Beyond Camera Motion Removing: How to Handle Outliers in Deblurring
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Performing camera motion deblurring is an important low-level vision task for achieving better imaging quality. When a scene has outliers such as saturated pixels and salt-and pepper noise, the image becomes more difficult to restore. In this paper, we propose an edge-aware scalerecurrent network (EASRN) to conduct camera motion deblurring. EASRN has a separate deblurring module that removes blur at multiple scales and an upsampling module that fuses different input scales. We propose a salient edge detection network to supervise the training process and solve the outlier problem by proposing a novel method of dataset generation. Light streaks are printed on the sharp image to simulate the cutoff effect from saturation. We evaluate our method on the standard deblurring datasets. Both objective evaluation indexes and subjective visualization show that our method results in better deblurring quality than the other state-of-the-art approaches.
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