Deep architecture for super-resolution and deblurring of text images

Image deblurring and super resolution attempts to restore images that have been degraded. We propose a joint technique for super resolution and deblurring to solve the problem of blur and low resolution in text images. This joint technique is based on the use of a Deep Convolutional Neural Network (Deep CNN). Deep CNN has achieved promising 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 a model that jointly performs super-resolution and deblurring of low-resolution blurry text images based on DCSCN. Our model uses subsampled blurry images in the input and original sharp images as ground truth. The proposed architecture consists of a higher number of filters in the input CNN layer to a better analysis of the text details. The experimental results have achieved state-of-the-art performance in the peak-signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), the information fidelity criterion (IFC) and Visual Information Fidelity (VIF) metrics. Thus, we confirm that DCSCN provides satisfactory results for enhancement tasks on low blurry images. The quantitative and qualitative evaluation on different datasets proves the high performance of our model to reconstruct high-resolution and sharp text images with PSNR= 20.406, SSIM= 0.877, VIF= 0.351, IFC= 2.868 for scale 4 compared to DCSCN with PSNR= 15.553, SSIM= 0.621, VIF= 0.166 IFC= 1.129. In addition, in terms of computational time, our proposed method gives competitive performance compared to state-of-the-art methods.