Abstract
We propose an end-to-end rotational motion deblurring method based on conditional generation adversarial networks. The proposed method calculates the blur path value of each pixel on the rotational motion blurred image to provide a priori information of its blur degree, and then connects it to the blurred image as the input of the network. In addition, a rotational motion blurred image dataset is produced, which contains different degrees of rotational motion blurred images, as an evaluation dataset for the method to the effect of rotational motion deblurring. Experiments show that the proposed method is superior to existing end-to-end deblurring methods in both qualitative and quantitative analysis when dealing with different degrees of rotational motion blur.
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