22 papers with code • 11 benchmarks • 2 datasets
In this paper, we propose a state-of-the-art video denoising algorithm based on a convolutional neural network architecture.
We present a spatial pixel aggregation network and learn the pixel sampling and averaging strategies for image denoising.
Joint Adaptive Sparsity and Low-Rankness on the Fly: An Online Tensor Reconstruction Scheme for Video Denoising
In this work, we propose a novel video denoising method, based on an online tensor reconstruction scheme with a joint adaptive sparse and low-rank model, dubbed SALT.
Transform learning methods involve cheap computations and have been demonstrated to perform well in applications such as image denoising and medical image reconstruction.
Modeling the processing chain that has produced a video is a difficult reverse engineering task, even when the camera is available.