# A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy
## 1 Introduction
Moreover, a number of HE variants in the literature have been found to be promising in the enhancement of a dynamic range of modalities. Each variant has its own merits and demerits [11]. An adaptive histogram equalization (AHE) was then presented to overcome the shortcomings of HE, i.e., the over-enhancement of artifacts and intensity saturation. These adaptive approaches applied the histogram on pixel levels to boost the regional contrast of the image. The AHE produces good results, but it has a slow processing speed and great influence on the amplification of noise [12]. To address these problems, a contrast-limited adaptive histogram equalization (CLAHE) procedure was released in Reference [13], which has been observed to be remarkable in the removal of high rations of noise, artifacts, and over-enhancement of bright regions from the images. Due to its popularity, various CLAHE extensions have been proposed by authors [14; 15]. The key problem of CLAHE is not employing the complete range of histograms for image enhancement, as well as suppressing some fine details. In addition to the usage of a non-holistic histogram range, the performance of CLAHE also highly depends on the selection of contextual regions, tile size, histogram bins, adequate distribution of tiles, and the parameters of an enhanced image itself. This seems to be computationally expensive, and obtained CLAHE results become worse than an original image [16]. Later, an exposure-based sub-image histogram equalization (ESIHE) was created to come up with a solution to regulate the enhancement rate, maintain brightness, and lessen noise level [12]. [PERSON] et al. [17] considered a bin underflow-bin overflow histogram equalization (BUBOHE) scheme to solve the HE problems. Their scheme remained successful in image enhancement, but it failed to address the problem of decreased local contrast. Another study based on adaptive gamma correction (AGC), proposed by [PERSON] et al. [18], was used to enhance the quality of dimmed light images. They employed weight distribution on pixel level intensity to improve image contrast. Their approach presented significant contrast levels, but over-enhanced the bright area of the image. [PERSON] et al. [19] used an adaptive histogram equalization on a green plane of the RGB image to improve the contrast. Initially, a green channel was extracted because of its rich color and content details. Then, background subtraction was performed using a median filtered grayscale image, and finally, an AHE was applied to normalize the brightness and intensity. Similarly, a shaded correction method [20] was employed to subtract the background details and to improve the contrast of color images. However, these mechanisms have produced spot noise and increased the luminance of the brighter features, i.e., the optic disc and cup. [PERSON] et al. [21] considered a fuzzy set with a derivation equation to partition the histogram into multimodes for image enhancement. They mainly applied a fuzzy set on each mode of histogram to get an optimal crossover point of the S-function with the highest probability of the fuzzy index, which was then utilized for enhancement. Their scheme was found to be successful in image enhancement, but still, the problem of over-enhancement of noise was not addressed. Figure 3 demonstrates the application of an RGB component in the extraction of diagnostic parameters for DR assessment. [PERSON] et al. [22] computed a fuzzy histogram of the green channel and then partitioned it into subhistograms. Finally, contrast enhancement was achieved using the equalization of an intensity level of the subhistograms with minimization of the difference of mean brightness with respect to its corresponded original  
Figure 2: A sample of fundus images where (**a**) indicates less illumination ratio of an image; and (**b**) is a low-contrast and noisy image [8]. image. However, the problem of a high rate of luminosity in the brighter region of the image remained unsolved.