In fluorescence microscopy live-cell imaging, there is a critical trade-off between the signal-to-noise ratio and spatial resolution on one side, and the integrity of the biological sample on the other side. To obtain clean high-resolution (HR) images, one can either use microscopy techniques, such as structured-illumination microscopy (SIM), or apply denoising and super-resolution (SR) algorithms. However, the former option requires multiple shots that can damage the samples, and although efficient deep learning based algorithms exist for the latter option, no benchmark exists to evaluate these algorithms on the joint denoising and SR (JDSR) tasks. To study JDSR on microscopy data, we propose such a novel JDSR dataset, Widefield2SIM (W2S), acquired using a conventional fluorescence widefield and SIM imaging. W2S includes 144,000 real fluorescence microscopy images, resulting in a total of 360 sets of images. A set is comprised of noisy low-resolution (LR) widefield images with different noise levels, a noise-free LR image, and a corresponding high-quality HR SIM image. W2S allows us to benchmark the combinations of 6 denoising methods and 6 SR methods. We show that state-of-the-art SR networks perform very poorly on noisy inputs. Our evaluation also reveals that applying the best denoiser in terms of reconstruction error followed by the best SR method does not necessarily yield the best final result. Both quantitative and qualitative results show that SR networks are sensitive to noise and the sequential application of denoising and SR algorithms is sub-optimal. Lastly, we demonstrate that SR networks retrained end-to-end for JDSR outperform any combination of state-of-the-art deep denoising and SR networks
We leverage deep learning techniques to jointly denoise and super-resolve biomedical images acquired with fluorescence microscopy. We develop a deep learning algorithm based on the networks and method described in the recent W2S paper to solve a joint denoising and super-resolution problem. Specifically, we address the restoration of SIM images from widefield images. Our TensorFlow model is trained on the W2S dataset of cell images and is made accessible online in this repository: https://github.com/mchatton/w2s-tensorflow. On test images, the model shows a visually-convincing denoising and in…
Zero-Shot Image Denoising for High-Resolution Electron Microscopy
School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）；Lingang Laboratory（临港实验室）；School of Physical Science and Technology, ShanghaiTech University（上海科技大学物理科学与技术学院）；School of Software, Tsinghua University（清华大学软件学院）；School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学生物医学工程学院与医学机器人研究所）
Indian Institute of Technology Guwahati（印度理工学院古瓦哈蒂分校）；UiT The Arctic University of Norway（挪威特罗姆瑟大学（UiT 挪威北极大学））；Indian Institute of Technology Dhanbad（印度理工学院丹巴德分校）
Scanning acoustic microscopy (SAM) has been employed since microscopic images are widely used for biomedical or materials research. Acoustic imaging is an important and well-established method used in nondestructive testing (NDT), bio-medical imaging, and structural health monitoring.The imaging is frequently carried out with signals of low amplitude, which might result in leading that are noisy and lacking in details of image information. In this work, we attempted to analyze SAM images acquired from low amplitude signals and employed a block matching filter over time domain signals to obtain…
Denoising diffusion models for high-resolution microscopy image restoration
Frankfurt Institute for Advanced Studies (FIAS)（法兰克福高级研究所）；Goethe University Frankfurt（法兰克福大学）；Institute of Physical and Theoretical Chemistry（物理与理论化学研究所）
University of Notre Dame（圣母大学）；California Institute of Technology（加州理工学院）；Indiana University（印第安纳大学）
Fluorescence lifetime imaging microscopy (FLIM) systems are limited by their slow processing speed, low signal-to-noise ratio (SNR), and expensive and challenging hardware setups. In this work, we demonstrate applying a denoising convolutional network to improve FLIM SNR. The network will be integrated with an instant FLIM system with fast data acquisition based on analog signal processing, high SNR using high-efficiency pulse-modulation, and cost-effective implementation utilizing off-the-shelf radio-frequency components. Our instant FLIM system simultaneously provides the intensity, lifetime…
