精准模拟手机传感器退化，提升真实场景超分辨率效果
RAW-Domain Degradation Models for Realistic Smartphone Super-Resolution
- 通过校准还原渲染图像至各手机RAW域，建模设备特异性退化
- 在未见设备上测试，性能超越基于随机退化的基线模型
- 为手机端超分提供可复现、高保真训练数据生成方法
智能手机数码变焦依赖基于学习的RAW域超分辨率（SR）模型，但缺乏传感器级真实标签数据。通过“逆处理”流程合成数据可模拟高分辨率（HR）到低分辨率（LR）的退化过程，但现有方法因退化建模不完整或不真实，导致域差异。本文证明，通过精心设计的退化建模可显著提升真实场景下的SR性能。我们不依赖通用相机模糊与噪声先验，而是通过校准将公开渲染图像逆处理至不同手机的RAW域，构建图像对。基于这些配对数据训练单图RAW-to-RGB SR模型，并在保留设备的真实数据上评估。实验表明，精确退化建模带来明显性能提升，所提模型优于在大量任意选择退化数据上训练的基线。
原文摘要 · Abstract (English)
Digital zoom on smartphones relies on learning-based super-resolution (SR) models that operate on RAW sensor images, but obtaining sensor-specific training data is challenging due to the lack of ground-truth images. Synthetic data generation via ``unprocessing'' pipelines offers a potential solution by simulating the degradations that transform high-resolution (HR) images into their low-resolution (LR) counterparts. However, these pipelines can introduce domain gaps due to incomplete or unrealistic degradation modeling. In this paper, we demonstrate that principled and carefully designed degradation modeling can enhance SR performance in real-world conditions. Instead of relying on generic priors for camera blur and noise, we model device-specific degradations through calibration and unprocess publicly available rendered images into the RAW domain of different smartphones. Using these image pairs, we train a single-image RAW-to-RGB SR model and evaluate it on real data from a held-out device. Our experiments show that accurate degradation modeling leads to noticeable improvements, with our SR model outperforming baselines trained on large pools of arbitrarily chosen degradations.
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