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Most existing Low-Light Image Enhancement (LLIE) methods are primarilydesigned to improve brightness in dark regions, which suffer from severedegradation in nighttime images. However, these methods have limitedexploration in another major visibility damage, the glow effects in real nightscenes. Glow effects are inevitable in the presence of artificial light sourcesand cause further diffused blurring when directly enhanced. To settle thisissue, we innovatively consider the glow suppression task as learning physicalglow generation via multiple scattering estimation according to the AtmosphericPoint Spread Function (APSF). In response to the challenges posed by unevenglow intensity and varying source shapes, an APSF-based Nighttime Imaging Modelwith Near-field Light Sources (NIM-NLS) is specifically derived to design ascalable Light-aware Blind Deconvolution Network (LBDN). The glow-suppressedresult is then brightened via a Retinex-based Enhancement Module (REM).Remarkably, the proposed glow suppression method is based on zero-shot learningand does not rely on any paired or unpaired training data. Empiricalevaluations demonstrate the effectiveness of the proposed method in both glowsuppression and low-light enhancement tasks.

From Generation to Suppression: Towards Effective Irregular Glow Removal for Nighttime Visibility Enhancement