Image denoising based on iterative generalized cross-validation and fast translation invariant |
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| Affiliation: | 1. School of Mathematics, Shandong University, Jinan 250100, China;2. Department of Mathematics, University of South Carolina, Columbia 29208, USA;3. School of Computer Science and Technology, Shandong University, Jinan 250101, China;4. College of Arts Management, Shandong University of Arts, Jinan 250300, China;5. Department of Intervention Medicine, The Second Hospital of Shandong University, Jinan 250033, China;1. Institute of Genome Biology, Leibniz-Institute for Farm Animal Biology (FBN), Dummerstorf, Germany;2. Oscar Langendorff Institute of Physiology, University of Rostock, Germany;3. F. Hoffmann-La Roche AG, pRED, Pharma Research & Early Development, DTA CNS, Basel, Switzerland;4. Institute of Behavioural Physiology, Leibniz-Institute for Farm Animal Biology (FBN), Dummerstorf, Germany;5. Institute of Genetic and Biometry, Leibniz-Institute for Farm Animal Biology (FBN), Dummerstorf, Germany;6. Hormone Research, Murdoch Childrens Research Institute, University of Melbourne, Australia;7. Medical Biology and Electron Microscopy Centre, University Medicine Rostock, Rostock, Germany;1. Department of Undergraduate Studies, Cizik School of Nursing, The University of Texas Health Science Center, 6901 Bertner Avenue, Room 691, Houston, TX 77030, United States of America;2. Cizik School of Nursing, University of Texas Health Science Center at Houston, Center for Nursing Research, Room #585, 6901 Bertner Avenue, Houston, TX 77030, United States of America;3. School of Nursing, The University of Texas Medical Branch at Galveston, 301 University Blvd., Galveston, TX, United States of America |
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| Abstract: | Wavelet shrinkage is a promising method in image denoising, the key factor of which lies in the threshold selection. A fast and effective wavelet denoising method, called Iterative Generalized Cross-Validation and Fast Translation Invariant (IGCV–FTI) is proposed, which reduces the computation cost of the standard Generalized Cross-Validation (GCV) method and efficiently suppresses the Pseudo-Gibbs phenomena with an extra gain of 1–1.87 dB in PSNR compared with GCV. In the proposed approach, we establish a novel functional relation between the GCV results of two neighboring thresholds based on integer wavelet transform, and combine it with threshold-search interval optimization. As a result, the proposed IGCV reduces the time complexity of original GCV algorithm by two orders of magnitude. In addition, a recursion strategy is applied to expedite the translation invariant. The high efficiency and proficient capacity to remove noise make IGCV–FTI a good choice for image denoising. |
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| Keywords: | Image processing Image denoising Wavelet shrinkage Integer wavelet transform Generalized cross-validation Translation invariant Pseudo-Gibbs phenomena Recursion strategy |
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