Leveraging Frame Affinity for sRGB-to-RAW Video De-rendering

Unprocessed RAW video has shown distinct advantages over sRGB video in video editing and computer vision tasks. However, capturing RAW video is challenging due to limitations in bandwidth and storage. Various methods have been proposed to address similar issues in single image RAW capture through de-rendering. These methods utilize both the metadata and the sRGB image to perform sRGB-to-RAW de-rendering and recover high-quality single-frame RAW data. However, metadata-based methods always require additional computation for online metadata generation, imposing severe burden on mobile camera device for high frame rate RAW video capture. To address this issue, we propose a framework that utilizes frame affinity to achieve high-quality sRGB-to-RAW video reconstruction. Our approach consists of two main steps. The first step, temporal affinity prior extraction, uses motion information between adjacent frames to obtain a reference RAW image. The second step, spatial feature fusion and mapping, learns a pixel-level mapping function using scene-specific and position-specific features provided by the previous frame. Our method can be easily applied to current mobile camera equipment without complicated adaptations or added burden. To demonstrate the effectiveness of our approach, we introduce the first RAW Video De-rendering Benchmark. In this benchmark, our method outperforms state-of-the-art RAW image reconstruction methods, even without image-level metadata.