A Novel Hybrid Algorithm for Effective Image Restoration

Image restoration ispivotal 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 shown promise in addressing these challenges, yet each has limitations. Traditional algorithms often struggle with complex distortions, while deeplearning 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.Our hybrid approach begins with a conventional preprocessing method to mitigate noise and reduce artifacts, followed by a deep learning-based refinement process to enhance image quality further and preserve critical details. This dual-step process improves the restoration performance andreduces the computational burden typically associated with 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 (U-former) architecture. Unlike existing methods, our approach combines the computational efficiency of classical techniques with the robustness and precision of deep learning. Compared to state-of-the-art methods, comprehensive evaluations on benchmark datasets demonstrate significant improvements in restoration quality (PSNR/SSIM) and computational efficiency. This research contributes a new perspective on hybrid methodologies, bridging the gap between traditional and modern approaches in image restoration