# A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy
## 4 Experimental Results and Settings
### Contrast-to-Noise-Ratio (CNR)  
The objective of CNR is to estimate the noise and contrast between an input and resultant enhanced image. Recently, CNR has been extensively used in medical imaging quality assessments [32]. The formula for the calculation of CNR is as follows:  
\[CNR\ =\frac{Contrast}{Noise}=\ \frac{|\mu_{1}-\mu_{2}|}{\sqrt{\sigma_{1}^{2}+ \sigma_{2}^{2}}}. \tag{14}\]  
Here in Equation (14), \(\mu_{1}\), \(\mu_{2}\), \(\sigma_{1}\), and \(\sigma_{2}\) indicate the expectation values and standard deviations of two different signals, i.e., two areas of different brightness in a fundus image.  
As seen in Table 5, the proposed fundus image enhancement method was able to improve the contrast of noisy, poor contrast, and non-uniform illumination images. The achieved CNR values of the presented art were found to be more prominent than histogram-oriented approaches.  
The mean and standard deviation values of the original and final enhanced images of the proposed art and all other histogram-based approaches were calculated, as given in the comparison Table 6. Standard deviation implies image quality, i.e., a higher standard deviation value indicates a high-quality image. As seen in Table 6, the achieved standard deviation values of the proposed fundus image enhancement art using Entropy (4.60 \(\pm\) 4.52), PSNR (23.78 \(\pm\) 20.10), SSIM (0.98 \(\pm\) 0.96), and CNR  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Statistical Parameter** & **Method** & **Image01** & **Image02** & **Image03** & **Image04** \\ \hline \multirow{4}{*}{**SSIM**} & HE & 0.96 & 0.95 & 0.91 & 0.90 \\  & AHE & 0.95 & 0.96 & 0.93 & 0.91 \\  & CLAHE & 0.96 & 0.96 & 0.92 & 0.94 \\  & ESIHE & 0.97 & 0.96 & 0.94 & 0.95 \\  & **PROPOSED** & **0.98** & **0.97** & **0.96** & **0.98** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Structure similarity index measure (SSIM) comparison of the proposed art with literature approaches. A large value denotes high structure similarity.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Statistical Parameter** & **Method** & **Image01** & **Image02** & **Image03** & **Image04** \\ \hline \multirow{8}{*}{**CNR**} & Original & 3.81 & 3.70 & 3.98 & 3.99 \\  & HE & 4.10 & 5.01 & 4.78 & 5.65 \\  & AHE & 4.11 & 4.99 & 5.10 & 5.79 \\  & CLAHE & 5.99 & 6.12 & 6.45 & 7.01 \\  & ESIHE & 7.90 & 6.90 & 6.90 & 7.90 \\  & **PROPOSED** & **8.10** & **8.23** & **7.67** & **8.78** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Contrast-to-noise ratio (CNR) comparison of proposed scheme with histogram approaches. A large value denotes a better CNR.  
(8.78 \(\pm\) 7.67) exhibit high mean and standard deviation results. Meanwhile, the results of the other approaches did not exhibit such a performance. In addition to the obtained standard deviation results, [PERSON]'s \(F\)-statistic was also applied to get the statistical analysis in order to justify the superiority of the proposed fundus image enhancement method over the histogram-related arts using entropy, PSNR, and CNR. In Table 7, the outcome of this statistical analysis is reported by showing \(F\)-values and \(p\)-values [33]. The \(p\)-value was observed to be less than 0.05 for all quality parameters. Thus, the quantitative assessment of all applied image enhancement arts with respect to quality metrics was statistically significant. It can be seen in Table 7 that the achieved statistical results denote that the proposed art was more successful in obtaining high image quality with intact image features than its competitors.