  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.
