Deep Denoising For Scientific Discovery: A Case Study In Electron Microscopy

• URL: http://arxiv.org/abs/2010.12970v2
• Date: Tue, 13 Jul 2021 15:12:33 GMT
• Title: Deep Denoising For Scientific Discovery: A Case Study In Electron Microscopy
• Authors: Sreyas Mohan, Ramon Manzorro, Joshua L. Vincent, Binh Tang, Dev Yashpal Sheth, Eero P. Simoncelli, David S. Matteson, Peter A. Crozier, Carlos Fernandez-Granda
• Abstract summary: We propose a simulation-based denoising (SBD) framework, in which CNNs are trained on simulated images. SBD outperforms existing techniques by a wide margin on a simulated benchmark dataset, as well as on real data. We release the first publicly available benchmark dataset of TEM images, containing 18,000 examples.
• Score: 22.566600256820646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Denoising is a fundamental challenge in scientific imaging. Deep convolutional neural networks (CNNs) provide the current state of the art in denoising natural images, where they produce impressive results. However, their potential has barely been explored in the context of scientific imaging. Denoising CNNs are typically trained on real natural images artificially corrupted with simulated noise. In contrast, in scientific applications, noiseless ground-truth images are usually not available. To address this issue, we propose a simulation-based denoising (SBD) framework, in which CNNs are trained on simulated images. We test the framework on data obtained from transmission electron microscopy (TEM), an imaging technique with widespread applications in material science, biology, and medicine. SBD outperforms existing techniques by a wide margin on a simulated benchmark dataset, as well as on real data. Apart from the denoised images, SBD generates likelihood maps to visualize the agreement between the structure of the denoised image and the observed data. Our results reveal shortcomings of state-of-the-art denoising architectures, such as their small field-of-view: substantially increasing the field-of-view of the CNNs allows them to exploit non-local periodic patterns in the data, which is crucial at high noise levels. In addition, we analyze the generalization capability of SBD, demonstrating that the trained networks are robust to variations of imaging parameters and of the underlying signal structure. Finally, we release the first publicly available benchmark dataset of TEM images, containing 18,000 examples.

Related papers

• Realistic Noise Synthesis with Diffusion Models [68.48859665320828]
  Deep image denoising models often rely on large amount of training data for the high quality performance. We propose a novel method that synthesizes realistic noise using diffusion models, namely Realistic Noise Synthesize Diffusor (RNSD) RNSD can incorporate guided multiscale content, such as more realistic noise with spatial correlations can be generated at multiple frequencies.
  arXiv  Detail & Related papers  (2023-05-23T12:56:01Z)
• Unsupervised Domain Transfer with Conditional Invertible Neural Networks [83.90291882730925]
  We propose a domain transfer approach based on conditional invertible neural networks (cINNs) Our method inherently guarantees cycle consistency through its invertible architecture, and network training can efficiently be conducted with maximum likelihood. Our method enables the generation of realistic spectral data and outperforms the state of the art on two downstream classification tasks.
  arXiv  Detail & Related papers  (2023-03-17T18:00:27Z)
• FSID: Fully Synthetic Image Denoising via Procedural Scene Generation [12.277286575812441]
  We develop a procedural synthetic data generation pipeline and dataset tailored to low-level vision tasks. Our Unreal engine-based synthetic data pipeline populates large scenes algorithmically with a combination of random 3D objects, materials, and geometric transformations. We then trained and validated a CNN-based denoising model, and demonstrated that the model trained on this synthetic data alone can achieve competitive denoising results.
  arXiv  Detail & Related papers  (2022-12-07T21:21:55Z)
• Robust Deep Ensemble Method for Real-world Image Denoising [62.099271330458066]
  We propose a simple yet effective Bayesian deep ensemble (BDE) method for real-world image denoising. Our BDE achieves +0.28dB PSNR gain over the state-of-the-art denoising method. Our BDE can be extended to other image restoration tasks, and achieves +0.30dB, +0.18dB and +0.12dB PSNR gains on benchmark datasets.
  arXiv  Detail & Related papers  (2022-06-08T06:19:30Z)
• Physics-based Noise Modeling for Extreme Low-light Photography [63.65570751728917]
  We study the noise statistics in the imaging pipeline of CMOS photosensors. We formulate a comprehensive noise model that can accurately characterize the real noise structures. Our noise model can be used to synthesize realistic training data for learning-based low-light denoising algorithms.
  arXiv  Detail & Related papers  (2021-08-04T16:36:29Z)
• Adaptive Denoising via GainTuning [17.72738152112575]
  Deep convolutional neural networks (CNNs) for image denoising are usually trained on large datasets. We propose "GainTuning", in which CNN models pre-trained on large datasets are adaptively and selectively adjusted for individual test images. We show that GainTuning improves state-of-the-art CNNs on standard image-denoising benchmarks, boosting their denoising performance on nearly every image in a held-out test set.
  arXiv  Detail & Related papers  (2021-07-27T13:35:48Z)
• Enhancement of a CNN-Based Denoiser Based on Spatial and Spectral Analysis [23.11994688706024]
  We propose a discrete wavelet denoising CNN (WDnCNN) which restores images corrupted by various noise with a single model. To address this issue, we present a band normalization module (BNM) to normalize the coefficients from different parts of the frequency spectrum. We evaluate the proposed WDnCNN, and compare it with other state-of-the-art denoisers.
  arXiv  Detail & Related papers  (2020-06-28T05:25:50Z)
• A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising [34.98772175073111]
  We present a highly accurate noise formation model based on the characteristics of CMOS photosensors. We also propose a method to calibrate the noise parameters for available modern digital cameras.
  arXiv  Detail & Related papers  (2020-03-28T09:16:48Z)
• 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)
• Noise2Inverse: Self-supervised deep convolutional denoising for tomography [0.0]
  Noise2Inverse is a deep CNN-based denoising method for linear image reconstruction algorithms. We develop a theoretical framework which shows that such training indeed obtains a denoising CNN. On simulated CT datasets, Noise2Inverse demonstrates an improvement in peak signal-to-noise ratio and structural similarity index.
  arXiv  Detail & Related papers  (2020-01-31T12:50:24Z)
• Deep Learning on Image Denoising: An overview [92.07378559622889]
  We offer a comparative study of deep techniques in image denoising. We first classify the deep convolutional neural networks (CNNs) for additive white noisy images. Next, we compare the state-of-the-art methods on public denoising datasets in terms of quantitative and qualitative analysis.
  arXiv  Detail & Related papers  (2019-12-31T05:03:57Z)

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.