Multi-stage feature-fusion dense network for motion deblurring

Although convolutional neural networks (CNNs) have recently shown considerable progress in motion deblur-ring, most existing methods that adopt multi-scale input schemes are still challenging in accurately restoring the heavily-blurred regions in blurry images. Several recent methods aim to further improve the deblurring effect using larger and more complex models, but these methods inevitably result in huge computing costs. To address the performance-complexity trade-off, we propose a multi-stage feature-fusion dense network (MFFDNet) for motion deblurring. Each sub-network of our MFFDNet has the similar structure and the same scale of input. Meanwhile, we propose a feature-fusion dense connection structure to reuse the extracted features, thereby improving the deblurring effect. Moreover, instead of using the multi-scale loss function, we only calculate the loss function at the output of the last stage since the input scale of our sub-network is invariant. Experimental results show that MFFDNet maintains a relatively small computing cost while outperforming state-of-the-art motion-deblurring methods. The source code is publicly available at: https://github.com/CaiGuoHS/MFFDNet_ release.