  Low-light imaging is challenging since images may appear to be dark and
noised due to low signal-to-noise ratio, complex image content, and the variety
in shooting scenes in extreme low-light condition. Many methods have been
proposed to enhance the imaging quality under extreme low-light conditions, but
it remains difficult to obtain satisfactory results, especially when they
attempt to retain high dynamic range (HDR). In this paper, we propose a novel
method of multi-granulation cooperative networks (MCN) with bidirectional
information flow to enhance extreme low-light images, and design an
illumination map estimation function (IMEF) to preserve high dynamic range
(HDR). To facilitate this research, we also contribute to create a new
benchmark dataset of real-world Dark High Dynamic Range (DHDR) images to
evaluate the performance of high dynamic preservation in low light environment.
Experimental results show that the proposed method outperforms the
state-of-the-art approaches in terms of both visual effects and quantitative
analysis.
