Image preprocessing involves improving image quality by removing irrelevant image data, making images suitable for various applications. Diabetes is a global condition that can cause noticeable microvascular complications in the retinal eye, such as diabetic retinopathy and macular edema, significant causes of worldwide vision loss. Retinal fundus images are commonly used in clinics for recognizing and evaluating diabetic retinopathy (DR). Raw medical images often contain irrelevant and undesired elements that can lead to inaccurate results. To eliminate these unwanted components, appropriate preprocessing techniques should be applied to enhance perception before specifically identifying infections. This study introduces an algorithm designed for the enhancement of fundus images by addressing noise removal and contrast enhancement. By combining various filters and employing Contrast Limited Adaptive Histogram Equalization (CLAHE), the algorithm aims to effectively address these issues in color fundus images. The evaluation of the proposed method’s effectiveness is carried out using diverse performance parameters such as Peak Signal to Noise Ratio (PSNR), Mean Absolute Error (MAE), Mean Square Error (MSE), and Structural Similarity Index Measure (SSIM). The method proposed showed improvement in PSNR when a combination of filters was used.