Extreme Low-Light Imaging with Multi-granulation Cooperative Networks

• URL: http://arxiv.org/abs/2005.08001v1
• Date: Sat, 16 May 2020 14:26:06 GMT
• Title: Extreme Low-Light Imaging with Multi-granulation Cooperative Networks
• Authors: Keqi Wang, Peng Gao, Steven Hoi, Qian Guo, Yuhua Qian
• Abstract summary: 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 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.
• Score: 18.438827277749525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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