Deep Joint Denoising and Detection for Enhanced Intracellular Particle Analysis
Erasmus University Medical Center（伊拉斯姆斯大学医学中心）；Utrecht University（乌得勒支大学）；University of New South Wales (UNSW)（新南威尔士大学）；NVIDIA Corporation（英伟达公司）；SURFsara
Fluorescence microscopy is a key driver to promote discoveries of biomedical research. However, with the limitation of microscope hardware and characteristics of the observed samples, the fluorescence microscopy images are susceptible to noise. Recently, a few self-supervised deep learning (DL) denoising methods have been proposed. However, the training efficiency and denoising performance of existing methods are relatively low in real scene noise removal. To address this issue, this paper proposed self-supervised image denoising method Noise2SR (N2SR) to train a simple and effective image den…
Google Research（谷歌研究院）；Fondazione Human Technopole（人类技术城基金会）
Image denoising and artefact removal are complex inverse problems admitting multiple valid solutions. Unsupervised diversity restoration, that is, obtaining a diverse set of possible restorations given a corrupted image, is important for ambiguity removal in many applications such as microscopy where paired data for supervised training are often unobtainable. In real world applications, imaging noise and artefacts are typically hard to model, leading to unsatisfactory performance of existing unsupervised approaches. This work presents an interpretable approach for unsupervised and diverse imag…
Idiap Research Institute（伊迪亚普研究所）；University of California, Santa Barbara（加州大学圣巴巴拉分校）
Optical flow is a method aimed at predicting the movement velocity of any pixel in the image and is used in medicine and biology to estimate flow of particles in organs or organelles. However, a precise optical flow measurement requires images taken at high speed and low exposure time, which induces phototoxicity due to the increase in illumination power. We are looking here to estimate the three-dimensional movement vector field of moving out-of-plane particles using normal light conditions and a standard microscope camera. We present a method to predict, from a single textured wide-field mic…
MPI-CBG/PKS (CSBD)（马克斯·普朗克分子细胞生物学与遗传学研究所/普朗克计算生物学中心（德累斯顿系统生物学中心））；Brno University of Technology（布尔诺理工大学）
Today, Convolutional Neural Networks (CNNs) are the leading method for image denoising. They are traditionally trained on pairs of images, which are often hard to obtain for practical applications. This motivates self-supervised training methods such as Noise2Void~(N2V) that operate on single noisy images. Self-supervised methods are, unfortunately, not competitive with models trained on image pairs. Here, we present 'Probabilistic Noise2Void' (PN2V), a method to train CNNs to predict per-pixel intensity distributions. Combining these with a suitable description of the noise, we obtain a compl…
Institute of Computer Science, University of Tartu（塔尔图大学计算机科学研究所）；Wellcome Sanger Institute（惠康桑格研究所）；PerkinElmer Cellular Technologies（珀金埃尔默细胞技术公司）
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 lear…
TOOPLOOX（TOOPLOOX 公司）；Wrocław University of Science and Technology（弗罗茨瓦夫科技大学）
Image quality is a nebulous concept with different meanings to different people. To quantify image quality a relative difference is typically calculated between a corrupted image and a ground truth image. But what metric should we use for measuring this difference? Ideally, the metric should perform well for both natural and scientific images. The structural similarity index (SSIM) is a good measure for how humans perceive image similarities, but is not sensitive to differences that are scientifically meaningful in microscopy. In electron and super-resolution microscopy, the Fourier Ring Corre…
FAU Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）；Siemens Healthcare GmbH（西门子医疗有限公司）；Fraunhofer Institute for Ceramic Technologies and Systems IKTS（弗劳恩霍夫陶瓷技术与系统研究所IKTS）；Ewha Womans University（梨花女子大学）
