Existing denoising methods typically restore clear results by aggregating pixels from the noisy input. Instead of relying on hand-crafted aggregation schemes, we propose to explicitly learn this process with deep neural networks. We present a spatial pixel aggregation network and learn the pixel sampling and averaging strategies for image denoising. The proposed model naturally adapts to image structures and can effectively improve the denoised results. Furthermore, we develop a spatio-temporal pixel aggregation network for video denoising to efficiently sample pixels across the spatio-temporal space. Our method is able to solve the misalignment issues caused by large motion in dynamic scenes. In addition, we introduce a new regularization term for effectively training the proposed video denoising model. We present extensive analysis of the proposed method and demonstrate that our model performs favorably against the state-of-the-art image and video denoising approaches on both synthetic and real-world data.

翻译：现有的脱色方法通常通过将噪音输入的像素集合起来,恢复清晰的结果。 我们建议不依靠手工艺的聚合计划,而是通过深神经网络来明确学习这一过程。 我们提出了一个空间像素聚合网络,并学习像素采样和图像脱色平均战略。 拟议的模型自然地适应图像结构,并能够有效地改进脱色结果。 此外, 我们开发了一个平流时像素聚合网络, 用于视频脱色, 以便在整个spatio- 时空空间高效采样像素。 我们的方法能够解决动态场上大动作造成的不匹配问题。 此外, 我们引入一个新的正规化术语, 以有效培训拟议的视频脱色模型。 我们对拟议方法进行了广泛的分析, 并表明我们的模型在合成数据和真实世界数据上都对最新图像和视频脱色方法有利。