解决纳米级的形态学化相变对各种学科的许多科学和工业应用至关重要。通过组合全场传输X射线显微镜(TXM)和X射线吸收附近边缘结构(XANES)的TXM-XANES成像技术是通过获取具有多能量X的一系列显微镜图像来操作的新兴工具 - 接合并配合以获得化学图。然而,由于系统误差和用于快速采集的低曝光照明,其能力受到差的信噪比差的限制。在这项工作中,通过利用TXM-XANES成像数据的内在属性和子空间建模,我们引入了一种简单且坚固的去噪方法来提高图像质量,这使得能够快速和高灵敏度的化学成像。对合成和实时数据集的广泛实验证明了该方法的优越性。
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由智能手机和中端相机捕获的照片的空间分辨率和动态范围有限,在饱和区域中未充满刺激的区域和颜色人工制品中的嘈杂响应。本文介绍了第一种方法(据我们所知),以重建高分辨率,高动态范围的颜色图像,这些颜色来自带有曝光括号的手持相机捕获的原始照相爆发。该方法使用图像形成的物理精确模型来结合迭代优化算法,用于求解相应的逆问题和学习的图像表示,以进行健壮的比对,并以前的自然图像。所提出的算法很快,与基于最新的学习图像恢复方法相比,内存需求较低,并且从合成但逼真的数据终止学习的特征。广泛的实验证明了其出色的性能,具有最多$ \ times 4 $的超分辨率因子在野外拍摄的带有手持相机的真实照片,以及对低光条件,噪音,摄像机摇动和中等物体运动的高度鲁棒性。
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基于深度学习的高光谱图像(HSI)恢复方法因其出色的性能而广受欢迎,但每当任务更改的细节时,通常都需要昂贵的网络再培训。在本文中,我们建议使用有效的插入方法以统一的方法恢复HSI,该方法可以共同保留基于优化方法的灵活性,并利用深神经网络的强大表示能力。具体而言,我们首先开发了一个新的深HSI DeNoiser,利用了门控复发单元,短期和长期的跳过连接以及增强的噪声水平图,以更好地利用HSIS内丰富的空间光谱信息。因此,这导致在高斯和复杂的噪声设置下,在HSI DeNosing上的最新性能。然后,在处理各种HSI恢复任务之前,将提议的DeNoiser插入即插即用的框架中。通过对HSI超分辨率,压缩感测和内部进行的广泛实验,我们证明了我们的方法经常实现卓越的性能,这与每个任务上的最先进的竞争性或甚至更好任何特定任务的培训。
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我们在凸优化和深度学习的界面上引入了一类新的迭代图像重建算法,以启发凸出和深度学习。该方法包括通过训练深神网络(DNN)作为Denoiser学习先前的图像模型,并将其替换为优化算法的手工近端正则操作员。拟议的airi(``````````````''''')框架,用于成像复杂的强度结构,并从可见性数据中扩散和微弱的发射,继承了优化的鲁棒性和解释性,以及网络的学习能力和速度。我们的方法取决于三个步骤。首先,我们从光强度图像设计了一个低动态范围训练数据库。其次,我们以从数据的信噪比推断出的噪声水平来训练DNN Denoiser。我们使用训练损失提高了术语,可确保算法收敛,并通过指示进行即时数据库动态范围增强。第三,我们将学习的DeNoiser插入前向后的优化算法中,从而产生了一个简单的迭代结构,该结构与梯度下降的数据输入步骤交替出现Denoising步骤。我们已经验证了SARA家族的清洁,优化算法的AIRI,并经过DNN训练,可以直接从可见性数据中重建图像。仿真结果表明,AIRI与SARA及其基于前卫的版本USARA具有竞争力,同时提供了显着的加速。干净保持更快,但质量较低。端到端DNN提供了进一步的加速,但质量远低于AIRI。
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Dynamic magnetic resonance image reconstruction from incomplete k-space data has generated great research interest due to its capability to reduce scan time. Never-theless, the reconstruction problem is still challenging due to its ill-posed nature. Recently, diffusion models espe-cially score-based generative models have exhibited great potential in algorithm robustness and usage flexi-bility. Moreover, the unified framework through the variance exploding stochastic differential equation (VE-SDE) is proposed to enable new sampling methods and further extend the capabilities of score-based gener-ative models. Therefore, by taking advantage of the uni-fied framework, we proposed a k-space and image Du-al-Domain collaborative Universal Generative Model (DD-UGM) which combines the score-based prior with low-rank regularization penalty to reconstruct highly under-sampled measurements. More precisely, we extract prior components from both image and k-space domains via a universal generative model and adaptively handle these prior components for faster processing while maintaining good generation quality. Experimental comparisons demonstrated the noise reduction and detail preservation abilities of the proposed method. Much more than that, DD-UGM can reconstruct data of differ-ent frames by only training a single frame image, which reflects the flexibility of the proposed model.