Self-supervised image denoising techniques emerged as convenient methods that allow training denoising models without requiring ground-truth noise-free data. Existing methods usually optimize loss metrics that are calculated from multiple noisy realizations of similar images, e.g., from neighboring tomographic slices. However, those approaches fail to utilize the multiple contrasts that are routinely acquired in medical imaging modalities like MRI or dual-energy CT. In this work, we propose the new self-supervised training scheme Noise2Contrast that combines information from multiple measured …
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究所）；University College London（伦敦大学学院）；Q.bio Inc.（Q.bio公司）
Microscopy is a powerful visualization tool in biology, enabling the study of cells, tissues, and the fundamental biological processes; yet, the observed images typically suffer from blur and background noise. In this work, we propose a unifying framework of algorithms for Gaussian image deblurring and denoising. These algorithms are based on deep learning techniques for the design of learnable regularizers integrated into the Wiener-Kolmogorov filter. Our extensive experimentation line showcases that the proposed approach achieves a superior quality of image reconstruction and surpasses the s…
Nanjing University of Science and Technology（南京理工大学）；Soochow University（苏州大学）
Structured illumination microscopy (SIM) is an important super-resolution based microscopy technique that breaks the diffraction limit and enhances optical microscopy systems. With the development of biology and medical engineering, there is a high demand for real-time and robust SIM imaging under extreme low light and short exposure environments. Existing SIM techniques typically require multiple structured illumination frames to produce a high-resolution image. In this paper, we propose a single-frame structured illumination microscopy (SF-SIM) based on deep learning. Our SF-SIM only needs o…
With the advent of advances in self-supervised learning, paired clean-noisy data are no longer required in deep learning-based image denoising. However, existing blind denoising methods still require the assumption with regard to noise characteristics, such as zero-mean noise distribution and pixel-wise noise-signal independence; this hinders wide adaptation of the method in the medical domain. On the other hand, unpaired learning can overcome limitations related to the assumption on noise characteristics, which makes it more feasible for collecting the training data in real-world scenarios. I…
California Polytechnic State University（加州州立理工大学）；University of Georgia（佐治亚大学）；Providence College（普罗维登斯学院）；University of Colorado Colorado Springs（科罗拉多大学科罗拉多斯普林斯分校）
We extend the blindspot model for self-supervised denoising to handle Poisson-Gaussian noise and introduce an improved training scheme that avoids hyperparameters and adapts the denoiser to the test data. Self-supervised models for denoising learn to denoise from only noisy data and do not require corresponding clean images, which are difficult or impossible to acquire in some application areas of interest such as low-light microscopy. We introduce a new training strategy to handle Poisson-Gaussian noise which is the standard noise model for microscope images. Our new strategy eliminates hyper…
Beyond Fixed Inference: Quantitative Flow Matching for Adaptive Image Denoising
School of Mathematical Sciences, Capital Normal University（首都师范大学数学科学学院）；National Center for Applied Mathematics Beijing, Capital Normal University（首都师范大学北京国家应用数学中心）；Academy for Multidisciplinary Studies, Capital Normal University（首都师范大学交叉科学研究院）；Institute of Nuclear and New Energy Technology, Tsinghua University（清华大学核能与新能源技术研究院）
German Research Center for Artificial Intelligence GmbH (DFKI)（德国人工智能研究中心有限公司（DFKI））；Saarland University（萨尔兰大学）；FEI（FEI公司）
In scanning electron microscopy, the achievable image quality is often limited by a maximum feasible acquisition time per dataset. Particularly with regard to three-dimensional or large field-of-view imaging, a compromise must be found between a high amount of shot noise, which leads to a low signal-to-noise ratio, and excessive acquisition times. Assuming a fixed acquisition time per frame, we compared three different strategies for algorithm-assisted image acquisition in scanning electron microscopy. We evaluated (1) raster scanning with a reduced dwell time per pixel followed by a state-of-…
