CN104685513A - 根据使用阵列源捕捉的低分辨率图像的基于特征的高分辨率运动估计 - Google Patents

根据使用阵列源捕捉的低分辨率图像的基于特征的高分辨率运动估计 Download PDF

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CN104685513A
CN104685513A CN201380049828.XA CN201380049828A CN104685513A CN 104685513 A CN104685513 A CN 104685513A CN 201380049828 A CN201380049828 A CN 201380049828A CN 104685513 A CN104685513 A CN 104685513A
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D·勒勒斯古
A·K·杰恩
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Fotonation Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformation in the plane of the image
    • G06T3/40—Scaling the whole image or part thereof
    • G06T3/4053—Super resolution, i.e. output image resolution higher than sensor resolution
    • G06T3/4069—Super resolution, i.e. output image resolution higher than sensor resolution by subpixel displacement
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/60—Editing figures and text; Combining figures or text
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformation in the plane of the image
    • G06T3/40—Scaling the whole image or part thereof
    • G06T3/4053—Super resolution, i.e. output image resolution higher than sensor resolution
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/13—Edge detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
    • H—ELECTRICITY
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    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H04N19/503—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving temporal prediction
    • H04N19/51—Motion estimation or motion compensation
    • H04N19/53—Multi-resolution motion estimation; Hierarchical motion estimation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H04N19/503—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving temporal prediction
    • H04N19/51—Motion estimation or motion compensation
