## 4 Experiments and Results
### Qualitative Results  
The proposed method also achieves significantly better restoration and enhancement results in terms of visual effects. To assess the quality of our results, we performed a survey on our university campus and showed the results to 75 participants (students, faculty, and staff). Most of the participants (72) gave satisfactory remarks, except for a few (3) who did not find any differences, resulting in a 96% success rate. It can be clearly seen from the results shown in Figures 4-6 that the noise is significantly reduced, the details are enhanced, and the images are sharpened when compared with the input images. connections, the network faces convergence issues, resulting in higher training errors and lower PSNR. Additionally, the Selective Kernel Feature Fusion (SKF) mechanism, which facilitates information exchange among parallel convolution streams, proves advantageous and boosts performance. Likewise, the Dynamic Attention Units (DUs) contribute positively to the overall image quality. Table 5 shows feature combinations with summation (SUM), concatenation (CAT), and Selective Kernel Feature Fusion (SKF). The proposed SKF is better than SUM and CAT, utilizing \(\sim\)6 times fewer parameters than CAT and generating better PSNR results. Specifically, SKF effectively enhances feature representation by adaptively selecting and combining informative features from parallel convolution streams. Additionally, a significant reduction in parameters highlights the efficiency and effectiveness of SKF in improving overall model performance. In Table 6, we perform an experiment on the RealSR [8] dataset. For the denoising task, it has not shown much difference, as the dataset has no noise. Still, the super-resolution task performs better visually and gets higher PSNR and SSIM values, which shows the significance of the method in real-world scenarios. Although it is a computationally resource-oriented task, as the process of denoising has been performed on a denoised dataset, the denoising mechanism still improves the image quality. Furthermore, in our ablation study, as shown in Figure 7, we found that each module within the ELEF model significantly contributes to its overall performance in image restoration and enhancement tasks. When the MSRB (Multi-Scale Residual Block) was removed, our model struggled to capture fine details, resulting in slightly softer restored images, as shown in Figure 7. Excluding the SKF (Selective Kernel Fusion) module affected the model's ability to integrate information across the image, leading to less natural-looking enhancements. Without the DU (Dual Attention Unit), the model had difficulty focusing on important image features, resulting in slightly noisier or less sharp restorations. Lastly,  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scales**} & \multicolumn{2}{c}{**Bicubic**} & \multicolumn{2}{c}{**RCAN [83]**} & \multicolumn{2}{c}{**LP-KPN [84]**} & \multicolumn{2}{c}{**MIRNet [72]**} & \multicolumn{2}{c}{**ELEF (Ours) \(\uparrow\)**} \\ \cline{2-10}  & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\ \hline \(\times\)**2** & 32.62 & 0.906 & 33.86 & 0.921 & 33.91 & 0.926 & 34.34 & 0.934 & 37.16 & 0.939 \\ \(\times\)**3** & 29.33 & 0.841 & 30.41 & 0.861 & 30.41 & 0.867 & 31.15 & 0.884 & 34.32 & 0.890 \\ \(\times\)**4** & 27.98 removing the RRM (Residual Refinement Module) meant that the model could not refine images as effectively, leaving some minor imperfections. These findings highlight the importance of each module (MSRB, SKF, DU, RRM) in enhancing the ELEF model's performance and guiding future improvements to achieve better restoration quality in various applications. In addition, we have analyzed our results on different resolutions: \(\times 2\), \(\times 3\), and \(\times 4\), as shown in Figures 8 and 9. It can be seen from the visual results that with the increase in resolution, the quality of the visual results deteriorated.