  Night imaging with modern smartphone cameras is troublesome due to low photon
count and unavoidable noise in the imaging system. Directly adjusting exposure
time and ISO ratings cannot obtain sharp and noise-free images at the same time
in low-light conditions. Though many methods have been proposed to enhance
noisy or blurry night images, their performances on real-world night photos are
still unsatisfactory due to two main reasons: 1) Limited information in a
single image and 2) Domain gap between synthetic training images and real-world
photos (e.g., differences in blur area and resolution). To exploit the
information from successive long- and short-exposure images, we propose a
learning-based pipeline to fuse them. A D2HNet framework is developed to
recover a high-quality image by deblurring and enhancing a long-exposure image
under the guidance of a short-exposure image. To shrink the domain gap, we
leverage a two-phase DeblurNet-EnhanceNet architecture, which performs accurate
blur removal on a fixed low resolution so that it is able to handle large
ranges of blur in different resolution inputs. In addition, we synthesize a
D2-Dataset from HD videos and experiment on it. The results on the validation
set and real photos demonstrate our methods achieve better visual quality and
state-of-the-art quantitative scores. The D2HNet codes and D2-Dataset can be
found at https://github.com/zhaoyuzhi/D2HNet.
