Curvelet based nonlocal means algorithm for image denoising |
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| Affiliation: | 1. Department of Biomedical Engineering, School of Life Science and Technology, Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Huazhong University of Science and Technology, Wuhan 430074, China;2. School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China;1. Department of Electronics and Telecommunication Engineering, Meghnad Saha Institute of Technology, Techno Complex, Madurdaha, Kolkata 700 150, India;2. S.K. Mitra Centre for Research in Space Environment, Institute of Radio Physics and Electronics, University of Calcutta, Kolkata 700 009, India;1. School of Electronic and Optical Engineering, Nanjing University of Science and Technology, 210094, Nanjing, China;2. Department of Software Engineering, Institute of Cybernetics, National Research Tomsk Polytechnic University, Russia;1. CNIT, DEI, University of Bologna, V.le del Risorgimento 2, 40136 Bologna, Italy;2. IEIIT, Consiglio Nazionale delle Ricerche, V.le del Risorgimento 2, 40136 Bologna, Italy;1. Department of Civil and Environmental Engineering, Politecnico di Milano, piazza Leonardo da Vinci 32,Milano 20133, ITALY;2. Carnegie Mellon University, USA;1. Informatics and Information Security Research Center (B?LGEM), Turkish Scientific and Technological Research Council (TÜB?TAK), 41470 Gebze, Kocaeli, Turkey;2. Department of Electronics Engineering, Gebze Institute of Technology, 41400 Gebze, Kocaeli, Turkey |
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| Abstract: | In this work, a curvelet based nonlocal means denoising method is proposed. In the proposed method, the curvelet transform is firstly implemented on the noisy image to produce reconstructed images. Then the similarity of two pixels in the noisy image is computed based on these reconstructed images which include complementary image features at relatively high noise levels or both the reconstructed images and the noisy image at relatively low noise levels. Finally, the pixel similarity and the noisy image are utilized to obtain the final denoised result using the nonlocal means method. Quantitative and visual comparisons demonstrate that the proposed method outperforms the state-of-art nonlocal means denoising methods in terms of noise removal and detail preservation. |
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| Keywords: | Image denoising Nonlocal means Curvelet transform Image reconstruction |
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