This research addresses the limitations of existing Single Image Super Resolution (SISR) methods that typically target
specific kinds of image deterioration, such as noised images or blurred images. These targeted approaches are ineffective in
real-world scenarios where images suffer from multiple degradation types simultaneously. The proposed adaptive deep learning
framework is designed to enhance the resolution of images affected by various degradation types, including noising, blurring,
and compression artifacts. The proposed framework involves developing a multi-degradation modeling approach, adaptive
feature learning mechanisms, tailored loss functions, and comprehensive datasets for effective training and evaluation. This
unified solution aims to advance the state of the art in SISR by robustly handling diverse degradation types, thus improving
image quality across a range of applications.