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图像去噪是许多领域下游任务的先决条件。低剂量和光子计数计算断层扫描(CT)去噪可以在最小化辐射剂量下优化诊断性能。监督深层去噪方法是流行的,但需要成对的清洁或嘈杂的样本通常在实践中不可用。受独立噪声假设的限制,电流无监督的去噪方法不能处理与CT图像中的相关噪声。在这里,我们提出了一种基于类似的类似性的无人监督的无监督的深度去噪方法,称为Coxing2Sim,以非局部和非线性方式起作用,不仅抑制独立而且还具有相关的噪音。从理论上讲,噪声2SIM在温和条件下渐近相当于监督学习方法。通过实验,Nosie2SIM从嘈杂的低剂量CT和光子计数CT图像中的内在特征,从视觉上,定量和统计上有效地或甚至优于实际数据集的监督学习方法。 Coke2Sim是一般无监督的去噪方法,在不同的应用中具有很大的潜力。
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A deep learning strategy is developed for fast and accurate gas property measurements using flame emission spectroscopy (FES). Particularly, the short-gated fast FES is essential to resolve fast-evolving combustion behaviors. However, as the exposure time for capturing the flame emission spectrum gets shorter, the signal-to-noise ratio (SNR) decreases, and characteristic spectral features indicating the gas properties become relatively weaker. Then, the property estimation based on the short-gated spectrum is difficult and inaccurate. Denoising convolutional neural networks (CNN) can enhance the SNR of the short-gated spectrum. A new CNN architecture including a reversible down- and up-sampling (DU) operator and a loss function based on proper orthogonal decomposition (POD) coefficients is proposed. For training and testing the CNN, flame chemiluminescence spectra were captured from a stable methane-air flat flame using a portable spectrometer (spectral range: 250 - 850 nm, resolution: 0.5 nm) with varied equivalence ratio (0.8 - 1.2), pressure (1 - 10 bar), and exposure time (0.05, 0.2, 0.4, and 2 s). The long exposure (2 s) spectra were used as the ground truth when training the denoising CNN. A kriging model with POD is trained by the long-gated spectra for calibration, and then the prediction of the gas properties taking the denoised short-gated spectrum as the input: The property prediction errors of pressure and equivalence ratio were remarkably lowered in spite of the low SNR attendant with reduced exposure.
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实际图像的稀疏表示是成像应用的非常有效的方法,例如去噪。近年来,随着计算能力的增长,利用一个或多个图像提取的补丁内冗余的数据驱动策略,以增加稀疏性变得更加突出。本文提出了一种新颖的图像去噪算法,利用了由量子多体理论的图像依赖性的基础。基于补丁分析,通过类似于量子力学的术语来形式化局部图像邻域中的相似度测量,可以有效地保留真实图像的局部结构的量子力学中的相互作用。这种自适应基础的多功能性质将其应用范围扩展到图像无关或图像相关的噪声场景,而无需任何调整。我们对当代方法进行严格的比较,以证明所提出的算法的去噪能力,无论图像特征,噪声统计和强度如何。我们说明了超参数的特性及其对去噪性能的各自影响,以及自动化规则,可以在实验设置中选择其值的自动化规则,其实际设置不可用。最后,我们展示了我们对诸如医用超声图像检测应用等实际图像的方法处理实际图像的能力。
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PtyChography是一种经过良好研究的相成像方法,可在纳米尺度上进行非侵入性成像。它已发展为主流技术,在材料科学或国防工业等各个领域具有各种应用。 PtyChography的一个主要缺点是由于相邻照明区域之间的高重叠要求以实现合理的重建,因此数据采集时间很长。扫描区域之间重叠的传统方法导致与文物的重建。在本文中,我们提出了从深层生成网络采样的数据中稀疏获得或不足采样的数据,以满足Ptychography的过采样要求。由于深度生成网络是预先训练的,并且可以在收集数据时计算其输出,因此可以减少实验数据和获取数据的时间。我们通过提出重建质量与先前提出的和传统方法相比,通过提出重建质量来验证该方法,并评论提出的方法的优势和缺点。
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本文的目的是描述一种从贝叶斯推理的观点来描述一种新的非参数降噪技术,其可以自动提高一个和二维数据的信噪比,例如例如,例如,天文图像和光谱。该算法迭代地评估数据的可能的平滑版本,平滑模型,获得与嘈杂测量统计上兼容的底层信号的估计。迭代基于最后一个顺利模型的证据和$ \ Chi ^ 2 $统计数据,并且我们将信号的预期值计算为整个平滑模型的加权平均值。在本文中,我们解释了算法的数学形式主义和数值实现,我们在利用真正的天文观测的电池对峰值信号,结构相似性指数和时间有效载荷来评估其性能。我们完全自适应的贝叶斯算法用于数据分析(Fabada)产生结果,没有任何参数调谐,与标准图像处理算法相当,其参数基于要恢复的真实信号进行了优化,在实际应用中不可能。诸如BM3D的最先进的非参数方法,以高信噪比提供稍微更好的性能,而我们的算法对于极其嘈杂的数据显着更准确(高于20-40 \%$相对错误,在天文领域特别兴趣的情况)。在此范围内,通过我们的重建获得的残差的标准偏差可能变得比原始测量值低的数量级。复制本报告中显示的所有结果所需的源代码,包括该方法的实现,在https://github.com/pablolyanala/fabada公开使用
