Quality-aware blind image motion deblurring

Abstract

Recent mapping-based motion deblurring methods lack the regularization of prior knowledge, resulting in an over-reliance on the training data and limited generalization ability. As deblurring aims to improve image quality, we quantitatively analyze and further discover the strong correlation between image quality and sharpness. Motivated by the above facts and notable accomplishments of recent no-reference image quality assessment (NR-IQA), we present a novel framework that incorporates quality knowledge into mapping-based deblurring models. Specifically, we extract quality-aware features from NR-IQA models as prior knowledge, and subsequently propose a prediction-based strategy and an encoder-reuse strategy to integrate knowledge into the encoder and decoder, respectively. After training, the model can simultaneously deblur images and predict quality features, indicating that it has grasped the knowledge and validating the effectiveness of the proposed embedding strategies. Extensive experimental results show that embedding quality knowledge consistently improves model performance and the model achieves state-of-the-art intra/cross-dataset results. Code and pre-trained models are available at https://github.com/esnthere/QAMD.