Bernoulli-Gaussian deconvolution in non-Gaussian noise from multiscale edges

H. Rousseau, P. Duvaut
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引用次数: 1

Abstract

This paper deals with the problem of deconvolution of Bernoulli-Gaussian processes immerged in a non-Gaussian noise. We apply a wavelet decomposition to the process to gaussianise the noise and at each scale a classical detection-estimation algorithm is performed on the signal. Finally, we use a fusion strategy to merge all results and obtain the final deconvolved result. When the noise variance is available, its value can be used in the algorithm, performance is improved only for strongly non-Gaussian noise like Poisson noise. When the noise variance cannot be estimated, we show by simulation an improvement by our method.
多尺度边缘非高斯噪声的伯努利-高斯反卷积
研究了非高斯噪声中伯努利-高斯过程的反卷积问题。我们应用小波分解对噪声进行高斯化处理,并在每个尺度上对信号执行经典的检测估计算法。最后,我们使用融合策略对所有结果进行合并,得到最终的反卷积结果。当噪声方差可用时,其值可用于算法,仅对泊松噪声等强非高斯噪声性能有所提高。在无法估计噪声方差的情况下,通过仿真证明了该方法的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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