论文标题
补丁程序自制培训，用于相关图像Denoising
Patch-Craft Self-Supervised Training for Correlated Image Denoising
论文作者
论文摘要
已知有监督的神经网络可以在各种图像恢复任务中取得出色的成果。但是，这种培训需要由成对损坏的图像及其相应地面真相目标组成的数据集。不幸的是，此类数据在许多应用程序中都不可用。对于未知噪声统计数据的图像denoising的任务，已经提出了几种自我监督的训练方法来克服这一困难。其中一些需要了解噪声模型，而另一些则认为污染噪声是不相关的，这两个假设都太限制了许多实际需求。这项工作提出了一种新型的自我监督训练技术，适合消除未知相关噪声。提出的方法既不需要了解噪声模型，也不需要访问地面真相目标。我们算法的输入由易于捕获的嘈杂镜头组成。我们的算法通过贴片匹配和缝线构造了这些爆发中的人造斑块的图像，并且获得的制作图像被用作训练的目标。我们的方法不需要在突发中注册图像。我们通过与合成和真实图像噪声进行的广泛实验评估了所提出的框架。
Supervised neural networks are known to achieve excellent results in various image restoration tasks. However, such training requires datasets composed of pairs of corrupted images and their corresponding ground truth targets. Unfortunately, such data is not available in many applications. For the task of image denoising in which the noise statistics is unknown, several self-supervised training methods have been proposed for overcoming this difficulty. Some of these require knowledge of the noise model, while others assume that the contaminating noise is uncorrelated, both assumptions are too limiting for many practical needs. This work proposes a novel self-supervised training technique suitable for the removal of unknown correlated noise. The proposed approach neither requires knowledge of the noise model nor access to ground truth targets. The input to our algorithm consists of easily captured bursts of noisy shots. Our algorithm constructs artificial patch-craft images from these bursts by patch matching and stitching, and the obtained crafted images are used as targets for the training. Our method does not require registration of the images within the burst. We evaluate the proposed framework through extensive experiments with synthetic and real image noise.
