Joint super-resolution and deblurring for low-resolution text image using two-branch neural network

The challenge of image reconstruction from very-low-resolution images is made exceedingly difficult by multiple degradation factors in practical applications. Traditional methods do not consider the interactions between these degradation factors, so the results are often insufficient. To reconstruct low-resolution blurry images, both super-resolution and deblurring processes must be applied. In this paper, we propose a joint super-resolution and a deblurring model with integrated processing of the degradation factors to obtain better image quality. The joint model includes two branches, a super-resolution module and deblurring module, and both of them share the same feature extraction module. The super-resolution module consists of multiple layers of residual blocks. The deblurring module supports the robustness of the super-resolution module through feature feed-back in the learning process, by introducing an image blurring feature description into the feature representation. To create modules with high magnification, the base two-branch model is also used in two stages with scale recursion. A second-stage deblurring module receives the output of the first-stage super-resolution module and improves the deblurring capability when the image is further magnified. The modules enhance each other, significantly improve the quality of very-low-resolution text images, and maintain a low model complexity. A step-by-step training strategy is applied to reduce second-stage training difficulty. Experiments show that our approach significantly outperforms state-of-the-art methods in terms of image quality and optical character recognition accuracy, and with a lower computational cost.