MRI Image Enhancement Using UNet Segmentation Based on AHE and Unsharp Mask

MRI image enhancement is an important and useful field for diagnosis, helping physicians identify diseases and determine the appropriate treatment. However, it is a delicate process, as the essential image information must be preserved unchanged. In other words, enhancement should not alter the basic image details but rather improve the quality and clarity of the MRI image. The most significant challenges in MRI image enhancement are preserving the essential image information and balancing noise removal with this information preservation. This research explores MRI image enhancement using the U-Net segmentation technique based on AHE and unsharp mask. The results were good compared to previous studies that used the same data: AG = 5.32, CEM = 0.76, SSIM = 0.83, PIQE = 57.94. This clearly demonstrates the success of the method in improving the image.