Learning Deformable Kernels for Image and Video Denoising

Understand

Most of the classical denoising methods restore clear results by selecting and averaging pixels in the noisy input.

• Instead of relying on hand-crafted selecting and averaging strategies, we propose to explicitly learn this process with deep neural networks.
• Specifically, we propose deformable 2D kernels for image denoising where the sampling locations and kernel weights are both learned.
• The proposed kernel naturally adapts to image structures and could effectively reduce the oversmoothing artifacts.