Understanding and Tackling Scattering and Reflective Flare for Mobile Camera Systems
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 camera settings. It is designed to enhance the generalizability of flare removal algorithms across a wide spectrum of conditions. Through detailed experiments, we have identified that ISP operations, such as denoising, compression, and sharpening, may either improve or obstruct flare removal, offering critical insights into optimizing ISP configurations for better flare mitigation. Our dataset is poised to advance the understanding of flare-related challenges, enabling more precise incorporation of flare removal steps into the ISP. Ultimately, this work paves the way for significant improvements in mobile image quality, benefiting both enthusiasts and professional mobile photographers alike.
Relevance To Conference: This work enhances multimedia/multimodal processing by tackling flare removal in mobile photography, a key challenge in image quality enhancement. It advances the field in several ways:
1. Dataset Enrichment: We Introduce a comprehensive raw image dataset tailored for mobile photography flare studies, featuring over 3,100 high-quality, full-resolution image pairs. This dataset is a critical resource for developing and testing image enhancement and restoration algorithms.
2. ISP Operations Analysis: We identify and analyze key operations within the mobile camera's internal image processing pipeline (ISP), such as denoising, compression, and sharpening. This analysis deepens our understanding of image processing and offers insights into optimizing image quality, crucial for multimedia applications where input image quality significantly influences output.
3. Image Quality Improvement: Image quality is improved by addressing scattering and reflective flare, fundamental for applications needing clear and detailed images for accurate analysis, such as augmented reality, computer vision, and image recognition.
In conclusion, by focusing on flare removal and conducting an in-depth analysis of ISP operations, this work significantly enhances the foundational elements of multimedia/multimodal processing, such as dataset resources and processing techniques. This not only advances technology and applications in image analysis but also benefits multimedia applications reliant on high-quality images.
Supplementary Material: zip
Primary Subject Area: [Content] Media Interpretation
Secondary Subject Area: [Content] Media Interpretation, [Experience] Multimedia Applications, [Systems] Systems and Middleware
Submission Number: 3331
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