A Data Generation Method for Image Flare Removal Based on Similarity and Centrosymmetric Effect

Abstract

Image pairs in under-illuminated scenes along with the presence of complex light sources often result in strong flare artifacts in images, affecting both image quality and the performance of downstream visual applications. Removing lens flare and ghosts is a challenging issue, particularly in low-light environments. Existing methods for flare removal are mainly restricted by inadequate simulation and real-world capture, resulting in singular categories of scattered flares and unavailable reflected ghosts. Therefore, a comprehensive deterioration procedure is crucial for generating a dataset for flare removal. We propose a methodology based on spatial position relationships for generating data pairs with flare deterioration, which is supported by theoretical analysis and real-world evaluation. Our procedure is comprehensive and realizes the similarity of scattered flares and the symmetric effect of reflected ghosts. We also construct a real-shot pipeline that respectively processes the effects of scattering and reflective flares, aiming to directly generate data for end-to-end methods. Experimental results demonstrate that our methodology adds diversity to existing flare datasets and constructs a comprehensive mapping procedure for flare data pairs. Our method facilitates the data-driven model to achieve better restoration in flare images and proposes a better evaluation system based on real shots, thus promoting progress in the area of real flare removal.