Papers › SDT-DCSCN for Simultaneous Super-Resolution and Deblurring of Text Images
SDT-DCSCN for Simultaneous Super-Resolution and Deblurring of Text Images
Hala Neji, Mohamed Ben Halima, Javier Nogueras-Iso, Tarek. M. Hamdani, Abdulrahman M. Qahtani, Omar Almutiry, Habib Dhahri, Adel M. ALIMI
Deep convolutional neural networks (Deep CNN) have achieved hopeful performance for single image super-resolution. In particular, the Deep CNN skip Connection and Network in Network (DCSCN) architecture has been successfully applied to natural images super-resolution. In this work we propose an approach called SDT-DCSCN that jointly performs super-resolution and deblurring of low-resolution blurry text images based on DCSCN. Our approach uses subsampled blurry images in the input and original sharp images as ground truth. The used architecture is consists of a higher number of filters in the input CNN layer to a better analysis of the text details. The quantitative and qualitative evaluation on different datasets prove the high performance of our model to reconstruct high-resolution and sharp text images. In addition, in terms of computational time, our proposed method gives competitive performance compared to state of the art methods.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Super-Resolution | hradis et al dataset | super-resolution | Average PSNR | 20.406 | #1 of 1 | Archive leaderboard | report |
| Super-Resolution | hradis et al dataset | super-resolution | SSIM | 0.877 | #1 of 1 | Archive leaderboard | report |
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