Bilateral filtering smooths images while preserving edges, by means of a nonlinear combination of nearby image values. The method is noniterative, local, and simple. It combines gray levels or colors based on both their geometric closeness and their photometric similarity, and prefers near values to distant values in both domain and range. In contrast with filters that operate on the three bands of a color image separately, a bilateral filter can enforce the perceptual metric underlying the CIE-Lab color space, and smooth colors and preserve edges in a way that is tuned to human perception. Also, in contrast with standard filtering, bilateral filtering produces no phantom colors along edges in color images, and reduces phantom colors where they appear in the original image.

We propose a new measure, the method noise, to evaluate and compare the performance of digital image denoising methods. We first compute and analyze this method noise for a wide class of denoising algorithms, namely the local smoothing filters. Second, we propose a new algorithm, the non local means (NL-means), based on a non local averaging of all pixels in the image. Finally, we present some experiments comparing the NL-means algorithm and the local smoothing filters.

We investigate the properties of a metric between two distributions, the Earth Mover's Distance (EMD), for content-based image retrieval. The EMD is based on the minimal cost that must be paid to transform one distribution into the other, in a precise sense, and was first proposed for certain vision problems by Peleg, Werman, and Rom. For image retrieval, we combine this idea with a representation scheme for distributions that is based on vector quantization. This combination leads to an image comparison framework that often accounts for perceptual similarity better than other previously proposed methods. The EMD is based on a solution to the transportation problem from linear optimization, for which efficient algorithms are available, and also allows naturally for partial matching. It is more robust than histogram matching techniques, in that it can operate on variable-length representations of the distributions that avoid quantization and other binning problems typical of histograms. When used to compare distributions with the same overall mass, the EMD is a true metric. In this paper we focus on applications to color and texture, and we compare the retrieval performance of the EMD with that of other distances.

人类轻松地检测突出物体是几个领域的研究的主题，包括计算机愿景，因为它具有许多应用。然而，突出物体检测对于处理颜色和纹理图像的许多计算机模型仍然是一个挑战。这里，我们通过简单的模型提出了一种新颖和有效的策略，几乎没有内部参数，它为自然图像产生了强大的显着性图。该策略包括将颜色信息集成到局部纹理图案中，以表征颜色微纹理。使用颜色和纹理功能的文献中的大多数模型分别对待它们。在我们的情况下，它是一个简单而强大的LTP（本地三元模式）纹理描述符，应用于允许我们实现这一结束的彩色空间的相对颜色对。每种颜色微纹理由载体表示，载体由Slico（简单的线性迭代聚类与零参数）算法所获得的超像素，这是简单，快速的，表现出最先进的边界依从性。每对颜色微观纹理之间的异常程度是通过FastMAP方法计算的，该方法的快速版本（多维缩放），其在保持其距离时考虑颜色微纹理非线性。这些不同程度的不相似性为每个RGB，HSL，LUV和CMY颜色空间提供了中间显着图。最终的显着图是它们的组合，以利用它们中的每一个的强度。 MAE（平均绝对误差）和F $ _ {\ beta} $衡量我们的显着性图，在复杂的ECSSD数据集上显示，我们的模型既简单又高效，表现出几种最先进的模型。

图像生物标准化倡议（IBSI）旨在通过标准化从图像中提取图像生物标志物（特征）的计算过程来提高射致研究的再现性。我们之前建立了169个常用特征的参考值，创建了标准的射频图像处理方案，并开发了用于垄断研究的报告指南。但是，若干方面没有标准化。在这里，我们提出了在射频中使用卷积图像过滤器的参考手册的初步版本。滤波器，例如高斯滤波器的小波或拉普拉斯，在强调特定图像特征（如边缘和Blob）中发挥重要组成部分。已发现从过滤滤波器响应图派生的功能可重复差。此参考手册构成了持续工作的基础，用于标准化卷积滤波器中的覆盖物中的持续工作，并在这项工作进行时更新。

