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Many video enhancement algorithms rely on optical flow to register frames in a video sequence.
Ranked #6 on Video Frame Interpolation on Middlebury
In this paper, we propose a state-of-the-art video denoising algorithm based on a convolutional neural network architecture.
Ranked #1 on Video Denoising on Set8 sigma10
Clean video frames for dynamic scenes cannot be captured with a long-exposure shutter or averaging multi-shots as was done for static images.
We present a spatial pixel aggregation network and learn the pixel sampling and averaging strategies for image denoising.
Most of the classical denoising methods restore clear results by selecting and averaging pixels in the noisy input.