<![CDATA[Image restoration plays a pivotal role in various applications, from medical imaging to satellite photography, by enhancing the quality of images degraded by noise, blur, or other distortions. Traditional methods and deep learning techniques have both shown promise in addressing these challenges, yet each has its limitations. Traditional algorithms often struggle with complex distortions, while deep learning models demand extensive computational resources and large datasets. To harness the strengths of both approaches, we propose a novel hybrid algorithm that integrates traditional image restoration techniques with advanced deep learning models. This paper presents a novel hybrid algorithm for image restoration, integrating traditional Wiener filtering with a state-of-the-art U-shaped transformer (Uformer) architecture. Unlike existing methods, our approach combines the computational efficiency of classical techniques with the robustness and precision of deep learning. Comprehensive evaluations on benchmark datasets demonstrate significant improvements in restoration quality (PSNR/SSIM) and computational efficiency compared to state-of-the-art methods. This research contributes a new perspective on hybrid methodologies, bridging the gap between traditional and modern approaches in image restoration.]]>
