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Poisson noise removal from images using the fast discrete curvelet transform
K. M.Muraleedhara Prabhu
Published in
2011
Abstract
We propose a strategy to combine the variance stabilizing transform (VST), used for Poisson image denoising, with the fast discrete Curvelet transform (FDCT). The VST transforms the Poisson image to approximately Gaussian distributed, and the subsequent denoising can be performed in the Gaussian domain. However, the performance of the VST degrades when the original image intensity is very low. On the other hand, the FDCT can sparsely represent the intrinsic features of images having discontinuities along smooth curves. Therefore, it is suitable for denoising applications. Combining the VST with the FDCT leads to good Poisson image denoising algorithms, even for low intensity images. We present a simple approach to achieve this and demonstrate some simulation results. The results show that the VST combined with the FDCT is a promising candidate for Poisson denoising. © 2011 IEEE.
About the journal
Journal2011 National Conference on Communications, NCC 2011
Open AccessNo
Concepts (17)
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    CURVELET TRANSFORMS
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    De-noising
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    GAUSSIAN DISTRIBUTED
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    GAUSSIAN DOMAIN
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    Image de-noising
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    IMAGE DENOISING ALGORITHM
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    INTRINSIC FEATURES
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    LOW-INTENSITY
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    NONSUBSAMPLED CONTOURLET TRANSFORM
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    Original images
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    Poisson noise
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    SIMPLE APPROACH
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    Simulation result
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    SMOOTH CURVES
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    VARIANCE STABILIZING TRANSFORM
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    Image processing
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    Noise pollution control