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Deep neural networks provide unprecedented performance gains in many real world problems in signal and image processing. Despite these gains, future development and practical deployment of deep networks is hindered by their blackbox nature, i.e., lack of interpretability, and by the need for very large training sets. An emerging technique called algorithm unrolling or unfolding offers promise in eliminating these issues by providing a concrete and systematic connection between iterative algorithms that are used widely in signal processing and deep neural networks. Unrolling methods were first proposed to develop fast neural network approximations for sparse coding. More recently, this direction has attracted enormous attention and is rapidly growing both in theoretic investigations and practical applications. The growing popularity of unrolled deep networks is due in part to their potential in developing efficient, high-performance and yet interpretable network architectures from reasonable size training sets. In this article, we review algorithm unrolling for signal and image processing. We extensively cover popular techniques for algorithm unrolling in various domains of signal and image processing including imaging, vision and recognition, and speech processing. By reviewing previous works, we reveal the connections between iterative algorithms and neural networks and present recent theoretical results. Finally, we provide a discussion on current limitations of unrolling and suggest possible future research directions.
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使用致动万向节的机械图像稳定使得能够捕获长曝光镜头而不会因相机运动而遭受模糊。然而,这些装置通常是物理上繁琐和昂贵的,限制了他们广泛的使用。在这项工作中,我们建议通过输入快速未稳定的相机的输入来数字化地模拟机械稳定的系统。在短曝光的长曝光和低SNR处开发运动模糊之间的折衷,我们通过聚集由未知运动相关的嘈杂短曝光框架来培训估计尖锐的高SNR图像的CNN。我们进一步建议以端到端的方式学习突发的曝光时间,从而平衡噪声和模糊穿过框架。我们展示了这种方法,通过传统的去掩盖单个图像或在合成和实际数据上去除固定曝光突发的传统方法的优势。
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信号处理是几乎任何传感器系统的基本组件,具有不同科学学科的广泛应用。时间序列数据,图像和视频序列包括可以增强和分析信息提取和量化的代表性形式的信号。人工智能和机器学习的最近进步正在转向智能,数据驱动,信号处理的研究。该路线图呈现了最先进的方法和应用程序的关键概述,旨在突出未来的挑战和对下一代测量系统的研究机会。它涵盖了广泛的主题,从基础到工业研究,以简明的主题部分组织,反映了每个研究领域的当前和未来发展的趋势和影响。此外,它为研究人员和资助机构提供了识别新前景的指导。
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最近,从图像中提取的不同组件的低秩属性已经考虑在MAN Hypspectral图像去噪方法中。然而,这些方法通常将3D矩阵或1D向量展开,以利用现有信息,例如非识别空间自相似性(NSS)和全局光谱相关(GSC),其破坏了高光谱图像的内在结构相关性(HSI) )因此导致恢复质量差。此外,由于在HSI的原始高维空间中的矩阵和张量的矩阵和张量的参与,其中大多数受到重大计算负担问题。我们使用子空间表示和加权低级张量正则化(SWLRTR)进入模型中以消除高光谱图像中的混合噪声。具体地,为了在光谱频带中使用GSC,将噪声HSI投影到简化计算的低维子空间中。之后,引入加权的低级张量正则化术语以表征缩减图像子空间中的前导。此外,我们设计了一种基于交替最小化的算法来解决非耦合问题。模拟和实时数据集的实验表明,SWLRTR方法比定量和视觉上的其他高光谱去噪方法更好。
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间接飞行时间(ITOF)相机是一个有希望的深度传感技术。然而,它们容易出现由多路径干扰(MPI)和低信噪比(SNR)引起的错误。传统方法,在去噪后,通过估计编码深度的瞬态图像来减轻MPI。最近,在不使用中间瞬态表示的情况下,共同去噪和减轻MPI的数据驱动方法已经成为最先进的。在本文中,我们建议重新审视瞬态代表。使用数据驱动的Priors,我们将其插入/推断ITOF频率并使用它们来估计瞬态图像。给定直接TOF(DTOF)传感器捕获瞬态图像,我们将我们的方法命名为ITOF2DTOF。瞬态表示是灵活的。它可以集成与基于规则的深度感测算法,对低SNR具有强大,并且可以处理实际上出现的模糊场景(例如,镜面MPI,光学串扰)。我们在真正深度传感方案中展示了先前方法上的ITOF2DTOF的好处。
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We introduce a parametric view of non-local two-step denoisers, for which BM3D is a major representative, where quadratic risk minimization is leveraged for unsupervised optimization. Within this paradigm, we propose to extend the underlying mathematical parametric formulation by iteration. This generalization can be expected to further improve the denoising performance, somehow curbed by the impracticality of repeating the second stage for all two-step denoisers. The resulting formulation involves estimating an even larger amount of parameters in a unsupervised manner which is all the more challenging. Focusing on the parameterized form of NL-Ridge, the simplest but also most efficient non-local two-step denoiser, we propose a progressive scheme to approximate the parameters minimizing the risk. In the end, the denoised images are made up of iterative linear combinations of patches. Experiments on artificially noisy images but also on real-world noisy images demonstrate that our method compares favorably with the very best unsupervised denoisers such as WNNM, outperforming the recent deep-learning-based approaches, while being much faster.
