SASFNet: Soft-edge awareness and spatial-attention feedback deep network for blind image deblurring

Abstract

When a camera is used to capture moving objects in natural scenes, the obtained images will be degraded to varying degrees due to camera shaking and object displacement, which is called motion blurring. Moreover, the complexity of natural scenes makes the image motion deblurring more challenging. Now, there are two crucial problems in Deep Learning-based methods for blind motion deblurring: (1) how to restore sharp images with fine textures, and (2) how to improve the generalization of the model. In this paper, we propose Soft-edge Awareness and Spatial-attention Feedback deep Network (SASFNet) to restore sharp images. First, we restore images with fine textures using a soft-edge assist mechanism. This mechanism uses the soft edge extraction network to map the fine edge information from the blurred image to assist the model to restore the high-quality clear image. Second, for the generalization of the model, we propose feedback mechanism with attention. Similar to course learning, feedback mechanism imitates the human learning process, learning from easy to difficult to restore sharp images, which not only refines the restored features, but also brings better generalization. To evaluate the model, we use the GoPro dataset for model training and validity testing, and the Realblur dataset to test the generalization of the model. Experiments show that our proposed SASFNet can not only restore sharp images that are more in line with human perception, but also has good generalization.