三维可操纵近正交滤波器对。

IF 2.1 3区 数学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE SIAM Journal on Imaging Sciences Pub Date : 2022-01-01 Epub Date: 2022-05-26 DOI:10.1137/21m143529x
Tommy M Tang, Hemant D Tagare
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引用次数: 0

摘要

接近正交的可操纵滤波器对有许多图像处理应用。本文提出了一种设计这种滤波器的新方法。关键思想是通过最小化偏离正交函数来设计可控制滤波器。这些最小滤波器对几乎完全是正交的。滤波器的极性部分是非负的,单调的,并且围绕轴高度聚焦,并且渐近滤波器达到精确的正交。这些结果是通过利用滤波器与广义希尔伯特矩阵之间的关系得到的。这些近正交滤波器非常接近三维Gabor滤波器。我们通过实验验证了渐近数学结果,并通过低温电子显微镜局部傅里叶壳相关的有效计算进一步证明了这些滤波器对的使用。
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Steerable Near-Quadrature Filter Pairs in Three Dimensions.

Steerable filter pairs that are near quadrature have many image processing applications. This paper proposes a new methodology for designing such filters. The key idea is to design steerable filters by minimizing a departure-from-quadrature function. These minimizing filter pairs are almost exactly in quadrature. The polar part of the filters is nonnegative, monotonic, and highly focused around an axis, and asymptotically the filters achieve exact quadrature. These results are established by exploiting a relation between the filters and generalized Hilbert matrices. These near-quadrature filters closely approximate three dimensional Gabor filters. We experimentally verify the asymptotic mathematical results and further demonstrate the use of these filter pairs by efficient calculation of local Fourier shell correlation of cryogenic electron microscopy.

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来源期刊
SIAM Journal on Imaging Sciences
SIAM Journal on Imaging Sciences COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
3.80
自引率
4.80%
发文量
58
审稿时长
>12 weeks
期刊介绍: SIAM Journal on Imaging Sciences (SIIMS) covers all areas of imaging sciences, broadly interpreted. It includes image formation, image processing, image analysis, image interpretation and understanding, imaging-related machine learning, and inverse problems in imaging; leading to applications to diverse areas in science, medicine, engineering, and other fields. The journal’s scope is meant to be broad enough to include areas now organized under the terms image processing, image analysis, computer graphics, computer vision, visual machine learning, and visualization. Formal approaches, at the level of mathematics and/or computations, as well as state-of-the-art practical results, are expected from manuscripts published in SIIMS. SIIMS is mathematically and computationally based, and offers a unique forum to highlight the commonality of methodology, models, and algorithms among diverse application areas of imaging sciences. SIIMS provides a broad authoritative source for fundamental results in imaging sciences, with a unique combination of mathematics and applications. SIIMS covers a broad range of areas, including but not limited to image formation, image processing, image analysis, computer graphics, computer vision, visualization, image understanding, pattern analysis, machine intelligence, remote sensing, geoscience, signal processing, medical and biomedical imaging, and seismic imaging. The fundamental mathematical theories addressing imaging problems covered by SIIMS include, but are not limited to, harmonic analysis, partial differential equations, differential geometry, numerical analysis, information theory, learning, optimization, statistics, and probability. Research papers that innovate both in the fundamentals and in the applications are especially welcome. SIIMS focuses on conceptually new ideas, methods, and fundamentals as applied to all aspects of imaging sciences.
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