2 February 2006 Remote sensing image denoising by using discrete multiwavelet transform techniques
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Proceedings Volume 6031, ICO20: Remote Sensing and Infrared Devices and Systems; 60310S (2006) https://doi.org/10.1117/12.667959
Event: ICO20:Optical Devices and Instruments, 2005, Changchun, China
Abstract
We present a new method by using GHM discrete multiwavelet transform in image denoising on this paper. The developments in wavelet theory have given rise to the wavelet thresholding method, for extracting a signal from noisy data. The method of signal denoising via wavelet thresholding was popularized. Multiwavelets have recently been introduced and they offer simultaneous orthogonality, symmetry and short support. This property makes multiwavelets more suitable for various image processing applications, especially denoising. It is based on thresholding of multiwavelet coefficients arising from the standard scalar orthogonal wavelet transform. It takes into account the covariance structure of the transform. Denoising of images via thresholding of the multiwavelet coefficients result from preprocessing and the discrete multiwavelet transform can be carried out by treating the output in this paper. The form of the threshold is carefully formulated and is the key to the excellent results obtained in the extensive numerical simulations of image denoising. We apply the multiwavelet-based to remote sensing image denoising. Multiwavelet transform technique is rather a new method, and it has a big advantage over the other techniques that it less distorts spectral characteristics of the image denoising. The experimental results show that multiwavelet based image denoising schemes outperform wavelet based method both subjectively and objectively.
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Haihui Wang, Haihui Wang, Jun Wang, Jun Wang, Jian Zhang, Jian Zhang, } "Remote sensing image denoising by using discrete multiwavelet transform techniques", Proc. SPIE 6031, ICO20: Remote Sensing and Infrared Devices and Systems, 60310S (2 February 2006); doi: 10.1117/12.667959; https://doi.org/10.1117/12.667959
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