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Iterative Regularization Denoising Method Based on OSV Model for BioMedical Image Denoising

机译:基于奥夫莫医学图像去噪的OSV模型的迭代正规去噪方法

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Biomedical image denoising algorithm based on gradient dependent energy functional often compromised the biomedicai image features like textures or certain details. This paper proposes an iterative regularization denoising method based on OSV model for biomedical image denoising. By using iterative regularization, the oscillating patterns of texture and detail are added back to fit and compute the original OSV model, and the iterative behavior avoids overfull smoothing while denoising the features of textures and details to a certain extent. In addition, the iterative procedure is proposed in this paper, and the proposed algorithm also be proved the convergence property. Experimental results show that the proposed method can achieve a batter result in preserving not only the features of textures for biomedical image denoising but also the details for biomedical image.
机译:基于梯度相关能量功能的生物医学图像去噪算法经常受到纹理或某些细节等生物专业的图像特征。本文提出了一种基于osv模型的生物医学图像去噪的迭代正则化去噪方法。通过使用迭代正则化,纹理和细节的振荡模式被添加回来适合并计算原始OSV模型,并且迭代行为避免过度平滑,同时在一定程度上将纹理和细节的特征取消。此外,本文提出了迭代程序,也可以证明该算法也被证明了该算法。实验结果表明,该方法可以达到击球手导致不仅保留了生物医学图像去噪的纹理的特征,而且是生物医学图像的细节。

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