在计算机视觉，图像处理和计算机图形学中，图像平滑过滤是一个非常基本和重要的任务，并预期拥有良好的边缘保留平滑性。在这里，我们解决了需要改进许多流行的局部平滑滤波器的边缘保存能力的问题。在本文中，我们提出了图像边缘恢复滤波器（ERF）以恢复局部平滑滤波器的输出中的模糊边缘像素清晰。可以在许多局部平滑滤波器（例如盒式滤波器，高斯滤波器，双边滤波器，引导过滤器等）之后实现所提出的滤波器。 “原始局部平滑滤光片+ ERF”的组合具有比原始局部平滑滤波器更好的边缘保持平滑性。图像平滑的实验，图像去噪和图像增强展示了所提出的滤波器的优异边缘恢复能力，以及“原始局部平滑滤光片+ ERF”的组合的良好边缘平滑性。拟议的滤波器将有益于各种各样的应用，鉴于平滑过滤是高频繁使用和基本操作的影响。

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

As an excellent local filter, guided image filters are subject to halo artifacts. In this paper, the algorithm uses gradient information to accurately determine the edge of the image, and uses the weighted information to further accurately distinguish the flat area and edge area of the image. As a result, the edges of the image are sharper and the level of blur in flat areas is reduced, avoiding halo artifacts caused by excessive blurring near edges. Experiments show that the proposed algorithm can better suppress halo artifacts at the edges. The proposed algorithm has good performance in both image denoising and image detail enhancement.

We propose a novel image denoising strategy based on an enhanced sparse representation in transform domain. The enhancement of the sparsity is achieved by grouping similar 2-D image fragments (e.g., blocks) into 3-D data arrays which we call "groups." Collaborative filtering is a special procedure developed to deal with these 3-D groups. We realize it using the three successive steps: 3-D transformation of a group, shrinkage of the transform spectrum, and inverse 3-D transformation. The result is a 3-D estimate that consists of the jointly filtered grouped image blocks. By attenuating the noise, the collaborative filtering reveals even the finest details shared by grouped blocks and, at the same time, it preserves the essential unique features of each individual block. The filtered blocks are then returned to their original positions. Because these blocks are overlapping, for each pixel, we obtain many different estimates which need to be combined. Aggregation is a particular averaging procedure which is exploited to take advantage of this redundancy. A significant improvement is obtained by a specially developed collaborative Wiener filtering. An algorithm based on this novel denoising strategy and its efficient implementation are presented in full detail; an extension to color-image denoising is also developed. The experimental results demonstrate that this computationally scalable algorithm achieves state-of-the-art denoising performance in terms of both peak signal-to-noise ratio and subjective visual quality.

In applications such as social, energy, transportation, sensor, and neuronal networks, high-dimensional data naturally reside on the vertices of weighted graphs. The emerging field of signal processing on graphs merges algebraic and spectral graph theoretic concepts with computational harmonic analysis to process such signals on graphs. In this tutorial overview, we outline the main challenges of the area, discuss different ways to define graph spectral domains, which are the analogues to the classical frequency domain, and highlight the importance of incorporating the irregular structures of graph data domains when processing signals on graphs. We then review methods to generalize fundamental operations such as filtering, translation, modulation, dilation, and downsampling to the graph setting, and survey the localized, multiscale transforms that have been proposed to efficiently extract information from high-dimensional data on graphs. We conclude with a brief discussion of open issues and possible extensions.

着色是一个计算机辅助过程，旨在为灰色图像或视频赋予色彩。它可用于增强黑白图像，包括黑白照片，老式电影和科学成像结果。相反，不着色是将颜色图像或视频转换为灰度。灰度图像或视频是指没有颜色信息的亮度信息的图像或视频。它是一些下游图像处理应用程序的基础，例如模式识别，图像分割和图像增强。与图像脱色不同，视频脱色不仅应考虑每个视频框架中的图像对比度保存，而且还应尊重视频框架之间的时间和空间一致性。研究人员致力于通过平衡时空的一致性和算法效率来开发脱色方法。随着数码相机和手机的流行，研究人员越来越关注图像和视频着色和脱色。本文概述了过去二十年来图像和视频着色和脱色方法的进度。