Confocal microscopy is essential for histopathologic cell visualization and quantification. Despite its significant role in biology, fluorescence confocal microscopy suffers from the presence of inherent noise during image acquisition. Non-local patch-wise Bayesian mean filtering (NLB) was until recently the state-of-the-art denoising approach. However, classic denoising methods have been outperformed by neural networks in recent years. In this work, we propose to exploit the strengths of NLB in the framework of Bayesian deep learning. We do so by designing a convolutional neural network and t…
Institute of Statistical Science, Academia Sinica（中央研究院統計科學研究所）；Institute of Chemistry, Academia Sinica（中央研究院化學研究所）
Principal component analysis (PCA) is arguably the most widely used dimension-reduction method for vector-type data. When applied to a sample of images, PCA requires vectorization of the image data, which in turn entails solving an eigenvalue problem for the sample covariance matrix. We propose herein a two-stage dimension reduction (2SDR) method for image reconstruction from high-dimensional noisy image data. The first stage treats the image as a matrix, which is a tensor of order 2, and uses multilinear principal component analysis (MPCA) for matrix rank reduction and image denoising. The se…
Max-Planck Institute of Molecular Cell Biology and Genetics（马克斯·普朗克分子细胞生物学与遗传学研究所）；Center for Systems Biology Dresden (CSBD)（德累斯顿系统生物学中心）；Fondazione Human Technopole（人类技术城基金会）；Max Planck Institute for the Physics of Complex Systems（马克斯·普朗克复杂系统物理研究所）
Many microscopy applications are limited by the total amount of usable light and are consequently challenged by the resulting levels of noise in the acquired images. This problem is often addressed via (supervised) deep learning based denoising. Recently, by making assumptions about the noise statistics, self-supervised methods have emerged. Such methods are trained directly on the images that are to be denoised and do not require additional paired training data. While achieving remarkable results, self-supervised methods can produce high-frequency artifacts and achieve inferior results compar…
Poisson2Gaussian: Noise Gaussianization to Enhance Image Denoising
College of AI, Tsinghua University（清华大学人工智能学院）；Department of Automation, Tsinghua University（清华大学自动化系）；School of Electronic and Information Engineering, Beihang University（北京航空航天大学电子信息工程学院）
In computer-aided diagnosis (CAD) focused on microscopy, denoising improves the quality of image analysis. In general, the accuracy of this process may depend both on the experience of the microscopist and on the equipment sensitivity and specificity. A medical image could be corrupted by both intrinsic noise, due to the device limitations, and, by extrinsic signal perturbations during image acquisition. Nowadays, CAD deep learning applications pre-process images with image denoising models to reinforce learning and prediction. In this work, an innovative and lightweight deep multiscale convol…
Fluorescence microscopy has enabled a dramatic development in modern biology. Due to its inherently weak signal, fluorescence microscopy is not only much noisier than photography, but also presented with Poisson-Gaussian noise where Poisson noise, or shot noise, is the dominating noise source. To get clean fluorescence microscopy images, it is highly desirable to have effective denoising algorithms and datasets that are specifically designed to denoise fluorescence microscopy images. While such algorithms exist, no such datasets are available. In this paper, we fill this gap by constructing a …
Universidad Politécnica de Madrid（马德里理工大学）；Centre national de la recherche scientifique (CNRS)（法国国家科学研究中心）；Interdisciplinary Institute for Neuroscience (IINS)（跨学科神经科学研究所）；University of Bordeaux（波尔多大学）；Mechanobiology Institute（机械生物学研究所）；National University of Singapore（新加坡国立大学）
Microscopy images often suffer from high levels of noise, which can hinder further analysis and interpretation. Content-aware image restoration (CARE) methods have been proposed to address this issue, but they often require large amounts of training data and suffer from over-fitting. To overcome these challenges, we propose a novel framework for few-shot microscopy image denoising. Our approach combines a generative adversarial network (GAN) trained via contrastive learning (CL) with two structure preserving loss terms (Structural Similarity Index and Total Variation loss) to further improve t…
