Noise2Stack: Improving Image Restoration by Learning from Volumetric Data

• URL: http://arxiv.org/abs/2011.05105v1
• Date: Tue, 10 Nov 2020 14:01:47 GMT
• Title: Noise2Stack: Improving Image Restoration by Learning from Volumetric Data
• Authors: Mikhail Papkov, Kenny Roberts, Lee Ann Madissoon, Omer Bayraktar, Dmytro Fishman, Kaupo Palo, Leopold Parts
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Biomedical images are noisy. The imaging equipment itself has physical limitations, and the consequent experimental trade-offs between signal-to-noise ratio, acquisition speed, and imaging depth exacerbate the problem. Denoising is, therefore, an essential part of any image processing pipeline, and convolutional neural networks are currently the method of choice for this task. One popular approach, Noise2Noise, does not require clean ground truth, and instead, uses a second noisy copy as a training target. Self-supervised methods, like Noise2Self and Noise2Void, relax data requirements by learning the signal without an explicit target but are limited by the lack of information in a single image. Here, we introduce Noise2Stack, an extension of the Noise2Noise method to image stacks that takes advantage of a shared signal between spatially neighboring planes. 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 and close the gap to supervised denoising methods. Our findings point towards low-cost, high-reward improvement in the denoising pipeline of multiplane biomedical images. As a part of this work, we release a microscopy dataset to establish a benchmark for the multiplane image denoising.

Related papers

• Noise2SR: Learning to Denoise from Super-Resolved Single Noisy Fluorescence Image [9.388253054229155]
  Noise2SR is designed for training with paired noisy images of different dimensions. It is more efficiently self-supervised and able to restore more image details from a single noisy observation. We envision that Noise2SR has the potential to improve more other kind of scientific imaging quality.
  arXiv  Detail & Related papers  (2022-09-14T04:44:41Z)
• 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)
• 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)
• Synergy Between Semantic Segmentation and Image Denoising via Alternate Boosting [102.19116213923614]
  We propose a boosting network to perform denoising and segmentation alternately. We observe that not only denoising helps combat the drop of segmentation accuracy due to noise, but also pixel-wise semantic information boosts the capability of denoising. Experimental results show that the denoised image quality is improved substantially and the segmentation accuracy is improved to close to that of clean images.
  arXiv  Detail & Related papers  (2021-02-24T06:48:45Z)
• 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)
• 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)

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.