  Camera shake or target movement often leads to undesired blur effects in
videos captured by a hand-held camera. Despite significant efforts having been
devoted to video-deblur research, two major challenges remain: 1) how to model
the spatio-temporal characteristics across both the spatial domain (i.e., image
plane) and temporal domain (i.e., neighboring frames), and 2) how to restore
sharp image details w.r.t. the conventionally adopted metric of pixel-wise
errors. In this paper, to address the first challenge, we propose a DeBLuRring
Network (DBLRNet) for spatial-temporal learning by applying a modified 3D
convolution to both spatial and temporal domains. Our DBLRNet is able to
capture jointly spatial and temporal information encoded in neighboring frames,
which directly contributes to improved video deblur performance. To tackle the
second challenge, we leverage the developed DBLRNet as a generator in the GAN
(generative adversarial network) architecture, and employ a content loss in
addition to an adversarial loss for efficient adversarial training. The
developed network, which we name as DeBLuRring Generative Adversarial Network
(DBLRGAN), is tested on two standard benchmarks and achieves the
state-of-the-art performance.
