Granular Computing (GC) is a novel computing paradigm that deals with complex and massive amounts of data. It also presents a one-of-a-kind, useful approach to improving the quality of images, extracting the details and eliminating noise or unwanted artifacts in the image. This paper introduces a novel method of image enhancement based on the use of granular computing and an enhanced support vector machine (SVM) classifier. The suggested approach splits images into segments in the first phase with the help of granular computing, which aids in the process of extracting significant features of the image. This is followed by a better SVM classifier that then classifies the features and improves the image. Two metrics are used to measure the performance of the proposed method, namely peak signal-to-noise ratio (PSNR) and mean square error (MSE). The obtained experimental results indicate that the proposed method is superior to other current state-of-the-art image enhancement methods in terms of PSNR and MSE. The research also includes confusion matrix results in order to show the accuracy of the suggested approach. The suggested technique may be applied to many image improvement tasks such as medical image processing, remote sensing, and other image improvement necessities.