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Low-light imaging is challenging since images may appear to be dark andnoised due to low signal-to-noise ratio, complex image content, and the varietyin shooting scenes in extreme low-light condition. Many methods have beenproposed to enhance the imaging quality under extreme low-light conditions, butit remains difficult to obtain satisfactory results, especially when theyattempt to retain high dynamic range (HDR). In this paper, we propose a novelmethod of multi-granulation cooperative networks (MCN) with bidirectionalinformation flow to enhance extreme low-light images, and design anillumination map estimation function (IMEF) to preserve high dynamic range(HDR). To facilitate this research, we also contribute to create a newbenchmark dataset of real-world Dark High Dynamic Range (DHDR) images toevaluate the performance of high dynamic preservation in low light environment.Experimental results show that the proposed method outperforms thestate-of-the-art approaches in terms of both visual effects and quantitativeanalysis.

Extreme Low-Light Imaging with Multi-granulation Cooperative Networks