Blurred image recognition using domain adaptation

Image blurring significantly degrades the image recognition performance. In this paper, we novelly address the blurred image recognition task from the perspective of domain adaptation (DA). The scenario is that, the training set (source domain) only comprises of the labelled clear images, and the test set (target domain) is composed of the unlabelled blurred images. DA is executed to eliminate the domain shift by subspace alignment. In this way, the clear and blurred image domains are pushed closer in the feature space. The supervised LMDR metric learning method is employed by us to construct the source domain subspace for further performance enhancement, compared to the unsupervised one (i.e., PCA). The experimental results on two datasets demonstrate that, the proposed DA-based blurred image recognition mechanism can significantly enhance the performance of different kinds of visual descriptors, especially when the blurring degree is strong.