  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.
