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.
Bibliographical noteFunding Information:
Manuscript received September 29, 2019; revised April 14, 2020 and May 17, 2020; accepted May 18, 2020. Date of publication June 8, 2020; date of current version July 8, 2020. This work was supported in part by the NSF CAREER under Grant 1149783, and gifts from eBay and Google. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Jiaying Liu. (Corresponding author: Ming-Hsuan Yang.) Xiangyu Xu is with the Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213 USA (e-mail: firstname.lastname@example.org).
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All Science Journal Classification (ASJC) codes
- Computer Graphics and Computer-Aided Design