Stack-based Scale-recurrent Network for Face Image Deblurring

摘要

In recent years, the image deblurring task has attracted more and more researchers’ attention. Many researchers are devoted to eliminating motion blur by using a “coarse-to-fine” architecture. This architecture can effectively eliminate motion blur caused by a simple relative displacement. But when using the architecture directly for the face image deblurring task, there would exist some problems. For example, complex network structures make the model difficult to be trained, and a large number of parameters need to be calculated would result in expensive runtime. Since details of images can’t be completely restored, the quality of images will deteriorate, and deblurring visual effect is poor, so in order to solve these issues, combining the “coarse-to-fine” architecture with the “stacked” architecture, we propose a new network architecture-“Stack-based Scale-recurrent Network” for the face image deblurring task. The ConvLSTM network is employed to build a model. We achieves the purpose of improving the visual clarity and the quality of restored face images. And we use the improved encoder-decoder to enhance network performance. Compared with the other deblurring methods, our method can restore clear face images with higher-quality and clearer vision.