    • H04N19/537—Motion estimation other than block-based
    • H04N19/54—Motion estimation other than block-based using feature points or meshes
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H04N19/59—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving spatial sub-sampling or interpolation, e.g. alteration of picture size or resolution
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/95—Computational photography systems, e.g. light-field imaging systems
    • H04N23/951—Computational photography systems, e.g. light-field imaging systems by using two or more images to influence resolution, frame rate or aspect ratio
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20212—Image combination
    • G06T2207/20221—Image fusion; Image merging

Abstract

根据本发明实施例的系统和方法使能了根据使用阵列相机捕捉的低分辨率图像的基于特征的高分辨率运动估计。一个实施例包括对于低分辨率图像序列执行特征检测(122，124)以确定(126)低分辨率图像序列中的多个检测到的特征的初始位置，其中至少一个低分辨率图像序列是从不同角度捕捉的低分辨率图像序列的集合的一部分。该方法还包括合成(128)高分辨率图像部分，其中合成的高分辨率图像部分包含所确定的来自低分辨率图像序列的多个检测到的特征。该方法还包括在高分辨率图像部分内执行特征检测(129)以确定检测到的特征的高精度位置，以及使用所述多个检测到的特征的高精度位置来估计相机运动。

Description

根据使用阵列源捕捉的低分辨率图像的基于特征的高分辨率运动估计
技术领域
本发明一般地涉及数字图像中的特征检测并且更具体地涉及使用阵列相机和超分辨率来提高特征检测的性能和效率。
背景技术
在数字成像和计算机视觉中，特征检测是基础操作，其通常是诸如运动估计、稳定、图像配准、对象跟踪和深度估计之类的基于特征的算法的预备步骤。这些算法的性能敏感地取决于特征点估计的质量。
各种类型的图像特征包括边缘、角或兴趣点以及感兴趣的斑点(blob)或区域。边缘是存在两个图像区域之间的边界之处的点，并且通常被定义为图像中具有强梯度幅值的点集合。角或兴趣点可以指代图像中具有局部二维结构的点状特征。角可以是两个边缘的交叉点，或者这样一个点，对于该点在该点的局部邻域中存在两个主要且不同的边缘方向。兴趣点可以是具有良好定义的位置并且可以被鲁棒地检测到的点，诸如角或者局部最大或最小强度的隔离点。感兴趣的斑点或区域可以描述关于区域的一类图像结构，其经常包含优选点。在这个意义上，许多斑点检测器也可以被看作兴趣点操作器。
角检测的一种简单但是计算密集的方法是使用相关。其他方法包括Harris&Stephens角检测算法，该算法使用平方差之和来考虑角分数关于方向的微分。
实现有效的特征检测部分地依赖于向特征检测器提供高质量数据，即，一个或多个高分辨率图像。
发明内容
根据本发明实施例的系统和方法使能了根据使用阵列相机捕捉的低分辨率图像的基于特征的高分辨率运动估计。一个实施例包括：使用通过软件而被配置的处理器对于低分辨率图像序列执行特征检测以确定(identify)低分辨率图像序列中的多个检测到的特征的初始位置，其中至少一个低分辨率图像序列是从不同角度捕捉的低分辨率图像序列的集合的一部分；使用通过软件而被配置的处理器根据从不同角度捕捉的低分辨率图像序列的集合来合成高分辨率图像部分以执行超分辨率处理，其中合成的高分辨率图像部分包含所确定的来自所述低分辨率图像序列中的多个检测到的特征；使用通过软件而被配置的处理器在所述高分辨率图像部分内执行特征检测以确定所述多个检测到的特征的高精度位置；以及使用通过软件而被配置的处理器来利用所述多个检测到的特征的高精度位置估计相机运动。
