基于傅里叶和小波联合域的闪烁图像恢复

N. Gribaa, N. Khlifa, K. Hamrouni
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引用次数: 4

摘要

本文主要研究了科学图像的恢复问题。这些图像经常受到检测设备质量差的干扰。目的是改善感兴趣区域的感知,帮助提取有用的信息,从而更好地理解病理现象。修复是一个不适定式的问题。因此,在加性噪声存在的情况下,反演失真模型在数值上往往是不稳定的。然后,我们提出了一个基于傅里叶和小波域的新框架,以利用它们各自的优点。傅里叶正则化反卷积利用了彩色噪声的傅里叶表示效率。而小波包去噪则利用了小波的表示效率和对固有噪声的良好定位。我们注意到该方法在图像边缘保持、对比度和均匀性方面的性能。
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Scintigraphic images restoration using jointly Fourier and Wavelet domains
The paper focus on the problem of scintigraphic images restoration. These images are often disturbed by the bad detection equipment quality. The aim is to improve regions of interest perception, to help useful information extraction and so to allow good understanding of the pathological phenomenon. The restoration is an ill-posed problem. So, inverting the the distortion model in presence of additive noise is often numerically unstable. We propose, then, a new framework based on the Fourier and the Wavelet domain, in order to benefit from the advantages of each one. The Fourier regularized deconvolution exploits the Fourier representation efficiency of the colored noise. Whereas the wavelet packets denoising exploits the wavelet representation efficiency and the good localization of inherent noise in this domain. We noticed the performance of the proposed method in terms of edges preservation, contrast and uniformity in the images.
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