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，我们旨在开发一种合成超级分辨图像的方法，该方法通过合并移动相机拍摄的多个输入图像来改善输入图像的空间分辨率。当移动的摄像头5拍摄图像序列时，图像的某些部分被不同的框架重叠。在这样的重叠部分中，检测到特征点是为了获得框架之间的点对应关系。此对应信息通过应用分解方法提供对象的形状。尽管分解方法的执行仅提供对象形状的一部分，但形状的许多部分已合并以生成整个形状。输入图像渲染到生成的对象形状上，以便可以重建具有纹理的3D模型。由于可以将许多图像渲染到对象的同一表面上，因此可以改进空间分辨率。从带有纹理的重建的3D模型中投射的生成的图像可以被视为具有超级分辨率的马赛克图像。在该研究项目中执行的实验中，我们可以生成具有空间分辨率4-16倍的超分辨率图像。我们还提出了一种新的方法，用于通过使用投影几何形状从未校准的移动相机中合成超级分辨的图像。在这种方法中，可以通过从投影几何形状得出的基本矩阵来生成输入帧之间的点对应关系。为了最小化相应的误差，我们还实施了一种优化技术，其中估计图像的较高频率成分以进行优化评估。实验结果证明了该方法的功效。