A U-net trained on noisy image pairs alone, without clean targets, denoises solar Stokes images to about 6e-4 continuum residual, matching clean-target training on synthetic data.
Model-blind Video Denoising Via Frame-to-frame Training
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abstract
Modeling the processing chain that has produced a video is a difficult reverse engineering task, even when the camera is available. This makes model based video processing a still more complex task. In this paper we propose a fully blind video denoising method, with two versions off-line and on-line. This is achieved by fine-tuning a pre-trained AWGN denoising network to the video with a novel frame-to-frame training strategy. 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. The on-line process only requires a couple of frames before achieving visually-pleasing results for a wide range of perturbations. It nonetheless reaches state of the art performance for standard Gaussian noise, and can be used off-line with still better performance.
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astro-ph.SR 1years
2019 1verdicts
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Solar image denoising with convolutional neural networks
A U-net trained on noisy image pairs alone, without clean targets, denoises solar Stokes images to about 6e-4 continuum residual, matching clean-target training on synthetic data.