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公开(公告)号：US20150071545A1
公开(公告)日：2015-03-12
申请号：US14020969
申请日：2013-09-09
Applicant: Adobe Systems Incorporated
Inventor： Jianchao Yang , Zhe Lin
CPC classification number: G06T5/50 , G06K9/46 , G06K9/4609 , G06K9/4647 , G06K9/6259 , G06T3/4053 , G06T3/4076 , G06T5/002 , G06T5/003 , G06T2207/10024 , G06T2207/20192 , G06T2207/20216
Abstract: Systems and methods are provided for image enhancement using self-examples in combination with external examples. In one embodiment, an image manipulation application receives an input image patch of an input image. The image manipulation application determines a first weight for an enhancement operation using self-examples and a second weight for an enhancement operation using external examples. The image manipulation application generates a first interim output image patch by applying the enhancement operation using self-examples to the input image patch and a second interim output image patch by applying the enhancement operation using external examples to the input image patch. The image manipulation application generates an output image patch by combining the first and second interim output image patches as modified using the first and second weights.
Abstract translation: 提供了使用自身实例与外部示例组合的图像增强的系统和方法。 在一个实施例中，图像处理应用接收输入图像的输入图像块。 图像处理应用使用自身示例确定用于增强操作的第一权重，并且使用外部示例来确定用于增强操作的第二权重。 图像处理应用程序通过使用自身示例将增强操作应用于输入图像贴片和第二中期输出图像贴片，通过使用外部示例将输入图像贴图应用增强操作来生成第一临时输出图像贴片。 图像处理应用程序通过组合使用第一和第二权重修改的第一和第二临时输出图像块来生成输出图像块。
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公开(公告)号：US20140368509A1
公开(公告)日：2014-12-18
申请号：US13920957
申请日：2013-06-18
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Jianchao Yang , Scott D. Cohen
IPC: G06T3/40
CPC classification number: G06T3/40 , G06T3/4053 , G06T3/4076 , G06T5/002 , G06T5/003 , G06T2207/20012 , G09G2340/0407
Abstract: Image upscaling techniques are described. These techniques may include use of iterative and adjustment upscaling techniques to upscale an input image. A variety of functionality may be incorporated as part of these techniques, examples of which include content-adaptive patch finding techniques that may be employed to give preference to an in-place patch to minimize structure distortion. In another example, content metric techniques may be employed to assign weights for combining patches. In a further example, algorithm parameters may be adapted with respect to algorithm iterations, which may be performed to increase efficiency of computing device resource utilization and speed of performance. For instance, algorithm parameters may be adapted to enforce a minimum and/or maximum number to iterations, cease iterations for image sizes over a threshold amount, set sampling step sizes for patches, employ techniques based on color channels (which may include independence and joint processing techniques), and so on.
Abstract translation: 描述了图像升高技术。 这些技术可以包括使用迭代和调整放大技术来升高输入图像。 作为这些技术的一部分，可以并入各种功能，其示例包括可用于优先使用就地补丁以最小化结构失真的内容自适应补片发现技术。 在另一示例中，可以采用内容度量技术来分配用于组合补丁的权重。 在另一示例中，算法参数可以针对算法迭代进行调整，这可以被执行以提高计算设备资源利用率和性能的效率。 例如，算法参数可以适于对迭代执行最小和/或最大数量，停止针对阈值量的图像大小的迭代，设置用于补丁的采样步长，采用基于颜色通道的技术（其可以包括独立性和联合 处理技术）等。
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公开(公告)号：US20180260655A1
公开(公告)日：2018-09-13
申请号：US15979546
申请日：2018-05-15
Applicant: Adobe Systems Incorporated
Inventor： Jianchao Yang , Jonathan Brandt
CPC classification number: G06K9/52 , G06F16/56 , G06K9/46 , G06K9/4642 , G06K9/6201 , G06K9/621 , G06K9/6226 , G06K9/6267 , G06K2009/4666
Abstract: Techniques are disclosed for image feature representation. The techniques exhibit discriminative power that can be used in any number of classification tasks, and are particularly effective with respect to fine-grained image classification tasks. In an embodiment, a given image to be classified is divided into image patches. A vector is generated for each image patch. Each image patch vector is compared to the Gaussian mixture components (each mixture component is also a vector) of a Gaussian Mixture Model (GMM). Each such comparison generates a similarity score for each image patch vector. For each Gaussian mixture component, the image patch vectors associated with a similarity score that is too low are eliminated. The selectively pooled vectors from all the Gaussian mixture components are then concatenated to form the final image feature vector, which can be provided to a classifier so the given input image can be properly categorized.
