Abstract
Single image deblurring is a typical ill-posed problem. Although a lot of effective algorithms have been proposed, there is a lack of blind evaluation metrics for the perceptual quality of deblurred images. In this paper, we introduce a new, low-cost and lightweight dataset, called Deblurred image Quality Assessment (DeblurQA). Next, we design an extendable model named DeBlurred Quality Assessment NETwork (DBQA-NET) based on multi-resolution deep feature aggregation, and train it by a two-stage training method of classification combined with quality prediction, along with a joint loss function of ranking and regression. Finally, we demonstrate the superiority of the method and show that it can assist the existing deblurring algorithms: in the hyperparameter selection experiment, it can find the best and worst results that match human perception; when applied to the training of deep-learning-based methods, it can significantly improve the abnormal results. The model, code and the dataset are available at https://github.com/weidelongdongqiang/deblurQA/tree/IQA.
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Li, C., Xu, C., Chen, C., Xu, C., Wei, Z. (2022). Blind Perceptual Quality Assessment for Single Image Motion Deblurring. In: Deng, W., et al. Biometric Recognition. CCBR 2022. Lecture Notes in Computer Science, vol 13628. Springer, Cham. https://doi.org/10.1007/978-3-031-20233-9_54
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