LAUSR

Image deblurring is an important problem encountered in many image restoration tasks. To remove the motion blur of images captured from dynamic scenes, various Convolutional Neural Networks (CNNs) based methods are developed to restore the latent sharp image via an end-to-end trainable. However, these CNNs-based methods cannot restore enough structure details as no significant information is provided by the blurry input only. In this work, we propose a reference-guided deep deblurring method by incorporating the high-quality reference image into the deep network for better deblurring effect. Concretely, the correlation between the blurry input and the reference image is computed in the high-level feature space, and further represented by the similarity maps. To pick up the most relevant similarity maps to the input, the selective attention module is designed, and the selected similarity maps are decoded to reconstruct the deblurred sharp image. Extensive experimental results on two public datasets demonstrate that the proposed method achieves superior performance to existing methods quantitatively and qualitatively. More ablation experiments also validate that the favourable deblurred results can still be obtained even if the reference image is not similar with the input image.