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Aggregated Dilated Convolutions for Efficient Motion Deblurring

Blind motion deblurring is a challenging computer vision problem due to its ill-posed nature. Moreover, this problem becomes more challenging when blur varies spatially over the entire image. Meanwhile, the efficiency of blind motion deblurring algorithms is also very important. In this work, we propose a convolutional neural network (CNN) to solve this problem in an end-to-end manner. The network architecture is based on conditional Generative Adversarial Network (cGAN) integrated with stacked encoder-decoder architecture and skip connections. We use a residual block which aggregates dilated convolutions to construct our network. With experiments on the benchmark dataset, we demonstrate that the proposed method achieves competitive performance compared with the state-of-the-art blind motion deblurring methods, and in the meanwhile reduces time and memory overhead.