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- W1835905189 abstract "This paper proposes a new algorithm for image restoration (deconvolution and denoising) which employs the recently developed dual-tree complex wavelet transform in an iterative Bayesian framework. Complex wavelets are selected for their key features: shift invariance, directional selectivity and efficiency. The aim is to find an optimal description of the restored image in the complex wavelet domain, which minimises a quadratic energy function of the wavelet coefficients. The algorithm searches for this minimum using an efficient conjugate gradient method. We show that this can improve the SNR performance of a good minimax deconvolution method, WaRD, which is used to initialise the iterations, by typically 1.2 dB. Convergence is quite rapid, achieving 80% of the ultimate performance gain in about 20 iterations. Each iteration takes around 5 seconds using MatLab on a 400 MHz Pentium computer with 256/spl times/256 pixel images." @default.
- W1835905189 created "2016-06-24" @default.
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- W1835905189 date "2002-11-13" @default.
- W1835905189 modified "2023-09-26" @default.
- W1835905189 title "Bayesian image deconvolution and denoising using complex wavelets" @default.
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- W1835905189 doi "https://doi.org/10.1109/icip.2001.958477" @default.
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