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Super-Resolved Image Mosaicing from Image Sequence Taken by Moving Camera

移动相机拍摄的图像序列的超分辨率图像马赛克

基本信息

批准号：
12650392
负责人：
OZAWA Shinji
金额：
$ 1.98万
依托单位：
Keio University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2000
资助国家：
日本
起止时间：
2000 至 2001
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-12650392/
关键词：
Shape Recovery 3D Model Super Resolution Mosaicing Factorization Method Computer Vision Moving Camera

项目摘要

In this research, we aim to develop a method for synthesizing super-resolved images, that improve spatial resolution of input images by merging multiple input images taken by a moving camera. When a moving camera5takes image sequence, some part of image is overlapped in different frames. In such overlapped part, feature points are detected for obtaining point-by-point correspondence between the frames. This correspondence information provides shape of the object by applying Factorization method. Although the execution of Factorization method only provides a part of the object shape, a number of the part of the shape are merged for generating whole shape. The input images are rendered onto the generated object shape, so that the 3D model with texture can be reconstructed. Since a number of images can be rendered onto the same surface of the object, spatial resolution can be improved. The generated images projected from the reconstructed 3D model with texture can be regarded as mosaic image with super resolution. In the experiment performed in this research project, we can generate super resolved images that have 4-16 times spatial resolution.We also propose a novel method for synthesizing super-resolved images from uncalibrated moving camera by using projective geometry. In this method, point-by-point correspondence between input frames can be generated by fundamental matrices that are derived from the projective geometry. For minimizing the corresponding error, we also implement an optimization technique, in which higher frequency component of the image is estimated for the optimization evaluation. The experimental results demonstrate the efficacy of the method.

在这项研究中，我们的目标是开发一种合成超分辨率图像的方法，该方法通过合并运动摄像机拍摄的多个输入图像来提高输入图像的空间分辨率。当移动摄像机拍摄图像序列时，图像的某些部分在不同的帧中重叠。在这样的重叠部分中，检测特征点以获得帧之间的逐点对应。该对应信息通过应用因式分解方法来提供对象的形状。虽然分解方法的执行只提供了对象形状的一部分，但形状的多个部分被合并以生成整个形状。将输入图像渲染到生成的对象形状上，从而可以重建具有纹理的3D模型。由于可以将多个图像渲染到对象的同一表面上，因此可以提高空间分辨率。基于纹理重建的3D模型所生成的图像可以看作是超分辨率的拼接图像。在本研究项目的实验中，我们可以生成空间分辨率为4-16倍的超分辨率图像。我们还提出了一种利用射影几何从未标定的运动摄像机合成超分辨率图像的新方法。在这种方法中，输入帧之间的逐点对应关系可以由从射影几何导出的基本矩阵来生成。为了使相应的误差最小，我们还实现了一种优化技术，即通过估计图像的高频分量来进行优化评估。实验结果证明了该方法的有效性。