Zero-Shot Noise2Noise: Efficient Image Denoising without any Data

• URL: http://arxiv.org/abs/2303.11253v3
• Date: Wed, 10 May 2023 09:12:59 GMT
• Title: Zero-Shot Noise2Noise: Efficient Image Denoising without any Data
• Authors: Youssef Mansour and Reinhard Heckel
• Abstract summary: We show that a simple 2-layer network, without any training data or knowledge of the noise distribution, can enable high-quality image denoising at low computational cost. Our approach is motivated by Noise2Noise and Neighbor2Neighbor and works well for denoising pixel-wise independent noise.
• Score: 26.808569077500128
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, self-supervised neural networks have shown excellent image denoising performance. However, current dataset free methods are either computationally expensive, require a noise model, or have inadequate image quality. In this work we show that a simple 2-layer network, without any training data or knowledge of the noise distribution, can enable high-quality image denoising at low computational cost. Our approach is motivated by Noise2Noise and Neighbor2Neighbor and works well for denoising pixel-wise independent noise. Our experiments on artificial, real-world camera, and microscope noise show that our method termed ZS-N2N (Zero Shot Noise2Noise) often outperforms existing dataset-free methods at a reduced cost, making it suitable for use cases with scarce data availability and limited computational resources. A demo of our implementation including our code and hyperparameters can be found in the following colab notebook: https://colab.research.google.com/drive/1i82nyizTdszyHkaHBuKPbWnTzao8HF9b

Related papers

• Self-Calibrated Variance-Stabilizing Transformations for Real-World Image Denoising [19.08732222562782]
  Supervised deep learning has become the method of choice for image denoising. We show that, contrary to popular belief, denoising networks specialized in the removal of Gaussian noise can be efficiently leveraged in favor of real-world image denoising.
  arXiv  Detail & Related papers  (2024-07-24T16:23:46Z)
• Enhancing convolutional neural network generalizability via low-rank weight approximation [6.763245393373041]
  Sufficient denoising is often an important first step for image processing. Deep neural networks (DNNs) have been widely used for image denoising. We introduce a new self-supervised framework for image denoising based on the Tucker low-rank tensor approximation.
  arXiv  Detail & Related papers  (2022-09-26T14:11:05Z)
• Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training [50.018580462619425]
  We propose a novel framework, namely Pixel-level Noise-aware Generative Adrial Network (PNGAN) PNGAN employs a pre-trained real denoiser to map the fake and real noisy images into a nearly noise-free solution space. For better noise fitting, we present an efficient architecture Simple Multi-versa-scale Network (SMNet) as the generator.
  arXiv  Detail & Related papers  (2022-04-06T14:09:02Z)
• Zero-shot Blind Image Denoising via Implicit Neural Representations [77.79032012459243]
  We propose an alternative denoising strategy that leverages the architectural inductive bias of implicit neural representations (INRs) We show that our method outperforms existing zero-shot denoising methods under an extensive set of low-noise or real-noise scenarios.
  arXiv  Detail & Related papers  (2022-04-05T12:46:36Z)
• 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)
• Noise2Score: Tweedie's Approach to Self-Supervised Image Denoising without Clean Images [35.41467558264341]
  We present a novel approach, called Noise2Score, which reveals a missing link in order to unite different approaches. Specifically, we show that image denoising problems without clean images can be addressed by finding the mode of the posterior distribution. Our method then uses the recent finding that the score function can be stably estimated from the noisy images using the amortized residual denoising autoencoder.
  arXiv  Detail & Related papers  (2021-06-13T14:41:09Z)
• 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)
• 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)
• Noise2Stack: Improving Image Restoration by Learning from Volumetric Data [0.0]
  We introduce Noise2Stack, an extension of the Noise2Noise method to image stacks. Our experiments on magnetic resonance brain scans and newly acquired multiplane microscopy data show that learning only from image neighbors in a stack is sufficient to outperform Noise2Noise and Noise2Void.
  arXiv  Detail & Related papers  (2020-11-10T14:01:47Z)
• CycleISP: Real Image Restoration via Improved Data Synthesis [166.17296369600774]
  We present a framework that models camera imaging pipeline in forward and reverse directions. By training a new image denoising network on realistic synthetic data, we achieve the state-of-the-art performance on real camera benchmark datasets.
  arXiv  Detail & Related papers  (2020-03-17T15:20:25Z)

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.