在另一实施例中，其中所述检测到的特征是从由以下各项组成的组中选择的：边缘、角和斑点。
在又一实施例中，对于低分辨率图像序列执行特征检测还包括在来自所述低分辨率图像序列的第一帧中检测特征的位置，以及在来自所述低分辨率图像序列的第二帧中检测特征的位置。
在又一实施例中，在来自所述低分辨率图像序列的第二帧中检测特征的位置进一步包括搜索所述低分辨率图像序列中的第二帧以定位在所述低分辨率图像序列中的第一帧中检测到的特征。
在又一实施例中，搜索来自所述低分辨率图像序列的第二帧以定位在所述低分辨率图像序列中的第一帧中检测到的特征进一步包括：确定在所述低分辨率图像序列中的第一帧中的给定特征的位置周围的图像分块(patch)，以及使用匹配标准来搜索所述低分辨率图像序列中的第二帧以寻找相对应的图像分块。
在又一实施例中，所述匹配标准包括使误差距离度量最小化。
在又一实施例中，在高分辨率图像部分内执行特征检测以确定所述多个检测到的特征的高精度位置进一步包括：使用包含来自所述低分辨率图像序列中的第一帧的特征的高分辨率图像区域来搜索包含来自所述低分辨率图像序列中的第二帧的特征的高分辨率图像区域以寻找来自所述低分辨率图像序列中的第一帧的特征。
在又一实施例中，搜索包含来自所述低分辨率图像序列中的第二帧的特征的高分辨率图像区域以寻找来自所述低分辨率图像序列中的第一帧的特征进一步包括：使用匹配标准将包含来自所述低分辨率图像序列中的第二帧的特征的高分辨率图像区域与包含来自所述低分辨率图像序列中的第一帧的特征的高分辨率图像区域相比较。
在又一实施例中，所述匹配标准包括使误差距离度量最小化。
在又一实施例中，处理器是还包括成像器阵列的阵列相机的一部分，所述方法还包括使用所述成像器阵列从不同角度捕捉所述低分辨率图像序列的集合中的至少多个低分辨率图像序列。
在又一实施例中，所述多个检测到的特征的高精度位置以相对于所述低分辨率图像序列中的帧的像素的大小的子像素精度估计特征位置。
在又一实施例中，对于低分辨率图像序列执行特征检测还包括对于多个低分辨率图像序列执行特征检测，其中每一个序列来自不同角度。
在又一实施例中，所述低分辨率图像序列的集合包括在多个不同颜色通道中捕捉的低分辨率图像序列，并且对于低分辨率图像序列执行特征检测进一步包括对于每一个颜色通道中的至少一个低分辨率图像序列执行特征检测。
另一个实施例包括成像器阵列，以及通过软件而被配置为控制所述成像器阵列的各种工作参数的处理器。此外，所述软件还将所述处理器配置为：捕捉使用所述成像器阵列从不同角度捕捉的低分辨率图像序列的集合；对于所述低分辨率图像序列的集合中的低分辨率图像序列执行特征检测以确定所述低分辨率图像序列中的多个检测到的特征的初始位置；根据从不同角度捕捉的低分辨率图像序列的集合来合成高分辨率图像部分，其中所述高分辨率图像部分包含所确定的来自所述低分辨率图像序列的多个检测到的特征；在所述高分辨率图像部分内执行特征检测以确定所述多个检测到的特征的高精度位置；以及使用所述多个检测到的特征的高精度位置来估计相机运动。
在另一实施例中，所述检测到的特征是从由以下各项组成的组中选择的：边缘、角和斑点。
在又一实施例中，所述处理器还被配置为通过在来自所述低分辨率图像序列的第一帧中检测特征的位置以及在来自所述低分辨率图像序列的第二帧中检测特征的位置来对于低分辨率图像序列执行特征检测。
在又一实施例中，所述处理器通过软件还被配置为：通过搜索所述低分辨率图像序列中的第二帧以定位在所述低分辨率图像序列中的第一帧中检测到的特征来在所述低分辨率图像序列中的第二帧中检测特征的位置。
在又一实施例中，所述处理器通过软件还被配置为通过以下操作搜索所述低分辨率图像序列中的第二帧以定位在所述低分辨率图像序列中的第一帧中检测到的给定特征：确定在所述低分辨率图像序列中的第一帧中的给定特征的位置周围的图像分块；以及使用匹配标准来搜索所述低分辨率图像序列中的第二帧以寻找相对应的图像分块。
在又一实施例中，所述匹配标准包括使误差距离度量最小化。
在又一实施例中，所述处理器通过软件还被配置为：通过使用包含来自所述低分辨率图像序列中的第一帧的特征的高分辨率图像区域来搜索包含来自所述低分辨率图像序列中的第二帧的特征的高分辨率图像区域以寻找来自所述低分辨率图像序列中的第一帧的特征，在高分辨率图像部分内执行特征检测以确定所述多个检测到的特征的高精度位置。
