Corpus ID: 235614202

GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images

  title={GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images},
  author={Sungmin Cha and Taeeon Park and Byeongjoon Kim and Jongduk Baek and Taesup Moon},
We tackle a challenging blind image denoising problem, in which only single distinct noisy images are available for training a denoiser, and no information about noise is known, except for it being zero-mean, additive, and independent of the clean image. In such a setting, which often occurs in practice, it is not possible to train a denoiser with the standard discriminative training or with the recently developed Noise2Noise (N2N) training; the former requires the underlying clean image for… Expand
FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian Noise
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Adam: A Method for Stochastic Optimization
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Field of experts
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A Poisson-Gaussian Denoising Dataset With Real Fluorescence Microscopy Images
  • Yide Zhang, Yinhao Zhu, +4 authors S. Howard
  • Computer Science, Engineering
  • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2019
This paper constructs a dataset - the Fluorescence Microscopy Denoising (FMD) dataset - that is dedicated to Poisson-Gaussian denoising and uses this dataset to benchmark 10 representative denoised algorithms and finds that deep learning methods have the best performance. Expand
High-Quality Self-Supervised Deep Image Denoising
This work builds on a recent technique that removes the need for reference data by employing networks with a "blind spot" in the receptive field, and significantly improves two key aspects: image quality and training efficiency. Expand
Noise2Void - Learning Denoising From Single Noisy Images
Noise2Void is introduced, a training scheme that allows us to train directly on the body of data to be denoised and can therefore be applied when other methods cannot, and compares favorably to training-free denoising methods. Expand
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
This paper investigates the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image Denoising, and uses residual learning and batch normalization to speed up the training process as well as boost theDenoising performance. Expand
Wasserstein Generative Adversarial Networks
This work introduces a new algorithm named WGAN, an alternative to traditional GAN training that can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches. Expand
Noise2Noise: Learning Image Restoration without Clean Data
It is shown that under certain common circumstances, it is possible to learn to restore signals without ever observing clean ones, at performance close or equal to training using clean exemplars. Expand