Brookhaven National Laboratory（布鲁克海文国家实验室）；University of California, Irvine（加利福尼亚大学尔湾分校）
Atom segmentation and localization, noise reduction and deblurring of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task. Although several conventional algorithms, such has thresholding, edge detection and clustering, can achieve reasonable performance in some predefined sceneries, they tend to fail when interferences from the background are strong and unpredictable. Particularly, for atomic-resolution STEM images, so far there is no well-established algorithm that is robust enough to segment or detect all atomic c…
Carl Zeiss AG（卡尔蔡司公司）；Friedrich Miescher Institute for Biomedical Research（弗里德里希·米歇尔生物医学研究所）；Fondazione Human Technopole（人类技术中心基金会）
In recent years, neural network based image denoising approaches have revolutionized the analysis of biomedical microscopy data. Self-supervised methods, such as Noise2Void (N2V), are applicable to virtually all noisy datasets, even without dedicated training data being available. Arguably, this facilitated the fast and widespread adoption of N2V throughout the life sciences. Unfortunately, the blind-spot training underlying N2V can lead to rather visible checkerboard artifacts, thereby reducing the quality of final predictions considerably. In this work, we present two modifications to the va…
MicroDiffuse3D: A Foundation Model for 3D Microscopy Imaging Restoration
Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA（保罗·G·艾伦计算机科学与工程学院，华盛顿大学，西雅图，华盛顿州，美国）；Department of Chemistry, University of Washington, Seattle, WA, USA（化学系，华盛顿大学，西雅图，华盛顿州，美国）
Computational Cannula Microscopy is a minimally invasive imaging technique that can enable high-resolution imaging deep inside tissue. Here, we apply artificial neural networks to enable fast, power-efficient image reconstructions that are more efficiently scalable to larger fields of view. Specifically, we demonstrate widefield fluorescence microscopy of cultured neurons and fluorescent beads with field of view of 200$μ$m (diameter) and resolution of less than 10$μ$m using a cannula of diameter of only 220$μ$m. In addition, we show that this approach can also be extended to macro-photography.
Computational Cannula Microscopy (CCM) is a high-resolution widefield fluorescence imaging approach deep inside tissue, which is minimally invasive. Rather than using conventional lenses, a surgical cannula acts as a lightpipe for both excitation and fluorescence emission, where computational methods are used for image visualization. Here, we enhance CCM with artificial neural networks to enable 3D imaging of cultured neurons and fluorescent beads, the latter inside a volumetric phantom. We experimentally demonstrate transverse resolution of ~6um, field of view ~200um and axial sectioning of ~…
Fourier ptychographic microscopy allows for the collection of images with a high space-bandwidth product at the cost of temporal resolution. In Fourier ptychographic microscopy, the light source of a conventional widefield microscope is replaced with a light-emitting diode (LED) matrix, and multiple images are collected with different LED illumination patterns. From these images, a higher-resolution image can be computationally reconstructed without sacrificing field-of-view. We use deep learning to achieve single-shot imaging without sacrificing the space-bandwidth product, reducing the acqui…
Oblique plane microscopy, OPM, is a form of lightsheet microscopy that permits volumetric imaging of biological samples at high temporal and spatial resolution. However, the imaging geometry of OPM, and related variants of light sheet microscopy, distorts the coordinate frame of the presented image sections with respect to real space coordinate frame in which the sample is moved to navigate to regions of interest. This makes live viewing and practical operation of such microscopes difficult. We present an open-source software package that utilises GPU acceleration and multiprocessing to transf…
Deep Denoising For Scientific Discovery: A Case Study In Electron Microscopy
