively. A comparative SC analysis of the OMKHES-MS model with existing techniques under distinct test images is performed in Figure 8. The figure portrayed that the OMKHES-MS model has resulted in superior SC values under every image. For instance, with Barbara image, the OMKHES-MS model has accomplished higher SC of 0.9986 whereas the whale optimization algorithm (WOA)-EBS and grey wolf optimizer (GWO)-EBS models have