Low-light raw denoising is an important and valuable task in computational photography where learning-based methods trained with paired real data are mainstream. However, the limited data volume and complicated noise distribution have constituted a learnability bottleneck for paired real data, which limits the denoising performance of learning-based methods. To address this issue, we present a learnability enhancement strategy to reform paired real data according to noise modeling. Our strategy consists of two efficient techniques: shot noise augmentation (SNA) and dark shading correction (DSC). Through noise model decoupling, SNA improves the precision of data mapping by increasing the data volume and DSC reduces the complexity of data mapping by reducing the noise complexity. Extensive results on the public datasets and real imaging scenarios collectively demonstrate the state-of-the-art performance of our method.

翻译：低光原始拆卸是计算摄影中一项重要和宝贵的任务,在计算摄影中,以对称真实数据培训的基于学习的方法成为主流,然而,数据量有限和复杂的噪音分布构成了对称真实数据的可学习瓶颈,限制了学习方法的可读性表现。为解决这一问题,我们提出了一个根据噪音模型改革对称真实数据的可学习性增强战略。我们的战略由两种高效技术组成:射击噪音放大(SNA)和暗影修正(DSC)。通过噪音模型脱钩,SNA通过增加数据量提高了数据绘图的精确性,DSC通过减少噪音复杂性降低了数据绘图的复杂性。关于公共数据集和真实成像情景的广泛结果共同展示了我们方法的最新表现。