Center for Data Science, New York University（纽约大学数据科学中心）；School for Engineering of Matter, Transport, and Energy, ASU（亚利桑那州立大学物质、运输与能源工程学院）；Department of Statistics and Data Science, Cornell University（康奈尔大学统计与数据科学系）；Indian Institute of Technology Madras（印度马德拉斯理工学院）；Howard Hughes Medical Institute（霍华德·休斯医学研究所）；Courant Institute of Mathematical Sciences, NYU（纽约大学柯朗数学科学研究所）
Indian Institute of Technology Madras（印度理工学院马德拉斯分校）；Robert Bosch Center for Data Science and AI（罗伯特·博世数据科学与人工智能中心）
Deep convolutional neural networks (CNNs) for video denoising are typically trained with supervision, assuming the availability of clean videos. However, in many applications, such as microscopy, noiseless videos are not available. To address this, we propose an Unsupervised Deep Video Denoiser (UDVD), a CNN architecture designed to be trained exclusively with noisy data. The performance of UDVD is comparable to the supervised state-of-the-art, even when trained only on a single short noisy video. We demonstrate the promise of our approach in real-world imaging applications by denoising raw vi…
Photoacoustic imaging is a new imaging technology in recent years, which combines the advantages of high resolution and rich contrast of optical imaging with the advantages of high penetration depth of acoustic imaging. Photoacoustic imaging has been widely used in biomedical fields, such as brain imaging, tumor detection and so on. The signal-to-noise ratio (SNR) of image signals in photoacoustic imaging is generally low due to the limitation of laser pulse energy, electromagnetic interference in the external environment and system noise. In order to solve the problem of low SNR of photoacous…
A Zero-Shot Deep Image Prior Framework for Denoising and Deconvolution in Fluorescence Microscopy
Wenzhou Key Laboratory of Biomedical Imaging（温州生物医学成像重点实验室）；Zhejiang Key Laboratory of Soft Matter Biomedical Materials（浙江软物质生物医学材料重点实验室）；Wenzhou Institute, University of Chinese Academy of Sciences（中国科学院大学温州研究院）；Postgraduate Training Base Alliance of Wenzhou Medical University（温州医学院研究生联合培养基地联盟）
We present a new convolutional neural network (CNN) based ImageJ plugin for fluorescence microscopy image denoising with an average improvement of 7.5 dB in peak signal-to-noise ratio (PSNR) and denoising instantly within 80 msec.
Center for Systems Biology Dresden (CSBD)（德累斯顿系统生物学中心）；Max-Planck Institute of Molecular Cell Biology and Genetics（马克斯·普朗克分子细胞生物学与遗传学研究所）
Deep learning (DL) has arguably emerged as the method of choice for the detection and segmentation of biological structures in microscopy images. However, DL typically needs copious amounts of annotated training data that is for biomedical projects typically not available and excessively expensive to generate. Additionally, tasks become harder in the presence of noise, requiring even more high-quality training data. Hence, we propose to use denoising networks to improve the performance of other DL-based image segmentation methods. More specifically, we present ideas on how state-of-the-art sel…
Fluorescence fluctuations-based super-resolution microscopy (FF-SRM) is an emerging field promising low-cost and live-cell compatible imaging beyond the resolution of conventional optical microscopy. A comprehensive overview on how the nature of fluctuations, label density, out-of-focus light, sub-cellular dynamics, and the sample itself influence the reconstruction in FF-SRM is crucial to design appropriate biological experiments. We have experimentally compared several of the recently developed FF-SRM techniques (namely ESI, bSOFI, SRRF, SACD, MUSICAL and HAWK) on widefield fluorescence imag…
University of California San Diego（加州大学圣迭戈分校）；National Taiwan University（台湾大学）；University of Macau（澳门大学）；National Taiwan University Hospital and College of Medicine（台湾大学医学院附设医院暨医学院）
As the rapid growth of high-speed and deep-tissue imaging in biomedical research, it is urgent to find a robust and effective denoising method to retain morphological features for further texture analysis and segmentation. Conventional denoising filters and models can easily suppress perturbative noises in high contrast images. However, for low photon budget multi-photon images, high detector gain will not only boost signals, but also bring huge background noises. In such stochastic resonance regime of imaging, sub-threshold signals may be detectable with the help of noises. Therefore, a denoi…
