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Regional sub-block matrices based multiple regularization and biomedical image reconstruction

机译:基于区域子块矩阵的多重正则化和生物医学图像重建

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A regional information based multiple regularization is studied for solving biological inverse problem. Inverse problems are usually optimized and solved by Newtons method and its variants. Optimization based on calculus is extremely localized. The regional gradient in calculus is more important for local physiological changes. A sub-block based multiple regularization is proposed in Gauss-Newtons method for biological diffuse optical tomograph (DOT). A study of single step regularization (STR) method and proposed subblock based multiple regularization method has been carried out. The reconstructed image analysis shows a significant improvement in the proposed method.
机译:研究了基于区域信息的多重正则化以解决生物逆问题。逆问题通常通过牛顿法及其变体来优化和解决。基于演算的优化非常局限。微积分的区域梯度对于局部生理变化更为重要。在Gauss-Newtons方法中,提出了一种基于子块的多重正则化方法,用于生物弥散光学层析成像(DOT)。进行了单步正则化(STR)方法和基于子块的多重正则化方法的研究。重建的图像分析显示了所提出方法的显着改进。

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