Riesz-Laplace Wavelet Transform and PCNN Based Image Fusion

• Autorzy: Sun Shuifa , Tang Yongheng , Mei Zhoujunshen , Yang Min , Tang Tinglong , Wu Yirong

Identyfikatory

DOI

10.22630/MGV.2023.32.1.4

• Języki publikacji: EN

Abstrakty

EN

Important information perceived by human vision comes from the low-level features of the image, which can be extracted by the Riesz transform. In this study, we propose a Riesz transform based approach to image fusion. The image to be fused is first decomposed using the Riesz transform. Then the image sequence obtained in the Riesz transform domain is subjected to the Laplacian wavelet transform based on the fractional Laplacian operators and the multi-harmonic splines. After Laplacian wavelet transform, the image representations have directional and multi-resolution characteristics. Finally, image fusion is performed, leveraging Riesz-Laplace wavelet analysis and the global coupling characteristics of pulse coupled neural network (PCNN). The proposed approach has been tested in several application scenarios, such as multi-focus imaging, medical imaging, remote sensing full-color imaging, and multi-spectral imaging. Compared with conventional methods, the proposed approach demonstrates superior performance on visual effects, contrast, clarity, and the overall efficiency.

Słowa kluczowe

EN

image fusion; Riesz transforms; polyharmonic spline; Laplacian wavelet; pulse-coupled neural networks; PCNN

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2023
• Tom: Vol. 32, No. 1
• Strony: 73--84
• Opis fizyczny: Bibliogr. 23 poz., rys., tab.

Twórcy

autor

Sun Shuifa

• Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, YiChang, China
• College of Computer and Information Technology, China Three Gorges University, YiChang, China

• https://orcid.org/0000-0003-0933-152X

autor

Tang Yongheng

• College of Computer and Information Technology, China Three Gorges University, YiChang, China
• College of Economics and Management, China Three Gorges University, YiChang, China

• https://orcid.org/0000-0002-3184-691X

autor

Mei Zhoujunshen

• College of Computer and Information Technology, China Three Gorges University, YiChang, China

autor

Yang Min

• College of Computer and Information Technology, China Three Gorges University, YiChang, China

• https://orcid.org/0000-0002-3827-5961

autor

Tang Tinglong

• Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, YiChang, China
• College of Computer and Information Technology, China Three Gorges University, YiChang, China

• https://orcid.org/0000-0002-6301-3592

autor

Wu Yirong

• Institute of Advanced Studies in Humanities and Social Sciences, Beijing Normal University, Zhuhai, China

Bibliografia

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Uwagi

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).