SDT-DCSCN for Simultaneous Super-Resolution and Deblurring of Text Images
This work addresses the specific problem of improving text image quality for applications like document analysis, but it is incremental as it adapts an existing architecture to a new task.
The authors tackled the problem of simultaneously enhancing low-resolution blurry text images through super-resolution and deblurring, achieving high performance in reconstructing sharp, high-resolution text images with competitive computational time compared to state-of-the-art methods.
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.