LightsOut

Lens flare significantly degrades image quality, impacting critical computer vision tasks like object detection and autonomous driving. Recent Single Image Flare Removal (SIFR) methods perform poorly when off-frame light sources are incomplete or absent. We propose LightsOut, a diffusion-based outpainting framework tailored to enhance SIFR by reconstructing off-frame light sources. Our method leverages a multitask regression module and LoRA fine-tuned diffusion model to ensure realistic and physically consistent outpainting results. Comprehensive experiments demonstrate LightsOut consistently boosts the performance of existing SIFR methods across challenging scenarios without additional retraining, serving as a universally applicable plug-and-play preprocessing solution.

(a) Light source prediction and conditioning: We introduce a multitask regression module to accurately predict off-frame or incomplete light source parameters (positions, radii, and confidences). These predicted parameters guide a rendering function to generate the corresponding light source mask. (b) Light source outpainting: Leveraging a LoRA fine-tuned diffusion-based inpainting model with light source conditioning, our approach accurately outpaints both missing off-frame light sources and associated flare artifacts, producing visually coherent and realistic results. (c) SIFR boosting: Our generated outpainted images serve as enhanced inputs to existing SIFR methods, significantly improving their performance on previously challenging scenarios with incomplete light source information. The proposed pipeline thus effectively operates as a plug-and-play module to boost existing flare removal models.