Image Restoration with Noise Diffusion: Recovering Lost Clarity and Details
Harsh Yadav, Dilip Singh Sisodia · 2024
Image enhancement is a crucial task in various domains, including photography, medical imaging, and surveillance. However, existing methods often struggle to effectively address both noise reduction and resolution enhancement simultaneously. This paper proposes a novel approach that merges a custom-built diffusion model with the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) to achieve comprehensive image enhancement. The diffusion model is employed for efficient noise removal, while ESRGAN is utilized for high-quality super-resolution. By integrating these two powerful techniques, we create a synergistic effect that amplifies the benefits of each model, leading to superior image quality. The merged model is finetuned on a diverse dataset to ensure optimal performance across various scenarios. Extensive experiments demonstrate the effectiveness of our approach in enhancing image quality, outperforming in-dividual models in both noise reduction and resolution enhancement tasks. The proposed method offers versatility and can be deployed in real-time applications, which makes it a valuable tool for practitioners and researchers in the field of image processing.