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

TL;DR

This paper introduces SDT-DCSCN, a deep learning model that simultaneously enhances resolution and sharpness of blurry text images, outperforming existing methods in quality and efficiency.

Contribution

The paper presents a novel joint super-resolution and deblurring approach for text images using an extended DCSCN architecture with improved filter analysis.

Findings

High-quality reconstruction of sharp, high-resolution text images.

Competitive computational performance compared to state-of-the-art methods.

Effective handling of blurry, low-resolution text images.

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.