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8 September 2011 Denoising approach for remote sensing image based on anisotropic diffusion and wavelet transform algorithm
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In this paper, a combined method have been put forward for one ASTER detected image with the wavelet filter to attenuate the noise and the anisotropic diffusion PDE(Partial Differential Equation) for further recovering image contrast. The model is verified in different noising background, since the remote sensing image usually contains salt and pepper, Gaussian as well as speckle noise. Considered the features that noise existing in wavelet domain, the wavelet filter with Bayesian estimation threshold is applied for recovering image contrast from the blurring background. The proposed PDE are performing an anisotropic diffusion in the orthogonal direction, thus preserving the edges during further denoising process. Simulation indicates that the combined algorithm can more effectively recover the blurred image from speckle and Gauss noise background than the only wavelet denoising method, while the denoising effect is also distinct when the pepper-salt noise has low intensity. The combined algorithm proposed in this article can be integrated in remote sensing image analyzing to obtain higher accuracy for environmental interpretation and pattern recognition.
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Xiaojun Wang and Weidong Lai "Denoising approach for remote sensing image based on anisotropic diffusion and wavelet transform algorithm", Proc. SPIE 8193, International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, 81934O (8 September 2011);

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