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公开(公告)号：US09992387B2
公开(公告)日：2018-06-05
申请号：US15063240
申请日：2016-03-07
Applicant: Adobe Systems Incorporated
Inventor： Hailin Jin , Zhuoyuan Chen , Scott D. Cohen , Jianchao Yang , Zhe Lin
CPC classification number: H04N5/21 , G06T3/4053 , G06T3/4076 , G06T5/002 , G06T5/003 , G06T5/20 , G06T5/50 , G06T7/269 , G06T2207/10016 , G06T2207/20021 , G06T2207/20032 , G06T2207/20182 , G06T2207/20201 , H04N5/144 , H04N5/145 , H04N5/147
Abstract: In techniques for video denoising using optical flow, image frames of video content include noise that corrupts the video content. A reference frame is selected, and matching patches to an image patch in the reference frame are determined from within the reference frame. A noise estimate is computed for previous and subsequent image frames relative to the reference frame. The noise estimate for an image frame is computed based on optical flow, and is usable to determine a contribution of similar motion patches to denoise the image patch in the reference frame. The similar motion patches from the previous and subsequent image frames that correspond to the image patch in the reference frame are determined based on the optical flow computations. The image patch is denoised based on an average of the matching patches from reference frame and the similar motion patches determined from the previous and subsequent image frames.
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公开(公告)号：US20180005354A1
公开(公告)日：2018-01-04
申请号：US15707418
申请日：2017-09-18
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Jianchao Yang , Hailin Jin , Xin Lu
CPC classification number: G06T5/005 , G06K9/00664 , G06K9/6218 , G06T5/002 , G06T2207/20081 , G06T2207/20084
Abstract: Patch partition and image processing techniques are described. In one or more implementations, a system includes one or more modules implemented at least partially in hardware. The one or more modules are configured to perform operations including grouping a plurality of patches taken from a plurality of training samples of images into respective ones of a plurality of partitions, calculating an image processing operator for each of the partitions, determining distances between the plurality of partitions that describe image similarity of patches of the plurality of partitions, one to another, and configuring a database to provide the determined distance and the image processing operator to process an image in response to identification of a respective partition that corresponds to a patch taken from the image.
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Patent Agency Ranking
公开(公告)号：US09524449B2
公开(公告)日：2016-12-20
申请号：US14107191
申请日：2013-12-16
Applicant: Adobe Systems Incorporated
Inventor： Jianchao Yang , Guang Chen , Hailin Jin , Jonathan Brandt , Elya Shechtman , Aseem Omprakash Agarwala
CPC classification number: G06K9/6267 , G06K9/6219 , G06K9/6282 , G06K9/6807
Abstract: Example systems and methods for classifying visual patterns into a plurality of classes are presented. Using reference visual patterns of known classification, at least one image or visual pattern classifier is generated, which is then employed to classify a plurality of candidate visual patterns of unknown classification. The classification scheme employed may be hierarchical or nonhierarchical. The types of visual patterns may be fonts, human faces, or any other type of visual patterns or images subject to classification.
Abstract translation: 提出了将视觉模式分类为多个类的示例系统和方法。 使用已知分类的参考视觉图案，生成至少一个图像或视觉模式分类器，然后将其用于对未知分类的多个候选视觉图案进行分类。 所使用的分类方案可以是分层的或非分层的。 视觉图案的类型可以是字体，人脸或任何其他类型的可分类的视觉图案或图像。
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公开(公告)号：US09508126B2
公开(公告)日：2016-11-29
申请号：US14624116
申请日：2015-02-17
Applicant: Adobe Systems Incorporated
Inventor： Jianchao Yang , Jue Wang
CPC classification number: G06T3/40 , G06T5/003 , G06T5/10 , G06T2207/10024
Abstract: Techniques are disclosed for removing haze from an image or video by constraining the medium transmission used in a haze image formation model. In particular, a de-hazed scene, which is a function of a medium transmission, is constrained to be greater than or equal to a fractionally scaled variant of the input image. The degree to which the input image is scaled can be selected manually or by using machine learning techniques on a pixel-by-pixel basis to achieve visually pleasing results. Next, the constrained medium transmission is filtered to be locally smooth with sharp discontinuities along image edge boundaries to preserve scene depth. This filtering results in a prior probability distribution that can be used for haze removal in an image or video frame. The input image is converted to gamma decoded sRGB linear space prior to haze removal, and gamma encoded into sRGB space after haze removal.
