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

arXiv: https://arxiv.org/abs/2201.05865 · HTML: https://mlsanity.com/paper/2201.05865/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 46 references that are in the map.
- First posted on arXiv 15 Jan 2022.

## Builds on (most cited first, 5 of 5)

- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network](https://mlsanity.com/paper/1707.05425/) (2017)
- [Gated Fusion Network for Joint Image Deblurring and Super-Resolution](https://mlsanity.com/paper/1807.10806/) (2018)
- [Deep Laplacian Pyramid Network for Text Images Super-Resolution](https://mlsanity.com/paper/1811.10449/) (2018)

## Built on by (most cited first, 1 of 1)

- [FP-THD: Full page transcription of historical documents](https://mlsanity.com/paper/2601.17040/) (2026)

## Abstract (arXiv)

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.