在又一实施例中，所述处理器通过软件还被配置为：通过使用匹配标准将包含来自所述低分辨率图像序列中的第二帧的特征的高分辨率图像区域与包含来自所述低分辨率图像序列中的第一帧的特征的高分辨率图像区域相比较，搜索包含来自所述低分辨率图像序列中的第二帧的特征的高分辨率图像区域以寻找来自所述低分辨率图像序列中的第一帧的特征。
在又一实施例中，所述匹配标准包括使误差距离度量最小化。
在又一实施例中，所述多个检测到的特征的高精度位置以相对于所述低分辨率图像序列中的帧的像素的大小的子像素精度估计特征位置。
在又一实施例中，所述成像器阵列中的多个成像器感测不同波长的光，并且所述低分辨率图像序列的集合包括在多个不同颜色通道中捕捉的低分辨率图像序列。
在又一实施例中，所述处理器通过软件还被配置为：通过对于每一个颜色通道中的至少一个低分辨率图像序列执行特征检测来对于低分辨率图像序列执行特征检测。
在又一实施例中，所述处理器通过软件还被配置为：通过对于多个低分辨率图像序列执行特征检测来对于低分辨率图像序列执行特征检测，其中每一个序列来自不同角度。
附图说明
图1a和1b是示出特征点和该特征点在稍后时间的移位位置的代表性低分辨率图像。
图2a和2b是示出特征点和该特征点在稍后时间的移位位置的代表性高分辨率图像。
图3是可以在根据本发明实施例的各种阵列相机配置中使用的阵列相机架构的概念例示。
图4是示出根据本发明一个实施例的使用利用超分辨率处理获得的高分辨率图像部分来细化(refine)特征对应关系的处理的流程图。
图5a和5b是示出特征点和该特征点周围的像素的确定邻域以及该特征点在稍后时间的移位位置的通过阵列相机捕捉的代表性低分辨率图像。
图6a和6b是示出使用超分辨率处理获得的每一个特征点周围的像素的高分辨率邻域以及该特征点在稍后时间的移位位置的代表性图像。
具体实施方式
现在转到附图，根据本发明实施例根据使用阵列相机捕捉的低分辨率图像的基于特征的高分辨率运动估计的系统和方法被示出。利用旧有相机顺序地捕捉的两个图像可以反映由相机的运动引起的相对位移。可以根据这些图像来恢复该相机运动，或者等价的，场景的3D结构。朝这些目标的第一步骤是执行这两个图像之间的特征匹配，其初始步骤是独立地检测一个或两个图像中的特征。然后通过选择每一个特征周围的分块并且使该分块与另一图像中的候选分块之间的诸如归一化互相关之类的误差距离度量最小化来在图像特征之间形成初始对应关系。然后可以针对给定运动模型使用诸如随机样本一致性(RANSAC)之类的验证过程来细化特征之间的初始对应关系集。
阵列相机提供了与旧有相机相比的多个优点和特征。阵列相机通常包含两个或多个成像器，其中的每一个通过单独的透镜系统来接收光。成像器操作来从略微不同的角度捕捉场景的图像。由这些成像器捕捉的图像通常被称作低分辨率图像并且超分辨率处理可以被用来根据低分辨率图像的子集来合成高分辨率或超分辨率的图像。两个低分辨率图像的最小值的比较可以提供在超分辨率处理中使用的视差信息。术语低分辨率和高分辨率是相对使用的并且不是用来指示任何特定图像分辨率。阵列中的成像器可以感测不同波长的光(例如，红、绿、蓝、红外)，这可以提高在不同光照条件下的性能以及对使用阵列捕捉的图像的超分辨率处理的性能。可以使用由阵列相机捕捉的低分辨率图像来生成更高分辨率图像的超分辨率处理包括在题为“Systems and Methods for Synthesizing High Resolution ImagesUsing Super-Resolution Processes”的序号为12/967,807的美国专利申请中公开的那些，该申请的公开内容通过引用而整体结合于此。
由阵列相机的成像器捕捉的低分辨率图像的序列如在旧有相机中一样通常包含由相机运动引起的帧之间的时间位移，但是也包含由视差引起的每一个帧的阵列的组成图像(即由阵列中的每一个成像器捕捉的低分辨率图像)之间的帧内位移。因为阵列中的每一个成像器的偏移距离是已知的，因此视差位移可以被计算并且被用来配准图像以执行超分辨率处理。
在若干实施例中，可以对使用由阵列相机捕捉的低分辨率图像生成的超分辨率图像的序列执行特征检测。以这种方式执行特征检测可以产生特征位置的子像素估计(即估计具有比用来捕捉低分辨率图像的阵列相机中的传感器的像素的大小更小的精度)。参考图1a，利用形成边缘101和102的像素示出了低分辨率图像。如上面讨论的，边缘是可以被定义为两个图像区域之间的边界的特征并且角可以被定义为两个边缘的交叉点。点103被确定为边缘101和102的交叉点处的角。在图1b中示出的时间t+1处的后一图像中，点104被确定为对应于点103的角。