Abstract translation: 公开了通过限制在雾度图像形成模型中使用的介质传输来从图像或视频中去除雾度的技术。 特别地，作为中等传输的函数的去雾化场景被限制为大于或等于输入图像的分数缩放变体。 可以手动选择输入图像的缩放程度，或者通过逐个像素地使用机器学习技术来获得视觉上令人满意的结果。 接下来，受限介质传输被过滤以局部平滑，沿着边缘边缘具有尖锐的不连续性，以保持场景深度。 该过滤导致可用于图像或视频帧中的雾度去除的先验概率分布。 在去除雾度之前，将输入图像转换为伽玛解码的sRGB线性空间，并在除去雾度之后将伽马编码为sRGB空间。
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公开(公告)号：US09436893B2
公开(公告)日：2016-09-06
申请号：US14091972
申请日：2013-11-27
Applicant: Adobe Systems Incorporated
Inventor： Jianchao Yang , Zhaowen Wang , Zhe Lin , Jonathan Brandt
IPC: G06K9/62
CPC classification number: G06K9/6269 , G06K9/6235
Abstract: A system and method for distributed similarity learning for high-dimensional image features are described. A set of data features is accessed. Subspaces from a space formed by the set of data features are determined using a set of projection matrices. Each subspace has a dimension lower than a dimension of the set of data features. Similarity functions are computed for the subspaces. Each similarity function is based on the dimension of the corresponding subspace. A linear combination of the similarity functions is performed to determine a similarity function for the set of data features.
Abstract translation: 描述了用于高维图像特征的分布式相似性学习的系统和方法。 访问一组数据功能。 使用一组投影矩阵来确定由该组数据特征形成的空间的子空间。 每个子空间的尺寸小于数据特征集合的维度。 为子空间计算相似度函数。 每个相似度函数基于相应子空间的维度。 执行相似度函数的线性组合以确定该组数据特征的相似度函数。
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公开(公告)号：US09286540B2
公开(公告)日：2016-03-15
申请号：US14085488
申请日：2013-11-20
Applicant: Adobe Systems Incorporated
Inventor： Zhe Lin , Jianchao Yang , Hailin Jin , Xin Lu
CPC classification number: G06K9/4642 , G06K9/6273 , G06K9/6276
Abstract: In techniques for fast dense patch search and quantization, partition center patches are determined for partitions of example image patches. Patch groups of an image each include similar image patches and a reference image patch that represents a respective patch group. A partition center patch of the partitions is determined as a nearest neighbor to the reference image patch of a patch group. The partition center patch can be determined based on a single-nearest neighbor (1-NN) distance determination, and the determined partition center patch is allocated as the nearest neighbor to the similar image patches in the patch group. Alternatively, a group of nearby partition center patches are determined as the nearest neighbors to the reference image patch based on a k-nearest neighbor (k-NN) distance determination, and the nearest neighbor to each of the similar image patches in the patch group is determined from the nearby partition center patches.
Abstract translation: 在快速密集补丁搜索和量化的技术中，为示例图像补丁的分区确定分区中心补丁。 图像的补丁组各自包括相似的图像补丁和代表相应补丁组的参考图像补丁。 分区的分区中心补丁被确定为补丁组的参考图像补丁的最近邻。 可以基于单个最近邻居（1-NN）距离确定来确定分区中心补丁，并且将所确定的分区中心补丁分配为补丁组中的相似图像补丁的最近邻。 或者，基于k个最近邻（k-NN）距离确定，将一组附近的分区中心补丁确定为参考图像补丁的最近邻，并且补丁组中每个相似图像补丁的最近邻 是从附近的分区中心补丁确定的。
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公开(公告)号：US20150262336A1
公开(公告)日：2015-09-17
申请号：US14205027
申请日：2014-03-11
Applicant: Adobe Systems Incorporated
Inventor： Hailin Jin , Zhuoyuan Chen , Scott D. Cohen , Jianchao Yang , Zhe Lin
CPC classification number: H04N5/21 , G06T3/4053 , G06T3/4076 , G06T5/002 , G06T5/003 , G06T5/20 , G06T5/50 , G06T7/269 , G06T2207/10016 , G06T2207/20021 , G06T2207/20032 , G06T2207/20182 , G06T2207/20201 , H04N5/144 , H04N5/145 , H04N5/147
Abstract: In techniques for video denoising using optical flow, image frames of video content include noise that corrupts the video content. A reference frame is selected, and matching patches to an image patch in the reference frame are determined from within the reference frame. A noise estimate is computed for previous and subsequent image frames relative to the reference frame. The noise estimate for an image frame is computed based on optical flow, and is usable to determine a contribution of similar motion patches to denoise the image patch in the reference frame. The similar motion patches from the previous and subsequent image frames that correspond to the image patch in the reference frame are determined based on the optical flow computations. The image patch is denoised based on an average of the matching patches from reference frame and the similar motion patches determined from the previous and subsequent image frames.
Abstract translation: 在使用光流的视频去噪的技术中，视频内容的图像帧包括破坏视频内容的噪声。 选择参考帧，并且从参考帧内确定参考帧中的图像块的匹配补丁。 针对相对于参考帧的先前和后续图像帧计算噪声估计。 基于光流计算图像帧的噪声估计，并且可用于确定类似运动补丁对参考帧中的图像补丁进行去噪的贡献。 基于光流计算确定与参考帧中的图像块相对应的来自先前和后续图像帧的类似运动补丁。 基于来自参考帧的匹配补丁的平均值和从先前和后续图像帧确定的类似运动补丁，去除图像补丁。
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