A domain translation network with contrastive constraint for unpaired motion image deblurring

Most motion deblurring methods require a large amount of paired training data, which is nearly unreachable in practice. To overcome the limitation, a domain translation network with contrastive constraint for unpaired motion image deblurring is proposed. First, a domain translation network with two streams, a sharp domain translation stream and a blurred domain translation stream, to handle unpaired sharp and blurred images from the real world is presented. Second, a contrastive constraint loss in the deep intermediate level for the two streams to promote the network to produce deblurring results close to the real sharp image is proposed. Third, distinct loss functions for the two streams to preserve the edge and texture detail of the deblurring image is designed. Extensive experiments on several benchmark datasets demonstrate that the proposed network achieves better visual performance than the current state-of-the-art methods for unpaired motion image deblurring.