LightsOut：基于扩散模型的图像外绘技术，实现增强型镜头光晕消除

摘要

镜头光晕显著降低图像质量，影响物体检测和自动驾驶等关键计算机视觉任务。现有的单张图像光晕去除（SIFR）方法在画面外光源不完整或缺失时表现欠佳。我们提出了LightsOut，一种基于扩散模型的画面外补全框架，专门用于通过重建画面外光源来增强SIFR效果。该方法结合了多任务回归模块和LoRA微调的扩散模型，确保生成真实且物理一致的补全结果。大量实验表明，LightsOut在各种挑战性场景下持续提升现有SIFR方法的性能，无需额外重新训练，可作为通用的即插即用预处理解决方案。项目页面：https://ray-1026.github.io/lightsout/

English

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. Project page: https://ray-1026.github.io/lightsout/

LightsOut：基于扩散模型的图像外绘技术，实现增强型镜头光晕消除

LightsOut: Diffusion-based Outpainting for Enhanced Lens Flare Removal

摘要

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