Joint self-supervised blind denoising and noise estimation

• URL: http://arxiv.org/abs/2102.08023v1
• Date: Tue, 16 Feb 2021 08:37:47 GMT
• Title: Joint self-supervised blind denoising and noise estimation
• Authors: Jean Ollion, Charles Ollion (CMAP), Elisabeth Gassiat (LMO), Luc Leh\'ericy (JAD), Sylvain Le Corff (IP Paris, TIPIC-SAMOVAR, SAMOVAR)
• Abstract summary: Two neural networks jointly predict the clean signal and infer the noise distribution. We show empirically with synthetic noisy data that our model captures the noise distribution efficiently.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel self-supervised image blind denoising approach in which two neural networks jointly predict the clean signal and infer the noise distribution. Assuming that the noisy observations are independent conditionally to the signal, the networks can be jointly trained without clean training data. Therefore, our approach is particularly relevant for biomedical image denoising where the noise is difficult to model precisely and clean training data are usually unavailable. Our method significantly outperforms current state-of-the-art self-supervised blind denoising algorithms, on six publicly available biomedical image datasets. We also show empirically with synthetic noisy data that our model captures the noise distribution efficiently. Finally, the described framework is simple, lightweight and computationally efficient, making it useful in practical cases.

Related papers

• Explainable Artificial Intelligence driven mask design for self-supervised seismic denoising [0.0]
  Self-supervised coherent noise suppression methods require extensive knowledge of the noise statistics. We propose the use of explainable artificial intelligence approaches to see inside the black box that is the denoising network. We show that a simple averaging of the Jacobian contributions over a number of randomly selected input pixels, provides an indication of the most effective mask.
  arXiv  Detail & Related papers  (2023-07-13T11:02:55Z)
• Exploring Efficient Asymmetric Blind-Spots for Self-Supervised Denoising in Real-World Scenarios [44.31657750561106]
  Noise in real-world scenarios is often spatially correlated, which causes many self-supervised algorithms to perform poorly. We propose Asymmetric Tunable Blind-Spot Network (AT-BSN), where the blind-spot size can be freely adjusted. We show that our method achieves state-of-the-art, and is superior to other self-supervised algorithms in terms of computational overhead and visual effects.
  arXiv  Detail & Related papers  (2023-03-29T15:19:01Z)
• Poisson2Sparse: Self-Supervised Poisson Denoising From a Single Image [34.27748767631027]
  We present a novel self-supervised learning method for single-image denoising. We approximate traditional iterative optimization algorithms for image denoising with a recurrent neural network. Our method outperforms the state-of-the-art approaches in terms of PSNR and SSIM.
  arXiv  Detail & Related papers  (2022-06-04T00:08:58Z)
• IDR: Self-Supervised Image Denoising via Iterative Data Refinement [66.5510583957863]
  We present a practical unsupervised image denoising method to achieve state-of-the-art denoising performance. Our method only requires single noisy images and a noise model, which is easily accessible in practical raw image denoising. To evaluate raw image denoising performance in real-world applications, we build a high-quality raw image dataset SenseNoise-500 that contains 500 real-life scenes.
  arXiv  Detail & Related papers  (2021-11-29T07:22:53Z)
• The potential of self-supervised networks for random noise suppression in seismic data [0.0]
  Blind-spot networks are shown to be an efficient suppressor of random noise in seismic data. Results are compared with two commonly used random denoising techniques: FX-deconvolution and Curvelet transform. We believe this is just the beginning of utilising self-supervised learning in seismic applications.
  arXiv  Detail & Related papers  (2021-09-15T14:57:43Z)
• Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images [98.82804259905478]
  We present Neighbor2Neighbor to train an effective image denoising model with only noisy images. In detail, input and target used to train a network are images sub-sampled from the same noisy image. A denoising network is trained on sub-sampled training pairs generated in the first stage, with a proposed regularizer as additional loss for better performance.
  arXiv  Detail & Related papers  (2021-01-08T02:03:25Z)
• Noise2Kernel: Adaptive Self-Supervised Blind Denoising using a Dilated Convolutional Kernel Architecture [3.796436257221662]
  We propose a dilated convolutional network that satisfies an invariant property, allowing efficient kernel-based training without random masking. We also propose an adaptive self-supervision loss to circumvent the requirement of zero-mean constraint, which is specifically effective in removing salt-and-pepper or hybrid noise.
  arXiv  Detail & Related papers  (2020-12-07T12:13:17Z)
• Adaptive noise imitation for image denoising [58.21456707617451]
  We develop a new textbfadaptive noise imitation (ADANI) algorithm that can synthesize noisy data from naturally noisy images. To produce realistic noise, a noise generator takes unpaired noisy/clean images as input, where the noisy image is a guide for noise generation. Coupling the noisy data output from ADANI with the corresponding ground-truth, a denoising CNN is then trained in a fully-supervised manner.
  arXiv  Detail & Related papers  (2020-11-30T02:49:36Z)
• Unpaired Learning of Deep Image Denoising [80.34135728841382]
  This paper presents a two-stage scheme by incorporating self-supervised learning and knowledge distillation. For self-supervised learning, we suggest a dilated blind-spot network (D-BSN) to learn denoising solely from real noisy images. Experiments show that our unpaired learning method performs favorably on both synthetic noisy images and real-world noisy photographs.
  arXiv  Detail & Related papers  (2020-08-31T16:22:40Z)
• Dual Adversarial Network: Toward Real-world Noise Removal and Noise Generation [52.75909685172843]
  Real-world image noise removal is a long-standing yet very challenging task in computer vision. We propose a novel unified framework to deal with the noise removal and noise generation tasks. Our method learns the joint distribution of the clean-noisy image pairs.
  arXiv  Detail & Related papers  (2020-07-12T09:16:06Z)
• Variational Denoising Network: Toward Blind Noise Modeling and Removal [59.36166491196973]
  Blind image denoising is an important yet very challenging problem in computer vision. We propose a new variational inference method, which integrates both noise estimation and image denoising.
  arXiv  Detail & Related papers  (2019-08-29T15:54:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.