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多分辨率图像序列的超分辨率重建

DOI: 10.3724/SP.J.1004.2012.01804, PP. 1804-1814

Keywords: 超分辨率重建,尺度不变特征转换,多分辨率尺度,随机抽样一致性算法,仿射变换

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Abstract:

?针对不同焦距下拍摄的多分辨率尺度的图像序列,本文提出了一种基于尺度不变特征转换(Scaleinvariantfeaturetransform,SIFT)和图像配准的超分辨率(Superresolution,SR)图像盲重建算法.首先提取图像SIFT特征点,然后用向量夹角余弦进行特征描述符向量的初匹配,并用随机抽样一致性(Randomsampleconsensus,RANSAC)算法消除误匹配提高配准精度.计算变换参数后,将低分辨率图像(Low-resolution,LR)像素点映射到高分辨率(How-resolution,HR)网格,最后利用像素可信度加权算法填充缺失像素值,重建更高分辨率的图像.实验表明,本文算法能精确估计图像序列的缩放因子,可以有效处理仿射变换模型,对配准误差也具有一定的鲁棒性.算法从实质上提高了多分辨率尺度图像序列的分辨率,尤其在低分辨率帧数较少可用于重建的信息量严重不足时也能获得比较满意的重建效果.

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