SDT-DCSCN for Simultaneous Super-Resolution and Deblurring of Text Images
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2022
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| _version_ | 1866915719017398272 |
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| author | Neji, Hala Halima, Mohamed Ben Nogueras-Iso, Javier Hamdani, Tarek. M. Qahtani, Abdulrahman M. Almutiry, Omar Dhahri, Habib Alimi, Adel M. |
| author_facet | Neji, Hala Halima, Mohamed Ben Nogueras-Iso, Javier Hamdani, Tarek. M. Qahtani, Abdulrahman M. Almutiry, Omar Dhahri, Habib Alimi, Adel M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2201_05865 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | SDT-DCSCN for Simultaneous Super-Resolution and Deblurring of Text Images Neji, Hala Halima, Mohamed Ben Nogueras-Iso, Javier Hamdani, Tarek. M. Qahtani, Abdulrahman M. Almutiry, Omar Dhahri, Habib Alimi, Adel M. Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | SDT-DCSCN for Simultaneous Super-Resolution and Deblurring of Text Images |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2201.05865 |