SDT-DCSCN for Simultaneous Super-Resolution and Deblurring of Text Images

• URL: http://arxiv.org/abs/2201.05865v1
• Date: Sat, 15 Jan 2022 14:51:50 GMT
• Title: SDT-DCSCN for Simultaneous Super-Resolution and Deblurring of Text Images
• Authors: Hala Neji, Mohamed Ben Halima, Javier Nogueras-Iso, Tarek. M. Hamdani, Abdulrahman M. Qahtani, Omar Almutiry, Habib Dhahri, Adel M. Alimi
• Abstract summary: 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.
• Score: 3.5590597557917363
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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