This study seeks to improve image quality through denoising using Vision Transformer (ViT) models, addressing a common problem in image processing that affects medical imaging and surveillance applications. The technique focuses on helping the model distinguish between noise and useful features, which improves image clarity. This is performed by fine-tuning the attention processes within ViTs, allowing the model to recognize connections between different sections of a picture. In addition, this model provides novel noise reduction architectures that blend traditional image processing techniques with cutting-edge deep learning methods. A series of investigations on well-known benchmark datasets indicated that this ViT-based denoising method performs admirably, highlighting its potential as an important tool for improving image quality.