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<p>Image processing and restoration are important in computer vision, particularly for images that are damaged by noise, blur, and other issues. Traditional methods often have a hard time with problems like periodic noise and do not effectively combine local and global data during the restoration process. To address these problems, we suggest an enhanced image restoration model that merges Lewin architecture with SwinIR, using advanced deep learning methods. This approach combines these techniques for a better restoration process improved by 4.2%. The model’s effectiveness is checked using PSNR and SSIM measurements, showing that it can lower noise while keeping key image details intact. When compared to traditional methods, our model shows better results, creating a new standard in image restoration for difficult situations. Test results show that this combined approach greatly enhances fixing performance across various image datasets, making it a strong solution for clearer images and noise reduction.</p>

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An enhanced image restoration using deep learning and transformer based contextual optimization algorithm

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月25日

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  • A. Senthil Anandhi,
  • M. Jaiganesh

摘要

Image processing and restoration are important in computer vision, particularly for images that are damaged by noise, blur, and other issues. Traditional methods often have a hard time with problems like periodic noise and do not effectively combine local and global data during the restoration process. To address these problems, we suggest an enhanced image restoration model that merges Lewin architecture with SwinIR, using advanced deep learning methods. This approach combines these techniques for a better restoration process improved by 4.2%. The model’s effectiveness is checked using PSNR and SSIM measurements, showing that it can lower noise while keeping key image details intact. When compared to traditional methods, our model shows better results, creating a new standard in image restoration for difficult situations. Test results show that this combined approach greatly enhances fixing performance across various image datasets, making it a strong solution for clearer images and noise reduction.