## 4 Experimentation
### Visual Analysis  
The effectiveness of the proposed algorithm was evaluated by assessing the image quality through human observation and perception. To evaluate the efficacy of the proposed technique in comparison with existing methods, a set of low-contrast coronary angiography medical images from a widely recognized benchmark database was employed. The versatility and effectiveness of the proposed method were thoroughly tested in contrast with other established approaches [37]. The comparison of the proposed method's performance against other existing methods is depicted in Figures 2 and 3.  
The outcomes depicted in Figure 2 demonstrate the simulation findings of the 'angiography' testing image alongside its corresponding histogram. The Retinax technique, illustrated in Figure 2b, demonstrates a notable concern with excessive noise and over-enhancement. The angiography images generated by the CS and GC techniques in Figure 2c,d suffer from irregular intensity distribution, resulting in the distortion of subtle edges within the testing image.  
As depicted in Figure 2e-h, the simulated angiography images derived from the HE, LBC, LTHE, and OMC methods exhibit multiple artifacts and struggle to preserve intricate edge features. Similarly, the results obtained from the PLT, Sigmoid, AHE, and BHE techniques (shown in Figure 2i-l) lack meaningful visual information due to their limited contrast adjustments. Furthermore, the experimental outcomes derived from the BBHE, CLAHE, and DSIHE methods (depicted in Figure 2m,o,p) produce contrast-enhanced images that are excessively bright.  
Among the displayed results, Figure 2p-s showcase visually appealing outcomes, albeit with reduced significant details. Notably, the proposed FLLF method, illustrated in Figure 2t, demonstrates that the enhanced angiography image possesses a visually pleasing appearance, characterized by well-defined edge details and preserved mean brightness.  
Figure 2: _Cont._  
Figure 2: 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.  
Figure 3: _Cont._  
Through an in-depth analysis of the histogram plot, it becomes evident that the proposed FLLF technique adeptly mitigates insignificant contrast enhancement issues by curtailing undesirable segments within the histogram plot. Furthermore, the technique enhances image details by evenly distributing intensity occurrence levels across the entire dynamic range. This process collectively contributes to the overall improvement of the angiography image, aligning well with visual expectations.  
Figure 3 presents the simulation results of the 'angiography' testing image, showcasing its histogram in parallel. Additionally, within Figure 3, each technique resultant image is visually illustrated alongside its accompanying histogram. This presentation allows for a comprehensive analysis and evaluation of the effectiveness and impact of each contrast enhancement technique.