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Defocus Blur-Invariant Scale-Space Feature Extractions

机译:散焦模糊不变尺度空间特征提取

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We propose modifications to scale-space feature extraction techniques scale-invariant feature transform (SIFT) and speeded up robust features (SURFs) that make the feature detection and description invariant to defocus blur. Specifically, the scale-space blob detection relies on the second derivative responses of images. Our analysis of circular defocus blur (which sufficiently approximates a real camera blur kernel) and its effect on scale-space blob detection suggests that fourth derivative—and not the usual second derivative—is optimal for detecting the blurred blobs, while multi-scale descriptors of blurred blobs are effective at establishing correspondences between the blurred images. The proposed defocus blur-invariant (DBI) scale-space feature extraction techniques—which we refer to as DBI-SIFT and DBI-SURF—do not require image deblurring nor blur kernel estimation, meaning that their accuracy does not depend on the quality of image deblurring. We offer empirical evidence of blur invariance by establishing interest point correspondences between sharp or blurred reference images and blurred target images.
机译:我们提出了对尺度空间特征提取技术的修改,即尺度不变特征变换(SIFT),并加快了鲁棒特征(SURF)的速度,使特征检测和描述不变,从而散焦模糊。具体来说,比例空间斑点检测依赖于图像的二阶导数响应。我们对圆形散焦模糊(充分逼近真实相机的模糊内核)及其对比例空间斑点检测的影响的分析表明,四阶导数(而不是通常的二阶导数)对于检测模糊斑点是最佳的,而多尺度描述符的模糊斑点有效地建立了模糊图像之间的对应关系。拟议的散焦模糊不变(DBI)尺度空间特征提取技术(我们称为DBI-SIFT和DBI-SURF)不需要图像去模糊或模糊核估计,这意味着它们的准确性不取决于图像质量。图像去模糊。通过在清晰或模糊的参考图像与模糊的目标图像之间建立兴趣点对应关系,我们提供了模糊不变性的经验证据。

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