通过应用诸如在美国专利申请序号12/967,807中描述的处理中的一个的超像素处理，特征检测的准确性可以被提高。通过向包括在图1a和1b中示出的低分辨率图像在内的低分辨率图像应用超像素处理而获得的更高像素图像分别在图2a和2b中示出。可以以对更低像素图像执行诸如运动估计、稳定、图像配准、对象跟踪和深度估计之类的基于特征的算法相同的方式对更高分辨率图像执行它们，益处是子像素准确性。然而，对整个图像执行超分辨率处理可能需要使用功率和计算资源的相当大的计算，并且可能无法在诸如移动平台之类的具有有限处理能力的设备上最佳执行。
在本发明的许多实施例中，通过最初确定低分辨率图像中的特征的位置然后选择性地执行超分辨率处理来以更高分辨率获得低分辨率图像中包含所确定特征的部分，可以以计算高效的方式实现精确的特征检测。通过仅执行超分辨率处理以获得在特征检测中使用的超分辨率图像的部分，可以在保留增大的准确性的益处的同时以更高速度(即以更少的计算)执行特征检测。以这种方式，可以以计算高效的方式执行诸如(但不限于)视频捕捉期间的实时图像稳定之类的依赖于特征识别的高级功能。下面进一步讨论了根据本发明实施例的用来获得用于执行特征检测的高分辨率图像部分的超分辨率处理的使用以及阵列相机。
阵列相机架构
可以在根据本发明实施例的各种阵列相机配置中使用的阵列相机架构在图3中示出。阵列相机100包括成像器阵列106，其被连接到处理器108。阵列106中的成像器110按照5x 5正方形被均匀地间隔开。在其他实施例中，成像器可以具有不同的间隔或者可以按照其他朝向被布置在阵列中。处理器108是控制成像器阵列106的各种工作参数的硬件、软件、固件或其组合。处理器108还可以用来使用超像素处理来处理从成像器阵列106接收到的图像以产生合成的更高分辨率图像，或者将这些图像传送到其他硬件、软件、固件或其组合以处理这些图像。在若干实施例中，阵列相机包括包含图像处理应用的存储器，该图像处理应用可以被用来使用下面描述的技术中的任一个利用由阵列相机捕捉的低分辨率图像来执行基于特征的高分辨率运动估计。
尽管在图3中示出了特定架构，但是根据本发明的实施例可以使用使能低分辨率图像的捕捉和超分辨率处理的应用以产生合成的高分辨率图像的各种架构中的任一个。
获得高分辨率图像部分
在本发明的许多实施例中，超分辨率被执行以获得与低分辨率图像中包括确定特征的部分相对应的高分辨率图像部分。一旦高分辨率图像部分被获得，就可以在更高分辨率细化使用低分辨率图像初始确定的特征对应关系。示出根据本发明实施例使用利用超分辨率处理获得的高分辨率图像部分来细化特征对应关系的处理120的流程图在图4中被示出。贯穿以下讨论，参照在图5a和5b中示出的样本图像。
在由阵列相机中的成像器捕捉的第一低分辨率图像上运行特征检测算法以确定(122)图像中的特征。来自阵列相机中的成像器中的任一个成像器的图像可以被选择，只要用来执行特征检测的第二低分辨率图像是从同一成像器捕捉的即可。在许多实施例中，可以对于由多个相机捕捉的图像序列来执行特征检测以获得关于特征位置的附加信息。在多个实施例中，阵列相机包括捕捉不同颜色通道中的图像的相机，并且阵列相机对于由多个相机中的相机捕捉的图像序列来执行特征检测。在某些实施例中，对于由每一个颜色通道中的至少一个相机捕捉的图像序列来执行特征检测。
如上面讨论的，可以在低分辨率图像中检测到的特征的类型可以包括(但不限于)边缘、角和斑点。通常，特征检测算法基于特征的定义来确定一类特征。诸如Harris&Stephens检测算法之类的角检测器可以被用来确定角。在Harris&Stephens算法中，图像分块在指定区域上被考虑并且被移位。角以两个分块之间在所有方向上的平方差的加权和的大变化为特征。
参考图5a，在时间t捕捉的低分辨率图像被示出。使用角检测算法将图像中的点140确定为角。类似地，在第二低分辨率图像上运行特征检测算法以确定(124)图像中的特征。某个稍后时间t+1处的第二图像在图5b中被示出。第二图像中的点142被确定为角。
