论文标题
自我监督的泊松高斯denoising
Self-Supervised Poisson-Gaussian Denoising
论文作者
论文摘要
我们将盲点模型扩展了自我监督的denoisising，以处理泊松高斯噪声，并引入了改进的训练方案，该方案避免了超参数并将DeNoiser适应测试数据。自我监督的Denoising模型仅从嘈杂的数据中学习denoise，并且不需要相应的干净图像，这些图像很难或不可能在某些感兴趣的应用领域（例如低光显微镜）中获取。我们引入了一种新的训练策略来处理泊松高斯噪声，这是显微镜图像的标准噪声模型。我们的新策略从损失函数中消除了超参数，这在自我监管的制度中很重要，在该制度中，没有地面真相数据可用于指导超参数调整。我们展示了如何将我们的DeNoiser适应测试数据以提高性能。我们对显微镜图像的评估定位基准验证了我们的方法。
We extend the blindspot model for self-supervised denoising to handle Poisson-Gaussian noise and introduce an improved training scheme that avoids hyperparameters and adapts the denoiser to the test data. Self-supervised models for denoising learn to denoise from only noisy data and do not require corresponding clean images, which are difficult or impossible to acquire in some application areas of interest such as low-light microscopy. We introduce a new training strategy to handle Poisson-Gaussian noise which is the standard noise model for microscope images. Our new strategy eliminates hyperparameters from the loss function, which is important in a self-supervised regime where no ground truth data is available to guide hyperparameter tuning. We show how our denoiser can be adapted to the test data to improve performance. Our evaluations on microscope image denoising benchmarks validate our approach.
