Calibration-based methods have dominated RAW image denoising under extremely low-light environments. However, these methods suffer from several main deficiencies: 1) the calibration procedure is laborious and time-consuming, 2) denoisers for different cameras are difficult to transfer, and 3) the discrepancy between synthetic noise and real noise is enlarged by high digital gain. To overcome the above shortcomings, we propose a calibration-free pipeline for Lighting Every Drakness (LED), regardless of the digital gain or camera sensor. Instead of calibrating the noise parameters and training repeatedly, our method could adapt to a target camera only with few-shot paired data and fine-tuning. In addition, well-designed structural modification during both stages alleviates the domain gap between synthetic and real noise without any extra computational cost. With 2 pairs for each additional digital gain (in total 6 pairs) and 0.5% iterations, our method achieves superior performance over other calibration-based methods. Our code is available at https://github.com/Srameo/LED .

翻译：基于标定的方法在极低光照环境下的RAW图像去噪中占据主导地位。然而，这些方法存在几个主要缺陷：1）标定过程繁琐且耗时，2）不同相机的去噪器难以迁移，3）高数字增益会放大合成噪声与真实噪声之间的差异。为了克服上述不足，我们提出了一种无需标定的流水线，用于“每两对点亮全部黑暗”（Lighting Every Darkness, LED），该方法不受数字增益或相机传感器的影响。我们的方法无需重复标定噪声参数和训练，仅通过少量配对数据微调即可适配目标相机。此外，在两个阶段中精心设计的结构修改缓解了合成噪声与真实噪声之间的域差距，且不增加额外计算成本。通过为每个额外数字增益提供2对数据（共6对）及0.5%的训练迭代次数，我们的方法取得了优于其他基于标定方法的性能。我们的代码开源于 https://github.com/Srameo/LED 。