Abstract
Lens flare is a common artifact in photographs occurring when the camera is pointed at a strong light source. It is caused by either multiple reflections within the lens or scattering due to scratches or dust on the lens, and may appear in a wide variety of patterns: halos, streaks, color bleeding, haze, etc. The diversity in its appearance makes flare removal extremely challenging. Existing software methods make strong assumptions about the artifacts' geometry or brightness, and thus only handle a small subset of flares. We take a principled approach to explicitly model the optical causes of flare, which leads to a novel semi-synthetic pipeline for generating flare-corrupted images from both empirical and wave-optics-simulated lens flares. Using the semi-synthetic data generated by this pipeline, we build a neural network to remove lens flare. Experiments show that our model generalizes well to real lens flares captured by different devices, and outperforms start-of-the-art methods by 3dB in PSNR.
Abstract (translated)
URL
https://arxiv.org/abs/2011.12485
