Understanding and Tackling Scattering and Reflective Flare for Mobile Camera Systems
Fengbo Lan, Chang Wen Chen

TL;DR
This paper introduces a comprehensive raw image dataset for mobile cameras to improve flare removal techniques, identifies ISP operations affecting flare mitigation, and provides insights for optimizing mobile camera image processing.
Contribution
It presents a new large-scale raw image dataset for mobile flare study and analyzes how ISP operations influence flare removal effectiveness.
Findings
ISP denoising, compression, sharpening impact flare removal
Dataset enables better generalization of flare removal algorithms
Insights into optimizing ISP for improved flare mitigation
Abstract
The rise of mobile devices has spurred advancements in camera technology and image quality. However, mobile photography still faces issues like scattering and reflective flares. While previous research has acknowledged the negative impact of the mobile devices' internal image signal processing pipeline (ISP) on image quality, the specific ISP operations that hinder flare removal have not been fully identified. In addition, current solutions only partially address ISP-related deterioration due to a lack of comprehensive raw image datasets for flare study. To bridge these research gaps, we introduce a new raw image dataset tailored for mobile camera systems, focusing on eliminating flare. This dataset encompasses over 2,000 high-quality, full-resolution raw image pairs for scattering flare, and 1,200 for reflective flare, captured across various real-world scenarios, mobile devices, and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsImage Enhancement Techniques · Image and Signal Denoising Methods · Advanced Image Processing Techniques
