Ghost Imaging in the Dark: A Multi-Illumination Estimation Network for Low-Light Image Enhancement

Abstract

It is well known that the diverse causes of low-light images challenge the adaptability of enhancement algorithms in uncertain environments. Most deep learning-based algorithms only learn single illuminance estimation or mapping relationship, which inhibit the generalization ability of the model. To address this, we propose a novel multi-illumination estimation framework based on ghost imaging theory, dubbed Ghillie. Specifically, we consider low-light enhancement as a re-imaging process for objects in dark scenes. First, the light modulation network (LMN) is designed to modulate a series of estimated lights following a normal light distribution. These lights “illuminate” the low-light image and the enhanced illuminance image can be reconstructed by a differential ghost imaging algorithm. Then, a gradient-guided denoising network (GDN) is constructed to eliminate noise and enhance details. Finally, we employ the color adaption network (CAN) to restore the color degradation. Additionally, a novel mean structural similarity loss (AM-SSIM) is proposed to guide the model to address the uneven image illumination. The qualitative and quantitative experimental results show that our enhanced methods outperform state-of-the-art methods on the vast majority of publicly available datasets. Our code is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zzj-dyj/Ghillie</uri>.