Channel edge detection using 2D complex shearlet transform: a case study from the South Caspian Sea

Karbalaali, Haleh; Javaherian, Abdolrahim; Dahlke, Stephan; Torabi, Siyavash

doi:10.1071/eg17057

Cited by 5 publications

(2 citation statements)

References 42 publications

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“…Generally speaking, it divides the source images into low-pass and high-pass subband images in different levels, i.e., the approximately sparse representation of the source images and the obvious feature information of the images. Different from the discrete wavelet, contourlet, and shearlet, the complex shearlet is realized based on the multi-scale pyramid filters and the Hilbert transform [19,20]. e former gives the multiple partitions of the image, and the latter provides the directional sub-bands in the complex space.…”

Section: E Complex Shearlet Transformmentioning

confidence: 99%

The Fusion of Multi-Focus Images Based on the Complex Shearlet Features-Motivated Generative Adversarial Network

Wang

Liu

Huang

et al. 2021

Journal of Advanced Transportation

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The traditional methods for multi-focus image fusion, such as the typical multi-scale geometric analysis theory-based methods, are usually restricted by sparse representation ability and the transferring efficiency of the fusion rules for the captured features. Aiming to integrate the partially focused images into the fully focused image with high quality, the complex shearlet features-motivated generative adversarial network is constructed for multi-focus image fusion in this paper. Different from the popularly used wavelet, contourlet, and shearlet, the complex shearlet provides more flexible multiple scales, anisotropy, and directional sub-bands with the approximate shift invariance. Therefore, the features in complex shearlet domain are more effective. With of help of the generative adversarial network, the whole procedure of multi-focus fusion is modeled to be the process of adversarial learning. Finally, several experiments are implemented and the results prove that the proposed method outperforms the popularly used fusion algorithms in terms of four typical objective metrics and the comparison of visual appearance.

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Section: E Complex Shearlet Transformmentioning

confidence: 99%

The Fusion of Multi-Focus Images Based on the Complex Shearlet Features-Motivated Generative Adversarial Network

Wang

Liu

Huang

et al. 2021

Journal of Advanced Transportation

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“…Previous versions of E(f, y) and R(f, y) that are based on complex-valued shearlets [60,44,62] were already successfully applied in different feature extraction tasks such as the detection and characterization of flame fronts [62,44], the detection of borders of tidal flats in the Wadden Sea [44], the extraction of fracture-traces in rock masses [13], the detection of channel boundaries in seismic data [41,42], and the automated detection of the boundaries of touching cells in scanning electron (SEM) images [55].…”

Section: Applicationsmentioning

confidence: 99%

Edge, Ridge, and Blob Detection with Symmetric Molecules

Reisenhofer¹,

King²

2019

SIAM J. Imaging Sci.

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We present a novel approach to the detection and characterization of edges, ridges, and blobs in two-dimensional images which exploits the symmetry properties of directionally sensitive analyzing functions in multiscale systems that are constructed in the framework of α-molecules. The proposed feature detectors are inspired by the notion of phase congruency, stable in the presence of noise, and by definition invariant to changes in contrast. We also show how the behavior of coefficients corresponding to differently scaled and oriented analyzing functions can be used to obtain a comprehensive characterization of the geometry of features in terms of local tangent directions, widths, and heights. The accuracy and robustness of the proposed measures are validated and compared to various state-of-the-art algorithms in extensive numerical experiments in which we consider sets of clean and distorted synthetic images that are associated with reliable ground truths. To further demonstrate the applicability, we show how the proposed ridge measure can be used to detect and characterize blood vessels in digital retinal images and how the proposed blob measure can be applied to automatically count the number of cell colonies in a Petri dish.

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Some remarks on cone-adapted shearlets

Dahlke,

Teschke

2024

Int J Geomath

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In recent years, cone-adapted shearlets have been established as a powerful tool for the detection of directional information in images. It has also turned out that an interpretation of cone-adapted shearlets in the realm of coorbit space theory exists. Then, cone-adapted shearlets are defined by requiring a certain sparsity pattern of the frame expansion coefficients. In this note, we discuss some structural properties of these spaces.

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Channel edge detection using 2D complex shearlet transform: a case study from the South Caspian Sea

Cited by 5 publications

References 42 publications

The Fusion of Multi-Focus Images Based on the Complex Shearlet Features-Motivated Generative Adversarial Network

The Fusion of Multi-Focus Images Based on the Complex Shearlet Features-Motivated Generative Adversarial Network

Edge, Ridge, and Blob Detection with Symmetric Molecules

Some remarks on cone-adapted shearlets

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