  When taking photos in dim-light environments, due to the small amount of
light entering, the shot images are usually extremely dark, with a great deal
of noise, and the color cannot reflect real-world color. Under this condition,
the traditional methods used for single image denoising have always failed to
be effective. One common idea is to take multiple frames of the same scene to
enhance the signal-to-noise ratio. This paper proposes a recurrent fully
convolutional network (RFCN) to process burst photos taken under extremely
low-light conditions, and to obtain denoised images with improved brightness.
Our model maps raw burst images directly to sRGB outputs, either to produce a
best image or to generate a multi-frame denoised image sequence. This process
has proven to be capable of accomplishing the low-level task of denoising, as
well as the high-level task of color correction and enhancement, all of which
is end-to-end processing through our network. Our method has achieved better
results than state-of-the-art methods. In addition, we have applied the model
trained by one type of camera without fine-tuning on photos captured by
different cameras and have obtained similar end-to-end enhancements.
