Wyniki wyszukiwania - BazTech

1

Catenary image enhancement method based on curvelet transform with adaptive enhancement function

Wu Changdong

Diagnostyka

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2019

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Vol. 20, No. 2

3--10

EN

In the process of catenary failure diagnosis system based on image processing technique, some catenary images present low contrast, which need to be enhanced. Curvelet transform has the high directional sensitivity and anisotropy, which is suitable for image enhancement because of its optimal sparse representation of image with rich details and edges. First, the catenary image is decomposed by Curvelet transform to get its high and low frequency coefficients, then adjust the high frequency coefficients using the enhancement function. Afterwards, combine the high frequency coefficients and low frequency coefficients by the inverse Curvelet transform, and thus to get the enhanced catenary image. In this paper, Curvelet transform is compared with the traditional enhancement methods. The experimental results show that the proposed method can effectively enhance the low contrast catenary images, the catenary insulator, arm, hanger, pillar and locator part become visible, the details become more obvious. Moreover, as for the online application of catenary failure diagnosis system, efficiency is another important consideration. The experimental results also show that the cost time of catenary image enhancement is within a few tens of seconds, which meets the requirements of catenary failure diagnosis system.

2

A seismic interpolation and denoising method with curvelet transform matching filter

Yang H., Long Y., Lin J., Zhang F., Chen Z.

Acta Geophysica

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2017

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Vol. 65, no. 5

1029--1042

EN

A new seismic interpolation and denoising method with a curvelet transform matching filter, employing the fast iterative shrinkage thresholding algorithm (FISTA), is proposed. The approach treats the matching filter, seismic interpolation, and denoising all as the same inverse problem using an inversion iteration algorithm. The curvelet transform has a high sparseness and is useful for separating signal from noise, meaning that it can accurately solve the matching problem using FISTA. When applying the new method to a synthetic noisy data sets and a data sets with missing traces, the optimum matching result is obtained, noise is greatly suppressed, missing seismic data are filled by interpolation, and the waveform is highly consistent. We then verified the method by applying it to real data, yielding satisfactory results. The results show that the method can reconstruct missing traces in the case of low SNR (signal-to-noise ratio). The above three problems can be simultaneously solved via FISTA algorithm, and it will not only increase the processing efficiency but also improve SNR of the seismic data.

3

Odszumianie obrazów CT za pomocą transformaty curvelet

Ławicki T.

Przegląd Elektrotechniczny

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2015

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R. 91, nr 2

222-224

PL

W artykule przedstawiono zastosowanie transformaty curvelet do odszumiania obrazów tomografii komputerowej (CT). Szum wpływa na zdolność do wizualizacji cech patologicznych i struktur żywych tkanek w obrazach CT. Szum w CT zależy od ilości oddzielnych fotonów rentgenowskich dochodzących do detektora. W CT szum jest odpowiedzialny za zmniejszenie widoczności obszarów o niskim kontraście i obiektów. Zaszumione obrazy mogą nie być prawidłowo interpretowane przez lekarza, w szczególności przy wykrywaniu zmian patologicznych w tkankach. Testy przeprowadzono na standardowym obrazie testowym Shepp-Logan z addytywnym szumem gaussowskim.

EN

The paper proposes a noise reduction method for CT by processing it through the curvelet transform. Noise affect the ability to visualize pathologic qualities and the living tissues structure in CT. Noise in CT depends on the amount of discrete x-ray photons reaching the detector. In the CT images noise is responsible for visibility reduction the of low contrast areas and objects. Noisy picture may not be properly interpreted by a physician, especially on detection of pathological changes in tissues. The tests were performed on the Shepp-Logan phantom standard test image with additive Gaussian noise.