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

• Autorzy: Wu Changdong

Identyfikatory

DOI

10.29354/diag/109175

• Języki publikacji: EN

Abstrakty

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.

Słowa kluczowe

EN

catenary image; enhancement; curvelet transform; coefficient structure; adaptive function

PL

sieć trakcyjna; awaria; diagnostyka; transformata curvelet

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2019
• Tom: Vol. 20, No. 2
• Strony: 3--10
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Wu Changdong

• School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, 610039, China

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).