Most existing Low-Light Image Enhancement (LLIE) methods are primarily designed to improve brightness in dark regions, which suffer from severe degradation in nighttime images. However, these methods have limited exploration in another major visibility damage, the glow effects in real night scenes. Glow effects are inevitable in the presence of artificial light sources and cause further diffused blurring when directly enhanced. To settle this issue, we innovatively consider the glow suppression task as learning physical glow generation via multiple scattering estimation according to the Atmospheric Point Spread Function (APSF). In response to the challenges posed by uneven glow intensity and varying source shapes, an APSF-based Nighttime Imaging Model with Near-field Light Sources (NIM-NLS) is specifically derived to design a scalable Light-aware Blind Deconvolution Network (LBDN). The glow-suppressed result is then brightened via a Retinex-based Enhancement Module (REM). Remarkably, the proposed glow suppression method is based on zero-shot learning and does not rely on any paired or unpaired training data. Empirical evaluations demonstrate the effectiveness of the proposed method in both glow suppression and low-light enhancement tasks.

翻译：现有的大部分低光照图像增强方法主要针对暗区亮度提升设计，但夜间图像往往存在严重退化。然而，这些方法对夜间真实场景中另一类主要视觉损伤——光晕效应——的探索十分有限。在人工光源存在时，光晕效应不可避免，且直接增强会导致扩散模糊进一步加剧。为解决此问题，我们创新性地将光晕抑制任务视为基于大气点扩散函数（APSF）的多重散射估计来学习物理光晕生成过程。针对光晕强度不均匀及光源形状多变带来的挑战，本文专门推导了一种基于APSF的含近场光源夜间成像模型（NIM-NLS），并据此设计了可扩展的光感知盲反卷积网络（LBDN）。光晕抑制后的结果通过基于Retinex的增强模块（REM）进行亮度提升。值得注意的是，所提出的光晕抑制方法基于零样本学习，无需依赖任何配对或非配对训练数据。实验评估表明，该方法在光晕抑制与低光照增强任务中均具有有效性。