Senthil Anandhi A, Jaiganesh M
Research Scholar-ICE, Anna University, Chennai, India.
Department of CSE, New Horizon College of Engineering, Bangalore, India.
Sci Rep. 2025 Mar 25;15(1):10324. doi: 10.1038/s41598-025-94449-5.
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.
图像处理和恢复在计算机视觉中很重要，特别是对于那些因噪声、模糊和其他问题而受损的图像。传统方法在处理周期性噪声等问题时往往很困难，并且在恢复过程中不能有效地结合局部和全局数据。为了解决这些问题，我们提出了一种增强的图像恢复模型，该模型将Lewin架构与SwinIR相结合，使用先进的深度学习方法。这种方法结合了这些技术，实现了更好的恢复过程，改进了4.2%。使用PSNR和SSIM测量来检查模型的有效性，结果表明它可以在保持关键图像细节完整的同时降低噪声。与传统方法相比，我们的模型显示出更好的结果，为困难情况下的图像恢复创造了新的标准。测试结果表明，这种组合方法大大提高了在各种图像数据集上的修复性能，使其成为获得更清晰图像和降噪的强大解决方案。
