Single image deblurring with cross-layer feature fusion and consecutive attention.

Single image deblurring aims to restore the single blurry image to its sharp counterpart and remains an active topic of enduring interest. Recently, deep Convolutional Neural Network (CNN) based methods have achieved promising performance. However, two primary limitations mainly exist on those CNNs-based image deblurring methods: most of them simply focus on increasing the complexity of the network, and rarely make full use of features extracted by encoder. Meanwhile, most of the methods perform the deblurred image reconstruction immediately after the decoder, and the roles of the decoded features are always underestimated. To address these issues, we propose a single image deblurring method, in which two modules to fuse multiple features learned in encoder (the Cross-layer Feature Fusion (CFF) module) and manipulate the features after decoder (the Consecutive Attention Module (CAM)) are specially designed, respectively. The CFF module is to concatenate different layers of features from encoder to enhance rich structural information to decoder, and the CAM module is able to generate more important and correlated textures to the reconstructed sharp image. Besides, the ranking content loss is employed to further restore more realistic details in the deblurred images. Comprehensive experiments demonstrate that our proposed method can generate less blur and more textures in deblurred image on both synthetic datasets and real-world image examples. • The cross-layer feature fusion module concatenates multi-layer features in encoder to reconstruct image. • The consecutive attention module after decoder enables to restore more correlated textures in deblurred image. • The ranking content loss is introduced to guide the generator to deblur. [ABSTRACT FROM AUTHOR]