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Many video enhancement algorithms rely on optical flow to register frames in a video sequence.
#5 best model for 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.
SOTA for 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.
Our denoiser can be used without knowledge of the origin of the video or burst and the post-processing steps applied from the camera sensor.
Modeling the processing chain that has produced a video is a difficult reverse engineering task, even when the camera is available.