ArticleScientific reports2025
An enhanced image restoration using deep learning and transformer based contextual optimization algorithm.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A novel exponential tension field for salt-and-pepper image denoising.Scientific reports · 2026Article
- Deep Learning for Brain MRI Artifact Correction: Current Challenges and Future Directions.Bioengineering (Basel, Switzerland) · 2026Review
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2 authors.
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Abstract
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.
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