在本文中，将颜色RGB图像转换为灰度，涵盖了用于将3个颜色通道投射到单个颜色通道的数学运算符的表征。基于以下事实：大多数运营商将$ 256^3 $颜色的每个颜色分配为单个灰度，范围从0到255，他们正在聚集算法，这些算法将颜色总体分配到256个亮度增加的簇中。为了可视化操作员的工作方式，绘制了簇的大小和每个集群的平均亮度。这项工作中引入的均衡模式（EQ）集中在集群大小上，而亮度映射（BM）模式描述了每个群集的CIE L*亮度分布。在线性操作员中发现了三类EQ模式和两类BM模式，定义了6级分类法。考虑到同等重量统一操作员，NTSC标准操作员以及被选为理想的算法，以减轻黑人的面孔以改善当前有偏见的分类器的面部识别，这是在案例研究中应用的理论/方法学框架。发现大多数用于评估颜色转换质量的当前指标更好地评估了两个BM模式类别之一，但是人团队选择的理想操作员属于另一个类别。因此，该警告不要将这些通用指标用于特定目的颜色到灰色转换。应该注意的是，该框架对非线性操作员的最终应用可能会引起新的EQ和BM模式。本文的主要贡献是提供一种工具，以更好地理解灰色转换器的颜色，即使是基于机器学习的灰色转换器，也可以在模型更好地解释的当前趋势中。

花岗岩块的可追溯性包括用代表数值代码的有限数量的颜色带识别每个块。在整个制造过程中，必须多次读取此代码，但其准确性受到人为错误的约束，从而导致可追溯性系统中的故障。提出了计算机视觉系统，以通过颜色检测和相关代码的解密来解决此问题。开发的系统利用了颜色空间转换，以及几个阈值来隔离颜色。实施了计算机视觉方法，以及用于颜色识别的轮廓检测程序。最后，对几何特征的分析用于解密捕获的颜色代码。所提出的算法对在不同的环境条件下拍摄的109张图片进行了训练，并在一​​组21张图像上进行了验证。结果显示出令人鼓舞的结果，在验证过程中的准确率为75.00％。因此，提出的申请可以帮助员工减少产品跟踪中的错误数量。

网状denoising是数字几何处理中的基本问题。它试图消除表面噪声，同时尽可能准确地保留表面固有信号。尽管传统的智慧是基于专门的先验来平稳表面的，但基于学习的方法在概括和自动化方面取得了巨大的成功。在这项工作中，我们对网格denoising的进步进行了全面的综述，其中包含传统的几何方法和最近的基于学习的方法。首先，要熟悉读者的denoising任务，我们总结了网格denoising中的四个常见问题。然后，我们提供了两种现有的脱氧方法的分类。此外，分别详细介绍和分析了三个重要类别，包括优化，过滤器和基于数据驱动的技术。说明了定性和定量比较，以证明最先进的去核方法的有效性。最后，指出未来工作的潜在方向来解决这些方法的共同问题。这项工作还建立了网格denoising基准测试，未来的研究人员将通过最先进的方法轻松方便地评估其方法。

$ \ Texit {Fermi} $数据中的银河系中多余（GCE）的两个领先假设是一个未解决的微弱毫秒脉冲条件（MSP）和暗物质（DM）湮灭。这些解释之间的二分法通常通过将它们建模为两个单独的发射组分来反映。然而，诸如MSP的点源（PSS）在超微弱的极限中具有统计变质的泊松发射（正式的位置，预期每个来源平均贡献远低于一个光子），导致可能提出问题的歧义如排放是否是PS样或性质中的泊松人。我们提出了一种概念上的新方法，以统一的方式描述PS和泊松发射，并且刚刚从此获得的结果中获得了对泊松组件的约束。为了实现这种方法，我们利用深度学习技术，围绕基于神经网络的方法，用于直方图回归，其表达量数量的不确定性。我们证明我们的方法对许多困扰先前接近的系统，特别是DM / PS误操作来稳健。在$ \ texit {fermi} $数据中，我们发现由$ \ sim4 \ times 10 ^ {-11} \ \ text {counts} \ {counts} \ text {counts} \ text {counts} \ \ text {cm} ^ { - 2} \ \ text {s} ^ { - 1} $（对应于$ \ sim3 - 4 $每pL期望计数），这需要$ n \ sim \ mathcal {o}（ 10 ^ 4）$源来解释整个过剩（中位数价值$ n = \文本{29,300} $横跨天空）。虽然微弱，但这种SCD允许我们获得95％信心的Poissonian比赛的约束$ \ eta_p \ leq 66 \％$。这表明大量的GCE通量是由于PSS 。

