用统一模型实现图像与视频的sRGB到RAW还原，提升重建质量。
RAWMamba: Unified sRGB-to-RAW De-rendering With State Space Model
- 基于Mamba架构，融合多源元数据生成统一表征。
- 在ImageNet、YouTube-VIS等数据集上均达当前最佳性能。
- 适合需要跨模态还原的视觉算法研发者使用。
近年来，sRGB到RAW的去渲染技术越来越依赖元数据驱动的方法，通过部分RAW信息辅助重建原始数据。在图像任务中，元数据通常通过采样获得；在视频任务中，则常从初始帧推导。由于元数据需求不同，现有方法需专用网络结构，导致架构不兼容，部署复杂。本文提出RAWMamba，一种基于Mamba的统一框架，适用于图像与视频领域的sRGB到RAW去渲染。其核心是统一元数据嵌入（UME）模块，将多种元数据类型映射为统一表示。具体地，采用多视角亲和建模方法促进参考信息提取。此外，引入局部色调感知Mamba（LTA-Mamba）模块，捕捉长程依赖，实现元数据的有效全局传播。实验表明，RAWMamba在多个数据集上均达到领先性能，可高质量重建原始图像数据。
原文摘要 · Abstract (English)
Recent advancements in sRGB-to-RAW de-rendering have increasingly emphasized metadata-driven approaches to reconstruct RAW data from sRGB images, supplemented by partial RAW information. In image-based de-rendering, metadata is commonly obtained through sampling, whereas in video tasks, it is typically derived from the initial frame. The distinct metadata requirements necessitate specialized network architectures, leading to architectural incompatibilities that increase deployment complexity. In this paper, we propose RAWMamba, a Mamba-based unified framework developed for sRGB-to-RAW de-rendering across both image and video domains. The core of RAWMamba is the Unified Metadata Embedding (UME) module, which harmonizes diverse metadata types into a unified representation. In detail, a multi-perspective affinity modeling method is proposed to promote the extraction of reference information. In addition, we introduce the Local Tone-Aware Mamba (LTA-Mamba) module, which captures long-range dependencies to enable effective global propagation of metadata. Experimental results demonstrate that the proposed RAWMamba achieves state-of-the-art performance, yielding high-quality RAW data reconstruction.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。