  Under low-light environment, handheld photography suffers from severe camera
shake under long exposure settings. Although existing deblurring algorithms
have shown promising performance on well-exposed blurry images, they still
cannot cope with low-light snapshots. Sophisticated noise and saturation
regions are two dominating challenges in practical low-light deblurring. In
this work, we propose a novel non-blind deblurring method dubbed image and
feature space Wiener deconvolution network (INFWIDE) to tackle these problems
systematically. In terms of algorithm design, INFWIDE proposes a two-branch
architecture, which explicitly removes noise and hallucinates saturated regions
in the image space and suppresses ringing artifacts in the feature space, and
integrates the two complementary outputs with a subtle multi-scale fusion
network for high quality night photograph deblurring. For effective network
training, we design a set of loss functions integrating a forward imaging model
and backward reconstruction to form a close-loop regularization to secure good
convergence of the deep neural network. Further, to optimize INFWIDE's
applicability in real low-light conditions, a physical-process-based low-light
noise model is employed to synthesize realistic noisy night photographs for
model training. Taking advantage of the traditional Wiener deconvolution
algorithm's physically driven characteristics and arisen deep neural network's
representation ability, INFWIDE can recover fine details while suppressing the
unpleasant artifacts during deblurring. Extensive experiments on synthetic data
and real data demonstrate the superior performance of the proposed approach.
