Reflective Flare Removal Using Image Bursts

When the camera is facing the light source, light scattering and reflections within the lens system may produce both scattering and reflective flares in the captured image. While scattering flares are typically radial and have been largely addressed by existing flare removal methods via modeling in the image spatial domain, reflective flares (which appear as bright spots or ghostings of the light source) are more challenging to remove as they may have similar intensity values and shapes to the light source in the camera-finished images. We observe that in an image burst, reflective flares tend to present larger spatial-temporal disturbances than the light source, as their disturbances tend to be amplified internally by the lens system during the camera shake. This phenomenon can be used to help separate reflective flares from light sources for detection. Inspired by this, we propose a novel neural approach for removing reflective flares based on image bursts. We first formulate an image formation model to model the "moving" reflective flares in a burst, which produces realistic data for training deep models. We then propose a novel neural network with a novel reflective flare localization (REFO) module for locating the reflective flares, a novel guided burst-feature alignment (GBFA) module for handling the spatial and color misalignments caused by camera shakes, and an adaptive fusion (ADFU) module for removing reflective flares. Extensive experiments show that our approach outperforms state-of-the-art methods and generalizes well on image bursts captured in the wild.