arxiv_cv 95% Match Research Paper Computer Vision Researchers,ML Engineers,Robotics Engineers,Autonomous Driving Developers,Photographers 2 weeks ago

LightsOut: Diffusion-based Outpainting for Enhanced Lens Flare Removal

computer-vision › diffusion-models

📄 Abstract

Abstract: 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/

Authors (5)

Shr-Ruei Tsai

Wei-Cheng Chang

Jie-Ying Lee

Chih-Hai Su

Yu-Lun Liu

Submitted

October 17, 2025

arXiv Category

cs.CV

arXiv PDF Code

Key Contributions

LightsOut is a diffusion-based outpainting framework that enhances Single Image Flare Removal (SIFR) by reconstructing missing off-frame light sources. It uses LoRA fine-tuning and a multitask regression module to ensure realistic results, acting as a plug-and-play preprocessing solution that boosts existing SIFR methods without retraining.

Business Value

Improves the reliability of computer vision systems in challenging lighting conditions, crucial for safety-critical applications like autonomous driving and enhancing the quality of photographic content.

Paper Metadata

Innovation Type

Algorithmic Improvement / Framework Design

Deployment Feasibility

High. Designed as a plug-and-play preprocessing module, it can be easily integrated into existing pipelines.

Limitations Addressed

SIFR methods failing with incomplete or absent off-frame light sources,Degradation of critical computer vision tasks due to lens flare,Need for retraining SIFR models for different scenarios

Performance Gains

Consistently boosts the performance of existing SIFR methods across challenging scenarios.

View Code on GitHub

Technical Tags

lens flare removalimage enhancementdiffusion modelsoutpaintingLoRA fine-tuningmultitask regressionplug-and-playpreprocessingcomputer visionautonomous driving

Research Topics

Computer VisionImage ProcessingGenerative ModelsDiffusion ModelsImage Restoration

Methods & Architectures

Diffusion-based outpaintingLoRA fine-tuningMultitask regression moduleReconstruction of off-frame light sources Diffusion modelsLoRA (Low-Rank Adaptation)

Applications & Tasks

Photography Autonomous Driving Robotics Surveillance Image Editing Poor performance of SIFR with incomplete light sourcesDegradation of image quality by lens flareNeed for robust preprocessing Lens flare removalImage enhancementPreprocessing for computer vision tasks

Related Fields

Computer VisionImage ProcessingGenerative AIDiffusion ModelsDeep Learning

Keywords

lens flare removalimage enhancementdiffusion modelsoutpaintingLoRAplug-and-playpreprocessingcomputer visionautonomous drivingimage restorationgenerative AI

Academic Context

#Computer Vision#Image Processing#Generative Models#Diffusion Models#Image Restoration

Commercial Potential

Potential Products

Image enhancement softwarePreprocessing modules for autonomous driving systemsAPIs for real-time image quality improvement

Target Industries

AutomotiveRoboticsPhotographySecurityMedia

Use Case Examples

Improving camera perception for self-driving cars in sunny conditionsEnhancing drone imagery captured with challenging lightingCleaning up photos affected by lens flare

Competitive Edge

Offers a unique diffusion-based outpainting approach specifically for reconstructing light sources, acting as a versatile, retraining-free enhancement for existing SIFR methods.

Market Opportunity

Significant market for image quality improvement and perception enhancement.

Revenue Models

Licensing to software providersintegration into hardware (e.g.camera systems).

Resource Requirements

Compute Needs

Moderate for inference, potentially high for LoRA fine-tuning.

Data Requirements

Requires datasets with and without lens flare, and potentially data capturing off-frame light sources.

Deployment Constraints

Adds computational overhead as a preprocessing step.

Scalability

Scalability depends on the efficiency of the diffusion model and the outpainting process.

Production Readiness

Maturity Level

Research

Time to Market

6-12 months for integration.

Patent Potential

Moderate, for the specific diffusion-based outpainting technique for SIFR.

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