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最近,由于高性能,深度学习方法已成为生物学图像重建和增强问题的主要研究前沿,以及其超快速推理时间。但是,由于获得监督学习的匹配参考数据的难度,对不需要配对的参考数据的无监督学习方法越来越兴趣。特别是,已成功用于各种生物成像应用的自我监督的学习和生成模型。在本文中,我们概述了在古典逆问题的背景下的连贯性观点,并讨论其对生物成像的应用,包括电子,荧光和去卷积显微镜,光学衍射断层扫描和功能性神经影像。
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Low-dose computed tomography (CT) plays a significant role in reducing the radiation risk in clinical applications. However, lowering the radiation dose will significantly degrade the image quality. With the rapid development and wide application of deep learning, it has brought new directions for the development of low-dose CT imaging algorithms. Therefore, we propose a fully unsupervised one sample diffusion model (OSDM)in projection domain for low-dose CT reconstruction. To extract sufficient prior information from single sample, the Hankel matrix formulation is employed. Besides, the penalized weighted least-squares and total variation are introduced to achieve superior image quality. Specifically, we first train a score-based generative model on one sinogram by extracting a great number of tensors from the structural-Hankel matrix as the network input to capture prior distribution. Then, at the inference stage, the stochastic differential equation solver and data consistency step are performed iteratively to obtain the sinogram data. Finally, the final image is obtained through the filtered back-projection algorithm. The reconstructed results are approaching to the normal-dose counterparts. The results prove that OSDM is practical and effective model for reducing the artifacts and preserving the image quality.
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We propose a deep learning method for three-dimensional reconstruction in low-dose helical cone-beam computed tomography. We reconstruct the volume directly, i.e., not from 2D slices, guaranteeing consistency along all axes. In a crucial step beyond prior work, we train our model in a self-supervised manner in the projection domain using noisy 2D projection data, without relying on 3D reference data or the output of a reference reconstruction method. This means the fidelity of our results is not limited by the quality and availability of such data. We evaluate our method on real helical cone-beam projections and simulated phantoms. Our reconstructions are sharper and less noisy than those of previous methods, and several decibels better in quantitative PSNR measurements. When applied to full-dose data, our method produces high-quality results orders of magnitude faster than iterative techniques.
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Motion blur from camera shake is a major problem in videos captured by hand-held devices. Unlike single-image deblurring, video-based approaches can take advantage of the abundant information that exists across neighboring frames. As a result the best performing methods rely on the alignment of nearby frames. However, aligning images is a computationally expensive and fragile procedure, and methods that aggregate information must therefore be able to identify which regions have been accurately aligned and which have not, a task that requires high level scene understanding. In this work, we introduce a deep learning solution to video deblurring, where a CNN is trained end-toend to learn how to accumulate information across frames. To train this network, we collected a dataset of real videos recorded with a high frame rate camera, which we use to generate synthetic motion blur for supervision. We show that the features learned from this dataset extend to deblurring motion blur that arises due to camera shake in a wide range of videos, and compare the quality of results to a number of other baselines 1 .
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