Stereo matching is one of the most active research areas in computer vision. While a large number of algorithms for stereo correspondence have been developed, relatively little work has been done on characterizing their performance. In this paper, we present a taxonomy of dense, two-frame stereo methods. Our taxonomy is designed to assess the different components and design decisions made in individual stereo algorithms. Using this taxonomy, we compare existing stereo methods and present experiments evaluating the performance of many different variants. In order to establish a common software platform and a collection of data sets for easy evaluation, we have designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can easily be extended to include new algorithms. We have also produced several new multi-frame stereo data sets with ground truth and are making both the code and data sets available on the Web. Finally, we include a comparative evaluation of a large set of today's best-performing stereo algorithms.

在机器学习中调用多种假设需要了解歧管的几何形状和维度，理论决定了需要多少样本。但是，在应用程序数据中，采样可能不均匀，歧管属性是未知的，并且（可能）非纯化；这意味着社区必须适应本地结构。我们介绍了一种用于推断相似性内核提供数据的自适应邻域的算法。从本地保守的邻域（Gabriel）图开始，我们根据加权对应物进行迭代率稀疏。在每个步骤中，线性程序在全球范围内产生最小的社区，并且体积统计数据揭示了邻居离群值可能违反了歧管几何形状。我们将自适应邻域应用于非线性维度降低，地球计算和维度估计。与标准算法的比较，例如使用K-Nearest邻居，证明了它们的实用性。

深度信息在许多图像处理应用程序中是有用的。然而，由于拍摄图像是在2D成像传感器上投射3D场景的过程，因此深度信息嵌入图像中。从图像中提取深度信息是一个具有挑战性的任务。引导原理是由于散焦引起的蓝色水平与物体和焦平面之间的距离有关。基于该原理和广泛使用的假设，即高斯模糊是散焦模糊的良好模型，我们制定了作为高斯模糊分类问题的空间变化散焦模糊的问题。我们通过培训深度神经网络来解决图像补丁中的20级蓝色蓝色之一来解决问题。我们创建了一个超过500000美元的尺寸为32 \ times32 $的数据集，用于培训和测试几种知名网络模型。我们发现MobileNetv2由于其较低的内存要求和高精度而适用于此应用。训练模型用于确定通过施加迭代加权引导滤波器来改进的贴剂模糊。结果是散焦图，其携带每个像素的模糊度的信息。我们将提出的方法与最先进的技术进行比较，我们展示了其在自适应图像增强，散焦倍率和多聚焦图像融合中的成功应用。

We present a machine-learning framework to accurately characterize morphologies of Active Galactic Nucleus (AGN) host galaxies within $z<1$. We first use PSFGAN to decouple host galaxy light from the central point source, then we invoke the Galaxy Morphology Network (GaMorNet) to estimate whether the host galaxy is disk-dominated, bulge-dominated, or indeterminate. Using optical images from five bands of the HSC Wide Survey, we build models independently in three redshift bins: low $(0<z<0.25)$, medium $(0.25<z<0.5)$, and high $(0.5<z<1.0)$. By first training on a large number of simulated galaxies, then fine-tuning using far fewer classified real galaxies, our framework predicts the actual morphology for $\sim$ $60\%-70\%$ host galaxies from test sets, with a classification precision of $\sim$ $80\%-95\%$, depending on redshift bin. Specifically, our models achieve disk precision of $96\%/82\%/79\%$ and bulge precision of $90\%/90\%/80\%$ (for the 3 redshift bins), at thresholds corresponding to indeterminate fractions of $30\%/43\%/42\%$. The classification precision of our models has a noticeable dependency on host galaxy radius and magnitude. No strong dependency is observed on contrast ratio. Comparing classifications of real AGNs, our models agree well with traditional 2D fitting with GALFIT. The PSFGAN+GaMorNet framework does not depend on the choice of fitting functions or galaxy-related input parameters, runs orders of magnitude faster than GALFIT, and is easily generalizable via transfer learning, making it an ideal tool for studying AGN host galaxy morphology in forthcoming large imaging survey.

Objective methods for assessing perceptual image quality have traditionally attempted to quantify the visibility of errors between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a Structural Similarity Index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. 1