在本发明的一些实施例中，第一帧中的每一个特征在可能的情况下被匹配到(即确定为对应于)第二帧中的特征。如果该特征已经移动出第二帧或者已经移动了显著距离，则该初始对应关系可能是不可能的。在其他实施例中，特征在低分辨率图像中不被匹配，而是在对低分辨率图像的多个部分(帧)执行超分辨率之后被匹配。
在每一个帧中选择(126)每一个特征周围的像素的邻域。这种邻域的合适尺寸可以是20像素乘20像素(20x 20)至60像素x 60像素(60x 60)，然而更小或者更大的邻域是可能的并且可以由对图像执行计算的计算平台的限制决定。另外，邻域可以是任何形状的并且不一定是正方形的。特征通常可以落入邻域的边界内，但是不一定在邻域中央。
参考图5a和5b，在第一帧中的点140周围选择20x 20像素的邻域144。类似地，在第二帧中的点142周围选择20x 20像素邻域146。对于每一个邻域，使用视差信息来执行(128)超分辨率处理以在由相机阵列中的其他成像器捕捉的低分辨率图像中应用任何必要的像素移位。可以使用由阵列相机生成的低分辨率图像的子集来应用超分辨率处理。如上面讨论的，阵列相机利用多个成像器同时捕捉图像。从不同角度获得的低分辨率图像的子集(即最小为两个)提供可以在超分辨率处理中使用的视差信息。合适的超分辨率处理可以包括(但不限于)在美国专利申请序号12/967,807(通过以上引用而被结合)中公开的那些。
如上面讨论的，由于视差的影响在由相机阵列的成像器捕捉的低分辨率图像中存在差异。为了合成包含指定邻域的高分辨率图像部分，通过确定图像之间的视差并且向低分辨率图像的像素应用适当像素移位来虑及视差的影响。像素移位可包括将像素移动到指定邻域中和将像素移出指定邻域。相应地，尽管合成的高分辨率图像中的特定像素邻域被确定，但是超分辨率算法在校正视差之后可以使用来自邻域外部的低分辨率图像的像素并且排除来自邻域内的低分辨率图像的像素。因此，用来使用超分辨率处理获得高分辨率图像的指定邻域的来自低分辨率图像的输入像素不限于通过在初始低分辨率图像对内执行特征检测来确定的指定邻域内的像素。指定邻域只是引导对于要使用的低分辨率像素的超分辨率处理以合成高分辨率图像中对应于指定邻域的部分。用于经由可在超分辨率处理中使用的阵列相机使用视差计算获得距离和其他信息的方法包括在题为“Systems andMethods for Parallax Detection and Correction in images capturedUsing Array Cameras”的序号为61/691,666的美国专利申请中公开的那些，该申请的公开内容通过引用而被整体结合于此。
由此得到的帧在图6a和6b中示出。指定邻域144的超分辨率给出在图6a中示出的高分辨率40x 40邻域144’和点140’。低分辨率邻域146的超分辨率给出在图6b中示出的高分辨率40x 40邻域146’和点142’。
在高分辨率邻域146’中，点142’的位置略微在其在低分辨率邻域146中出现之处的右边。因为超分辨率恢复图像的实际高频内容，因此更高分辨率邻域提供该点的实际位置的“更真实”表示。在本发明的许多实施例中，高分辨率邻域144’和146’中的点140’和142’的新计算的位置可以在匹配(即确定其之间的对应关系)点140’和142’时使用。
参考图6a和6b，在第一点140’和第二点142’之间形成初始对应关系。可以使用各种方法来建立对应关系。一种常见方法是选择每一个点周围的分块并且使该分块与另一图像中的候选分块之间的诸如(但不限于)归一化互相关之类的误差距离度量最小化。点142’从而被确定为对应于前一帧中的点140’。用于寻找对应关系的其他方法在本领域中是已知的。
使用初始对应关系，可以对图像执行包括(但不限于)运动估计、稳定、图像配准、对象跟踪或深度估计在内的各种基于特征的算法中的任一个。可以使用包含相关特征的像素的高分辨率邻域来进一步细化使用特征和对应关系开发的模型(例如，运动模型)。
使用高分辨率邻域(即高分辨率图像部分)来细化(130)点140’与142’之间的初始对应关系。细化可以使用各种方法来完成，这些方法包括(但不限于)重新计算一对相对应的高分辨率邻域之间的匹配度量(例如，归一化互相关)。重新计算匹配度量可以包括寻找高分辨率邻域144’与146’之间的归一化互相关，以及使用该度量来计算点142的估计位置，即后一帧中的点140’的未来位置。在其他实施例中，适合于具体应用的要求的各种方法中的任一个可以被使用。
