SpiralDiff: Spiral Diffusion with LoRA for RGB-to-RAW Conversion Across Cameras

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• SpiralDiff uses a signal-dependent noise weighting strategy to adapt reconstruction fidelity across different pixel intensity levels.method 95%
• SpiralDiff introduces CamLoRA, a camera-aware lightweight adaptation module that enables a unified model to adapt to different camera-specific ISP characteristics.method 95%
• SpiralDiff demonstrates superiority in RGB-to-RAW conversion quality compared to other methods on four benchmark datasets.result 90%
• SpiralDiff provides downstream benefits in RAW-based object detection tasks.result 90%
• Existing RGB-to-RAW conversion methods overlook the challenge that reconstruction difficulty varies with pixel intensity.limitation 85%
• Existing RGB-to-RAW conversion methods overlook the challenge that multi-camera conversion requires camera-specific adaptation.limitation 85%
• RAW images preserve superior fidelity and rich scene information compared to RGB images.technical 90%
• RGB-to-RAW conversion methods aim to alleviate the high cost of data collection for RAW images.technical 90%

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