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SIFT feature extraction method for the defocused blurred area of multi-focus images
- Optics and Precision Engineering Vol. 31, Issue 24, Pages: 3630-3639(2023)
- 作者机构：
长安大学 工程机械学院，陕西 西安 710064
- 作者简介：
- 基金信息：
DOI：10.37188/OPE.20233124.3630
CLC： TP391.41Received：04 May 2023，
Revised：16 June 2023，
Published：25 December 2023
- 稿件说明：
移动端阅览
夏晓华,赵倩,向华涛等.多聚焦图像离焦模糊区域的SIFT特征提取[J].光学精密工程,2023,31(24):3630-3639.
XIA Xiaohua,ZHAO Qian,XIANG Huatao,et al.SIFT feature extraction method for the defocused blurred area of multi-focus images[J].Optics and Precision Engineering,2023,31(24):3630-3639.
夏晓华,赵倩,向华涛等.多聚焦图像离焦模糊区域的SIFT特征提取[J].光学精密工程,2023,31(24):3630-3639. DOI： 10.37188/OPE.20233124.3630.
XIA Xiaohua,ZHAO Qian,XIANG Huatao,et al.SIFT feature extraction method for the defocused blurred area of multi-focus images[J].Optics and Precision Engineering,2023,31(24):3630-3639. DOI： 10.37188/OPE.20233124.3630.
摘要
常规的尺度不变特征变换（SIFT)图像特征提取方法难以提取多聚焦图像离焦模糊区域的特征，使得图像间存在局部、少量的公共特征，导致多聚焦图像配准精度差，严重影响后续图像融合和三维重建质量。在分析图像离焦模糊区域特征提取不确定性的基础上，提出了一种多聚焦图像离焦模糊区域的SIFT特征提取方法。首先提取多聚焦图像聚焦清晰区域的SIFT特征，再利用光流跟踪提取对应离焦模糊区域的SIFT特征，避免了在离焦模糊区域直接提取SIFT特征的不确定性。实验结果表明：提出的方法在离焦模糊区域具有良好的SIFT特征提取能力和提取精度，能实现多聚焦图像SIFT特征匹配数量显著增长，SIFT特征提取的误差为0.03~0.39 pixels，优于现有方法的0.21~1.71 pixels。降低了离焦模糊区域SIFT特征提取的不确定性，为多聚焦图像精确配准奠定了基础。
Abstract
Conventional SIFT image feature extraction methods have difficulty in extracting features from the defocused blurred area of multi-focus images. As a result， common features between images are local and few， leading to poor accuracy in multi-focus image registration， which seriously affects the quality of subsequent image fusion and 3D reconstruction. Based on analyzing the uncertainty of feature extraction from the defocused blurred areas of images， a feature extraction method is proposed for the defocused blurred area of multi-focus images. First， features are extracted from the focused clear area of multi-focus images. Subsequently， the features in the corresponding defocused blurred area are extracted using optical flow tracking， thereby avoiding the uncertainty of directly extracting features from the defocused blurred area. Experimental results show that the proposed method displays good feature extraction ability and accuracy in the defocused blurred area， significantly increasing the number of features matches. Feature extraction error ranges between 0.03-0.39 pixels， which is better than the 0.21-1.71 pixels of existing methods. This indicates a reduction in the uncertainty of feature extraction from the defocused blurred area， making it suitable for multi-focus image registration.
关键词
Keywords
references
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