可以对于使用初始特征和对应关系形成的给定模型(诸如用于运动估计的运动模型)使用诸如随机样本一致性(RANSAC)方法之类的各种验证过程。RANSAC方法使用观察数据值的集合、可以适合这些观察的参数化模型以及置信参数。原始数据的随机子集被迭代地选择为假设的内点(inlier)并且通过以下过程而被测试：使模型的参数适合假设的内点，对照适合的模型来测试所有其他数据，如果点良好适合估计的模型则将其包括为假设内点，如果足够多的点已被分类为假设内点则保持估计的模型，根据所有假设内点的更新集合来重新估计该模型，以及估计内点相对于该模型的误差。根据本发明的实施例还可以使用适合于具体应用的其他合适验证过程。
尽管在图4中示出了特定处理，但是根据本发明的实施例可以使用用于检测低分辨率阵列帧中的特征并且对这些特征使用超解析区域来细化特征对应关系的各种处理的任一个。虽然在此讨论的图和处理示出了图像中的单个角，但是本发明的实施例可以对包括各种类型的多个特征的图像进行操作。
尽管上面的描述包含许多特异性，但是这些不应当被理解为限制本发明的范围，而应理解为仅提供对本发明的当前优选实施例中的某些的例示。各种其他实施例在其范围内是可能的。

Claims (26)

1.一种用于根据多个低分辨率图像来执行基于特征的高分辨率运动估计的方法，包括：
使用通过软件而被配置的处理器(108)对于低分辨率图像序列执行特征检测(122，124)以确定(126)所述低分辨率图像序列中的多个检测到的特征的初始位置，其中至少一个低分辨率图像序列是从不同角度捕捉的低分辨率图像序列的集合的一部分；
使用通过软件而被配置的处理器(108)根据从不同角度捕捉的低分辨率图像序列的集合来合成(128)高分辨率图像部分以执行超分辨率处理，其中合成的高分辨率图像部分包含所确定的来自所述低分辨率图像序列的多个检测到的特征；
使用通过软件而被配置的处理器(108)在所述高分辨率图像部分内执行特征检测(129)以确定(130)所述多个检测到的特征的高精度位置；以及
使用通过软件而被配置的处理器(108)来利用所述多个检测到的特征的高精度位置估计相机运动。
2.如权利要求1所述的方法，其中，所述检测到的特征是从由以下各项组成的组中选择的：边缘、角和斑点。
3.如权利要求1所述的方法，其中，对于低分辨率图像序列执行特征检测(122，124)进一步包括：
在来自所述低分辨率图像序列的第一帧中检测特征的位置(122)；以及
在来自所述低分辨率图像序列的第二帧中检测特征的位置(124)。
4.如权利要求3所述的方法，其中，在来自所述低分辨率图像序列的第二帧中检测(124)特征的位置进一步包括：搜索来自所述低分辨率图像序列的第二帧以定位在来自所述低分辨率图像序列的第一帧中检测到的特征。
5.如权利要求4所述的方法，其中，搜索来自所述低分辨率图像序列的第二帧以定位在来自所述低分辨率图像序列的第一帧中检测到的特征进一步包括：
确定在所述低分辨率图像序列中的第一帧中的给定特征的位置周围的图像分块；以及
使用匹配标准来搜索所述低分辨率图像序列中的第二帧以寻找相对应的图像分块。
6.如权利要求5所述的方法，其中，所述匹配标准包括使误差距离度量最小化。
7.如权利要求3所述的方法，其中，在高分辨率图像部分内执行特征检测(129)以确定所述多个检测到的特征的高精度位置进一步包括：使用包含来自所述低分辨率图像序列中的第一帧的特征的高分辨率图像区域来搜索包含来自所述低分辨率图像序列中的第二帧的特征的高分辨率图像区域以寻找来自所述低分辨率图像序列中的第一帧的特征。
8.如权利要求7所述的方法，其中，搜索包含来自所述低分辨率图像序列中的第二帧的特征的高分辨率图像区域以寻找来自所述低分辨率图像序列中的第一帧的特征进一步包括：使用匹配标准将包含来自所述低分辨率图像序列中的第二帧的特征的高分辨率图像区域与包含来自所述低分辨率图像序列中的第一帧的特征的高分辨率图像区域相比较。
9.如权利要求8所述的方法，其中，所述匹配标准包括使误差距离度量最小化。
10.如权利要求1所述的方法，其中，所述处理器(108)是还包括成像器阵列的阵列相机的一部分，所述方法还包括使用所述成像器阵列从不同角度捕捉所述低分辨率图像序列的集合中的至少多个低分辨率图像序列。
11.如权利要求1所述的方法，其中，所述多个检测到的特征的高精度位置以相对于所述低分辨率图像序列中的帧的像素的大小的子像素精度估计特征位置。
12.如权利要求1所述的方法，其中，对于低分辨率图像序列执行特征检测进一步包括：对于多个低分辨率图像序列执行特征检测，其中每一个序列来自不同角度。
