A single image deblurring approach based on a fractional order dark channel prior

• Autorzy: Yu Xiaoyuan , Xie Wei , Yu Jinwei

Identyfikatory

DOI

10.34768/amcs-2022-0032

• Języki publikacji: EN

Abstrakty

EN

The dark channel prior has been successfully applied to solve the blind deblurring problem on different scene images. Since the dark channel of the blurry-noise image is similar to that of the corresponding clear image, the sparsity of the dark channel is less effective for image blind deblurring. Inspired by the fact that a fractional order calculation can inhibit the noise and preserve the texture information of the image, a fractional order dark channel prior is proposed for image deblurring in this paper. It is appropriate for kernel estimation where input images and intermediate images are processed by using a fractional order dark channel prior. Furthermore, the non-convex problem is solved by the half-quadratic splitting method, and some metrics are used for deblurring image quality assessment. Finally, quantitative and qualitative experimental results show that the proposed method achieves state-of-the-art results on synthetic and real blurry images.

Słowa kluczowe

EN

blind image deblurring; fractional order dark channel prior; nonconvex problem

PL

obraz rozmyty; rząd ułamkowy; problem niewypukły

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2022
• Tom: Vol. 32, no. 3
• Strony: 441--454
• Opis fizyczny: Bibliogr. 40 poz., rys., tab., wykr.

Twórcy

autor

Yu Xiaoyuan

• School of Automation Science and Engineering, South China University of Technology, Wushan Road, Tianhe District, 510641 Guangzhou, China

autor

Xie Wei

• School of Automation Science and Engineering, South China University of Technology, Wushan Road, Tianhe District, 510641 Guangzhou, China
• Key Laboratory of Autonomous Systems and Networked Control, Ministry of Education, Wushan Road, Tianhe District, 510641 Guangzhou, China

autor

Yu Jinwei

• School of Automation Science and Engineering, South China University of Technology, Wushan Road, Tianhe District, 510641 Guangzhou, China

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)