## 4 Experimentation
### Statistical Analysis  
An objective approach to performance evaluation was conducted on the existing and proposed methods using five different metrics, including PSNR, MSE, RMSE, NC, and CoC. An analysis of Tables 1 and 2 makes it evident that the proposed Fast Local Laplacian Filter (FLLF) method offers the most promising outcomes. For both test image 1 and test  
Figure 3: Input image and its corresponding contrast enhancement results of Test Image 1: (**a**) input image, (**b**) Retinax, (**c**) CS, (**d**) GC, (**e**) HE, (**f**) LBC, (**g**) LTHE, (**h**) OMC, (**i**) PLT, (**j**) Sigmoid, (**k**) AHE, (**l**) BHE, (**m**) BBHE, (**n**) CLAHE, (**o**) DSIIE, (**p**) LT, (**q**) GTHE, (**r**) MHE, (**s**) MSRCR, and (**t**) FLLF. image 2, the FLLF method achieved the highest PSNR values, recording values of 36.61 and 40.12, respectively. In terms of MSE and RMSE, which measure the average squared difference between pixel values of the original and processed images, the FLLF method consistently attained the lowest values among all the techniques evaluated. Moreover, the CoC, a crucial metric for gauging enhancement quality, shows that the FLLF method closely approaches a value of 1. This indicates favorable results, particularly evident in the case of test image 2, and ranks the FLLF method as the third best for test image 1. Lastly, the NC metric highlights that the proposed algorithm achieved the highest value, indicating notable results for the LT technique on test image 1 and the OMC technique on test image 2. Tables 2 and 3 provide a visual representation of the most notable outcomes, with a clear distinction through color coding. The top three best results are visually differentiated using distinct colors: blue is assigned to the topmost results, green indicates the second-best outcomes, and orange highlights the third-best results. Figures 4 and 5 also include bar charts that demonstrate the top three best results and emphasize the superiority of our proposed method over all the other methods. Figures 6 and 7 present a visual representation of the time comparison between the proposed Fast Local Laplacian Filter (FLLF) method and various state-of-the-art techniques. Notably, FLLF, MHE, and OME exhibit favorable time complexities, signifying efficient performance. Conversely, the PLT method demonstrates the poorest time efficiency among the evaluated techniques. \begin{table}
\begin{tabular}{l l l l l l} \hline
**Contrast Techniques** & **PSNR** & **MSE** & **RMSE** & **NC** & **CoC** \\ \hline Retinax & 33.03 & 18,333.3601 & 135.4007 & 1.1377 & 0.6950 \\ \hline CS & 33.39 & 202.0203 & 14.2271 & 1.1628 & 0.9992 \\ \hline GC & 27.44 & 1189.6561 & 34.4913 & 1.1287 & 0.9954 \\ \hline HE & 27.58 & 3416.8531 & 58.4538 & 1.0514 & 0.9910 \\ \hline LBC & 27.76 & 7060.3217 & 84.0257 & 1.2837 & 0.9871 \\ \hline LTHE & 28.55 & 261.2856 & 16.1643 & 1.2102 & 0.9471 \\ \hline OMC & 36.39 & 201.0203 & 14.2248 & 1.1628 & 0.9999 \\ \hline PLT & 28.59 & 202.4859 & 14.2297 & 1.1092 & 0.9987 \\ \hline Sigmoid & 28.82 & 18,690.0791 & 136.7116 & 1.0525 & 0.4992 \\ \hline AHE & 28.45 & 312.3480 & 17.6733 & 1.1981 & 0.9390 \\ \hline BHE & 29.44 & 12,334.8630 & 111.0624 & 1.1844 & 0.8451 \\ \hline BBHE & 28.37 & 1353.1227 & 36.7848 & 1.2041 & 0.8897 \\ \hline CLAHE & 31.45 & 312.3480 & 17.6733 & 1.1981 & 0.9390 \\ \hline DSIHE & 27.89 & 3307.2439 & 57.5086 & 1.2546 & 0.7789 \\ \hline GTHE & 27.58 & 3416.8531 & 58.4538 & 1.0514 & 0.9910 \\ \hline LT & 27.54 & 1335.1442 & 36.5396 & 1.4988 & 0.9759 \\ \hline MHE & 27.95 & 7390.1161 & 85.9657 & 1.1611 & 0.2412 \\ \hline MSRCR & 27.64 & 13,659.7440 & 116.8749 & 1.1412 & 0.1630 \\ \hline FLLF & 36.61 & 199.1741 & 13.9166 & 1.4757 & 0.9994 \\ \hline \end{tabular}
\end{table} 
Table 2: Illustrates a comprehensive comparison between the proposed method and advanced noise removal techniques applied to test image 1.