13.如权利要求1所述的方法，其中，
所述低分辨率图像序列的集合包括在多个不同颜色通道中捕捉的低分辨率图像序列；并且
对于低分辨率图像序列执行特征检测进一步包括：对于每一个颜色通道中的至少一个低分辨率图像序列执行特征检测。
14.一种阵列相机(100)，配置为根据使用所述阵列相机捕捉的低分辨率图像来执行基于特征的高分辨率运动估计，所述阵列相机包括：
成像器阵列(106)；以及
处理器(108)，通过软件而被配置为控制所述成像器阵列的各种工作参数；
其中所述软件还将所述处理器(108)配置为：
捕捉使用所述成像器阵列从不同角度捕捉的低分辨率图像序列的集合；
对于所述低分辨率图像序列的集合中的低分辨率图像序列执行特征检测(122，124)以确定(126)所述低分辨率图像序列中的多个检测到的特征的初始位置，根据从不同角度捕捉的低分辨率图像序列的集合来合成(128)高分辨率图像部分，其中所述高分辨率图像部分包含所确定的来自所述低分辨率图像序列的多个检测到的特征；
在所述高分辨率图像部分内执行特征检测(129)以确定(130)所述多个检测到的特征的高精度位置；以及
使用所述多个检测到的特征的高精度位置来估计相机运动。
15.如权利要求14所述的阵列相机(100)，其中，所述检测到的特征是从由以下各项组成的组中选择的：边缘、角和斑点。
16.如权利要求14所述的阵列相机(100)，其中，所述处理器(108)还被配置为通过以下操作对于低分辨率图像序列执行特征检测(122，124)：
在来自所述低分辨率图像序列的第一帧中检测(122)特征的位置；以及
在来自所述低分辨率图像序列的第二帧中检测(124)特征的位置。
17.如权利要求16所述的阵列相机(100)，其中，所述处理器(108)通过软件还被配置为：通过搜索所述低分辨率图像序列中的第二帧以定位在所述低分辨率图像序列中的第一帧中检测到(122)的特征来在所述低分辨率图像序列中的第二帧中检测(124)特征的位置。
18.如权利要求17所述的阵列相机(100)，其中，所述处理器(108)通过软件还被配置为通过以下操作搜索所述低分辨率图像序列中的第二帧以定位在所述低分辨率图像序列中的第一帧中检测到的给定特征：
确定在所述低分辨率图像序列中的第一帧中的给定特征的位置周围的图像分块；以及
使用匹配标准来搜索所述低分辨率图像序列中的第二帧以寻找相对应的图像分块。
19.如权利要求18所述的阵列相机(100)，其中，所述匹配标准包括使误差距离度量最小化。
20.如权利要求16所述的阵列相机(100)，其中，所述处理器(108)通过软件还被配置为：通过使用包含来自所述低分辨率图像序列中的第一帧的特征的高分辨率图像区域来搜索包含来自所述低分辨率图像序列中的第二帧的特征的高分辨率图像区域以寻找来自所述低分辨率图像序列中的第一帧的特征，在高分辨率图像部分内执行特征检测(129)以确定(130)所述多个检测到的特征的高精度位置。
21.如权利要求20所述的阵列相机(100)，其中，所述处理器(108)通过软件还被配置为：通过使用匹配标准将包含来自所述低分辨率图像序列中的第二帧的特征的高分辨率图像区域与包含来自所述低分辨率图像序列中的第一帧的特征的高分辨率图像区域相比较，搜索包含来自所述低分辨率图像序列中的第二帧的特征的高分辨率图像区域以寻找来自所述低分辨率图像序列中的第一帧的特征。
22.如权利要求21所述的阵列相机(100)，其中，所述匹配标准包括使误差距离度量最小化。
23.如权利要求14所述的阵列相机(100)，其中，所述多个检测到的特征的高精度位置以相对于所述低分辨率图像序列中的帧的像素的大小的子像素精度估计特征位置。
24.如权利要求14所述的阵列相机(100)，其中，所述成像器阵列(106)中的多个成像器(110)感测不同波长的光，并且所述低分辨率图像序列的集合包括在多个不同颜色通道中捕捉的低分辨率图像序列。
25.如权利要求24所述的阵列相机(100)，其中，所述处理器(108)通过软件还被配置为：通过对于每一个颜色通道中的至少一个低分辨率图像序列执行特征检测来对于低分辨率图像序列执行特征检测。
26.如权利要求14所述的阵列相机(100)，其中，所述处理器(108)通过软件还被配置为：通过对于多个低分辨率图像序列执行特征检测来对于低分辨率图像序列执行特征检测，其中每一个序列来自不同角度。
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