# A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy
## 5 Discussion
Meanwhile, ESIHE achieved more acceptable entropy results than its extensions. In contrast to the ESIHE results, the entropy ratio of the proposed art was noticed to be very satisfactory, with reference to original image entropies. Hence, it can be concluded that the proposed method has the ability to enhance fundus image quality without improving noise and other artifacts. The third experiment was to test the potential of the proposed art using the PSNR parameter against the original and histogram-based methods. From Table 3, it is clearly shown that the obtained PSNR values of HE, AHE, CLAHE, and ESIHE were reported to be at a minimum compared to the proposed method. These arts failed to improve the contrast of the fundus image to an appreciable level. However, the PSNR of the presented art was found to be superior to the other methods, which means that it could boost the contrast of the fundus image to the highest level. It has been clear that a greater value of an SSIM or CNR leads to high contrast and identical information between the reference and well-enhanced image. In the fourth and fifth experiments, the obtained SSIM and CNR results of the proposed art, as given in Tables 4 and 5, were observed to be more superior than HE, AHE, CLAHE, and ESIHE. This further indicates that the proposed method was found to be successful in the improvement of fundus image contrast while preserving high structure similarity with the original image. The proposed fundus image enhancement strategy was also successful in the removal of blurriness and noise existing in the DR fundus image. As is clearly seen in Figure 9b, the output of the proposed scheme contained a high-contrast fundus image with more feasible DR features after the processing of an input noisy image. Hence, the proposed fundus image enhancement system can be a useful tool in the production of real-time DR applications. Figure 10 illustrates the strength of the proposed art in uneven illumination images. Here, it is noticed that non-uniform illumination was removed at a high rate by the proposed method. Moreover, fundus anatomic features were observed with a high visible ratio, resulting in making existing computerized DR systems more effective in DR assessment. Figure 9: A DR fundus image enhancement comparison: (**a**) Input noisy image, and (**b**) proposed enhanced image. From the aforementioned experiments, it is well understood that the proposed method was more successful in fundus image quality enhancement than histogram-based image enhancement approaches using a diverse range of low-contrast and non-uniform illumination of images. Both contrast enhancement and color preservation of blurry and non-uniform illumination fundus images were improved by the presented method without following any long pre- or postprocessing steps such as histogram stretching, division of histogram modes, denoising techniques, and cumulative distribution functions. Those processing steps are often adopted by existing image enhancement methods to boost image quality. Moreover, existing fundus image enhancement methods are focused mainly on central contrast enhancement rather than brightness. However, both brightness and contrast factors have a vital role in improving DR-based CAD system efficiency in segmentation and classification of important NPDR features. The only limitation of the proposed enhancement strategy was not to directly implement DR-based CAD systems due to further investigating insights into the proposed art using a diverse range of high-quality fundus images for advancement rather than applications.